WorldWideScience

Sample records for optimal transmission scheduling

  1. Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission

    Directory of Open Access Journals (Sweden)

    Mingjie Zhao

    2018-02-01

    Full Text Available The conventional battery electric buses (BEBs have limited potential to optimize the energy consumption and reach a better dynamic performance. A practical dual-motor equipped with 4-speed Automated Manual Transmission (AMT propulsion system is proposed, which can eliminate the traction interruption in conventional AMT. A discrete model of the dual-motor-AMT electric bus (DMAEB is built and used to optimize the gear shift schedule. Dynamic programming (DP algorithm is applied to find the optimal results where the efficiency and shift time of each gear are considered to handle the application problem of global optimization. A rational penalty factor and a proper shift time delay based on bench test results are set to reduce the shift frequency by 82.5% in Chinese-World Transient Vehicle Cycle (C-WTVC. Two perspectives of applicable shift rule extraction methods, i.e., the classification method based on optimal operating points and clustering method based on optimal shifting points, are explored and compared. Eventually, the hardware-in-the-loop (HIL simulation results demonstrate that the proposed structure and extracted shift schedule can realize a significant improvement in reducing energy loss by 20.13% compared to traditional empirical strategies.

  2. Optimizing Transmission and Shutdown for Energy-Efficient Real-time Packet Scheduling in Clustered Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Rajkumar Ragunathan

    2005-01-01

    Full Text Available Energy efficiency is imperative to enable the deployment of ad hoc networks. Conventional power management focuses independently on the physical or MAC layer and approaches differ depending on the abstraction level. At the physical layer, the fundamental tradeoff between transmission rate and energy is exploited, which leads to transmit as slow as possible. At MAC level, power reduction techniques aim to transmit as fast as possible to maximize the radios power-off interval. The two approaches seem conflicting and it is not obvious which one is the most appropriate. We propose a transmission strategy that optimally mixes both techniques in a multiuser context. We present a cross-layer solution considering the transceiver power characteristics, the varying system load, and the dynamic channel constraints. Based on this, we derive a low-complexity online scheduling algorithm. Results considering an -ary quadrature amplitude modulation radio show that for a range of scenarios a large power reduction is achieved, compared to the case where only scaling or shutdown is considered.

  3. Maritime wideband communication networks video transmission scheduling

    CERN Document Server

    Yang, Tingting

    2014-01-01

    This Springer Brief covers emerging maritime wideband communication networks and how they facilitate applications such as maritime distress, urgency, safety and general communications. It provides valuable insight on the data transmission scheduling and protocol design for the maritime wideband network. This brief begins with an introduction to maritime wideband communication networks including the architecture, framework, operations and a comprehensive survey on current developments. The second part of the brief presents the resource allocation and scheduling for video packet transmission wit

  4. Optimizing Unmanned Aircraft System Scheduling

    Science.gov (United States)

    2008-06-01

    ASC-U uses a deterministic algorithm to optimize over a given finite time horizon to obtain near-optimal UAS mission area assignments. ASC-U...the details of the algorithm . We set an upper bound on the total number of schedules that can be generated, so as not to create unsolvable ILPs. We...COL_MISSION_NAME)) If Trim( CStr (rMissions(iRow, COL_MISSION_REQUIRED))) <> "" Then If CLng(rMissions(iRow, COL_MISSION_REQUIRED)) > CLng

  5. Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks

    Science.gov (United States)

    Dudukovich, Rachel; Raible, Daniel E.

    2016-01-01

    The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

  6. Sensor Transmission Power Schedule for Smart Grids

    Science.gov (United States)

    Gao, C.; Huang, Y. H.; Li, J.; Liu, X. D.

    2017-11-01

    Smart grid has attracted much attention by the requirement of new generation renewable energy. Nowadays, the real-time state estimation, with the help of phasor measurement unit, plays an important role to keep smart grid stable and efficient. However, the limitation of the communication channel is not considered by related work. Considering the familiar limited on-board batteries wireless sensor in smart grid, transmission power schedule is designed in this paper, which minimizes energy consumption with proper EKF filtering performance requirement constrain. Based on the event-triggered estimation theory, the filtering algorithm is also provided to utilize the information contained in the power schedule. Finally, its feasibility and performance is demonstrated using the standard IEEE 39-bus system with phasor measurement units (PMUs).

  7. Schedule optimization study implementation plan

    International Nuclear Information System (INIS)

    1993-11-01

    This Implementation Plan is intended to provide a basis for improvements in the conduct of the Environmental Restoration (ER) Program at Hanford. The Plan is based on the findings of the Schedule Optimization Study (SOS) team which was convened for two weeks in September 1992 at the request of the U.S. Department of Energy (DOE) Richland Operations Office (RL). The need for the study arose out of a schedule dispute regarding the submission of the 1100-EM-1 Operable Unit (OU) Remedial Investigation/Feasibility Study (RI/FS) Work Plan. The SOS team was comprised of independent professionals from other federal agencies and the private sector experienced in environmental restoration within the federal system. The objective of the team was to examine reasons for the lengthy RI/FS process and recommend ways to expedite it. The SOS team issued their Final Report in December 1992. The report found the most serious impediments to cleanup relate to a series of management and policy issues which are within the control of the three parties managing and monitoring Hanford -- the DOE, U.S. Environmental Protection Agency (EPA), and the State of Washington Department of Ecology (Ecology). The SOS Report identified the following eight cross-cutting issues as the root of major impediments to the Hanford Site cleanup. Each of these eight issues is quoted from the SOS Report followed by a brief, general description of the proposed approach being developed

  8. Cloud Service Scheduling Algorithm Research and Optimization

    Directory of Open Access Journals (Sweden)

    Hongyan Cui

    2017-01-01

    Full Text Available We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS. In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO and a Genetic Algorithm (GA to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO are optimal.

  9. Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Traian; Pop, Paul; Eles, Petru

    2005-01-01

    We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource......, they are organized in a hierarchy. In this paper, we address design problems that are characteristic to such hierarchically scheduled systems: assignment of scheduling policies to tasks, mapping of tasks to hardware components, and the scheduling of the activities. We present algorithms for solving these problems....... Our heuristics are able to find schedulable implementations under limited resources, achieving an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  10. Optimal scheduling of coproduction with a storage

    International Nuclear Information System (INIS)

    Ravn, H.F.; Rygard, J.M.

    1993-02-01

    We consider the problem of optimal scheduling of a system with combined heat and heat (CHP) units and a heat storege. The purpose of the heat storage is to permit a partial decoupling of the variations in the demand for heat and electrical power. We formulate the problem of optimal scheduling as that of minimizing the total costs over the planning period. The heat demand from the district heating system and the ''shadow prices'' for the electrical power system are taken as externally given parameters. The resulting model is solved by dynamic programming. We describe implementation details and we give examples of result of the optimization. (au) (12 refs.)

  11. Transmission tariffs based on optimal power flow

    International Nuclear Information System (INIS)

    Wangensteen, Ivar; Gjelsvik, Anders

    1998-01-01

    This report discusses transmission pricing as a means of obtaining optimal scheduling and dispatch in a power system. This optimality includes consumption as well as generation. The report concentrates on how prices can be used as signals towards operational decisions of market participants (generators, consumers). The main focus is on deregulated systems with open access to the network. The optimal power flow theory, with demand side modelling included, is briefly reviewed. It turns out that the marginal costs obtained from the optimal power flow gives the optimal transmission tariff for the particular load flow in case. There is also a correspondence between losses and optimal prices. Emphasis is on simple examples that demonstrate the connection between optimal power flow results and tariffs. Various cases, such as open access and single owner are discussed. A key result is that the location of the ''marketplace'' in the open access case does not influence the net economical result for any of the parties involved (generators, network owner, consumer). The optimal power flow is instantaneous, and in its standard form cannot deal with energy constrained systems that are coupled in time, such as hydropower systems with reservoirs. A simplified example of how the theory can be extended to such a system is discussed. An example of the influence of security constraints on prices is also given. 4 refs., 24 figs., 7 tabs

  12. An introduction to optimal satellite range scheduling

    CERN Document Server

    Vázquez Álvarez, Antonio José

    2015-01-01

    The satellite range scheduling (SRS) problem, an important operations research problem in the aerospace industry consisting of allocating tasks among satellites and Earth-bound objects, is examined in this book. SRS principles and solutions are applicable to many areas, including: Satellite communications, where tasks are communication intervals between sets of satellites and ground stations Earth observation, where tasks are observations of spots on the Earth by satellites Sensor scheduling, where tasks are observations of satellites by sensors on the Earth. This self-contained monograph begins with a structured compendium of the problem and moves on to explain the optimal approach to the solution, which includes aspects from graph theory, set theory, game theory and belief networks. This book is accessible to students, professionals and researchers in a variety of fields, including: operations research, optimization, scheduling theory, dynamic programming and game theory. Taking account of the distributed, ...

  13. Anesthesiology Nurse Scheduling using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Leopoldo Altamirano

    2012-02-01

    Full Text Available In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nursesarsquo; stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.

  14. Optimal Scheduling of Domestic Appliances via MILP

    Directory of Open Access Journals (Sweden)

    Zdenek Bradac

    2014-12-01

    Full Text Available This paper analyzes a consumption scheduling mechanism for domestic appliances within a home area network. The aim of the proposed scheduling is to minimize the total energy price paid by the consumer and to reduce power peaks in order to achieve a balanced daily load schedule. An exact and computationally efficient mixed-integer linear programming (MILP formulation of the problem is presented. This model is verified by several problem instances. Realistic scenarios based on the real price tariffs commercially available in the Czech Republic are calculated. The results obtained by solving the optimization problem are compared with a simulation of the ripple control service currently used by many domestic consumers in the Czech Republic.

  15. Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission.

    Science.gov (United States)

    Qin, Danyang; Yang, Songxiang; Zhang, Yan; Ma, Jingya; Ding, Qun

    2017-06-09

    Wireless energy collecting technology can effectively reduce the network time overhead and prolong the wireless sensor network (WSN) lifetime. However, the traditional energy collecting technology cannot achieve the balance between ergodic channel capacity and average collected energy. In order to solve the problem of the network transmission efficiency and the limited energy of wireless devices, three improved scheduling mechanisms are proposed: improved signal noise ratio (SNR) scheduling mechanism (IS2M), improved N-SNR scheduling mechanism (INS2M) and an improved Equal Throughput scheduling mechanism (IETSM) for different channel conditions to improve the whole network performance. Meanwhile, the average collected energy of single users and the ergodic channel capacity of three scheduling mechanisms can be obtained through the order statistical theory in Rayleig, Ricean, Nakagami- m and Weibull fading channels. It is concluded that the proposed scheduling mechanisms can achieve better balance between energy collection and data transmission, so as to provide a new solution to realize synchronous information and energy transmission for WSNs.

  16. Optimal scheduling using priced timed automata

    DEFF Research Database (Denmark)

    Behrmann, Gerd; Larsen, Kim Guldstrand; Rasmussen, Jacob Illum

    2005-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European...... projects VHS [20] and AMETIST [16] and are available in the recently released UPPAAL CORA [12], a variant of the real-time verification tool UPPAAL [18, 5] specialized for cost-optimal reachability for the extended model of so-called priced timed automata....

  17. Project Scheduling Based on Risk of Gas Transmission Pipe

    Science.gov (United States)

    Silvianita; Nurbaity, A.; Mulyadi, Y.; Suntoyo; Chamelia, D. M.

    2018-03-01

    The planning of a project has a time limit on which must be completed before or right at a predetermined time. Thus, in a project planning, it is necessary to have scheduling management that is useful for completing a project to achieve maximum results by considering the constraints that will exists. Scheduling management is undertaken to deal with uncertainties and negative impacts of time and cost in project completion. This paper explains about scheduling management in gas transmission pipeline project Gresik-Semarang to find out which scheduling plan is most effectively used in accordance with its risk value. Scheduling management in this paper is assissted by Microsoft Project software to find the critical path of existing project scheduling planning data. Critical path is the longest scheduling path with the fastest completion time. The result is found a critical path on project scheduling with completion time is 152 days. Furthermore, the calculation of risk is done by using House of Risk (HOR) method and it is found that the critical path has a share of 40.98 percent of all causes of the occurence of risk events that will be experienced.

  18. Optimized Treatment Schedules for Chronic Myeloid Leukemia.

    Directory of Open Access Journals (Sweden)

    Qie He

    2016-10-01

    Full Text Available Over the past decade, several targeted therapies (e.g. imatinib, dasatinib, nilotinib have been developed to treat Chronic Myeloid Leukemia (CML. Despite an initial response to therapy, drug resistance remains a problem for some CML patients. Recent studies have shown that resistance mutations that preexist treatment can be detected in a substantial number of patients, and that this may be associated with eventual treatment failure. One proposed method to extend treatment efficacy is to use a combination of multiple targeted therapies. However, the design of such combination therapies (timing, sequence, etc. remains an open challenge. In this work we mathematically model the dynamics of CML response to combination therapy and analyze the impact of combination treatment schedules on treatment efficacy in patients with preexisting resistance. We then propose an optimization problem to find the best schedule of multiple therapies based on the evolution of CML according to our ordinary differential equation model. This resulting optimization problem is nontrivial due to the presence of ordinary different equation constraints and integer variables. Our model also incorporates drug toxicity constraints by tracking the dynamics of patient neutrophil counts in response to therapy. We determine optimal combination strategies that maximize time until treatment failure on hypothetical patients, using parameters estimated from clinical data in the literature.

  19. Algorithm comparison for schedule optimization in MR fingerprinting.

    Science.gov (United States)

    Cohen, Ouri; Rosen, Matthew S

    2017-09-01

    In MR Fingerprinting, the flip angles and repetition times are chosen according to a pseudorandom schedule. In previous work, we have shown that maximizing the discrimination between different tissue types by optimizing the acquisition schedule allows reductions in the number of measurements required. The ideal optimization algorithm for this application remains unknown, however. In this work we examine several different optimization algorithms to determine the one best suited for optimizing MR Fingerprinting acquisition schedules. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Group Elevator Peak Scheduling Based on Robust Optimization Model

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  1. Towards an optimal transmission system

    International Nuclear Information System (INIS)

    Calviou, M.

    2005-01-01

    This presentation provided background on National Grid USA and discussed transmission investment in the United States (US) and United Kingdom. It also discussed barriers to transmission investments and improvements, thoughts on solutions and a long-term vision. The presentation identified that transmission investment should follow from clear reliability rules designed to promote better operation and management; investment does not necessarily mean new rights-of-way; and transmission investment should target benefits to customers. It was stated that US transmission investment levels have decreased. A comparison between US and UK transmission investment was presented along with a chart of increasing US congestion costs. Barriers to investment in US transmission include vertical integration; misperception of transmission as a market product; federal and state jurisdiction issues; fragmentation in transmission ownership and operation; rate cap based plans that impact transmission; lack of clarity in cost allocation; and the site selection process. Possible solutions include policy and incentives, promoting independence and resolving structural issues. tabs., figs

  2. Power Transmission Scheduling for Generators in a Deregulated Environment Based on a Game-Theoretic Approach

    Directory of Open Access Journals (Sweden)

    Bingtuan Gao

    2015-12-01

    Full Text Available In a deregulated environment of the power market, in order to lower their energy price and guarantee the stability of the power network, appropriate transmission lines have to be considered for electricity generators to sell their energy to the end users. This paper proposes a game-theoretic power transmission scheduling for multiple generators to lower their wheeling cost. Based on the embedded cost method, a wheeling cost model consisting of congestion cost, cost of losses and cost of transmission capacity is presented. By assuming each generator behaves in a selfish and rational way, the competition among the multiple generators is formulated as a non-cooperative game, where the players are the generators and the strategies are their daily schedules of power transmission. We will prove that there exists at least one pure-strategy Nash equilibrium of the formulated power transmission game. Moreover, a distributed algorithm will be provided to realize the optimization in terms of minimizing the wheeling cost. Finally, simulations were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game approach for the generators in a deregulated environment.

  3. A Gas Scheduling Optimization Model for Steel Enterprises

    Directory of Open Access Journals (Sweden)

    Niu Honghai

    2017-01-01

    Full Text Available Regarding the scheduling problems of steel enterprises, this research designs the gas scheduling optimization model according to the rules and priorities. Considering different features and the process changes of the gas unit in the process of actual production, the calculation model of process state and gas consumption soft measurement together with the rules of scheduling optimization is proposed to provide the dispatchers with real-time gas using status of each process, then help them to timely schedule and reduce the gas volume fluctuations. In the meantime, operation forewarning and alarm functions are provided to avoid the abnormal situation in the scheduling, which has brought about very good application effect in the actual scheduling and ensures the safety of the gas pipe network system and the production stability.

  4. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

    Energy Technology Data Exchange (ETDEWEB)

    Cardoso, Goncalo [Technical Univ. of Lisbon (Portugal); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Center for Energy and Innovation Technologies (Austria); Bozchalui, Mohammed C. [NEC Laboratories American Inc., Irving, TX (United States); Sharma, Ratnesh [NEC Laboratories American Inc., Irving, TX (United States); Marnay, Chris [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Barbosa-Povoa, Ana [Technical Univ. of Lisbon (Portugal); Ferrao, Paulo [Technical Univ. of Lisbon (Portugal)

    2013-12-06

    The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

  5. Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm

    OpenAIRE

    T. Vigneswari; M. A. Maluk Mohamed

    2015-01-01

    Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Hete...

  6. Robust Optimization for Household Load Scheduling with Uncertain Parameters

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2018-04-01

    Full Text Available Home energy management systems (HEMS face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers’ comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

  7. Developing optimal nurses work schedule using integer programming

    Science.gov (United States)

    Shahidin, Ainon Mardhiyah; Said, Mohd Syazwan Md; Said, Noor Hizwan Mohamad; Sazali, Noor Izatie Amaliena

    2017-08-01

    Time management is the art of arranging, organizing and scheduling one's time for the purpose of generating more effective work and productivity. Scheduling is the process of deciding how to commit resources between varieties of possible tasks. Thus, it is crucial for every organization to have a good work schedule for their staffs. The job of Ward nurses at hospitals runs for 24 hours every day. Therefore, nurses will be working using shift scheduling. This study is aimed to solve the nurse scheduling problem at an emergency ward of a private hospital. A 7-day work schedule for 7 consecutive weeks satisfying all the constraints set by the hospital will be developed using Integer Programming. The work schedule for the nurses obtained gives an optimal solution where all the constraints are being satisfied successfully.

  8. customer-teller scheduling system for optimizing banks service

    African Journals Online (AJOL)

    els Bank Teller scheduling system for optimizing a Banks customer service. The model takes into account real time .... tinuous observation by management personnel and thus results in .... relationships in a mathematical model. A mathematical ...

  9. SIMULTANEOUS SCHEDULING AND OPERATIONAL OPTIMIZATION OF MULTIPRODUCT, CYCLIC CONTINUOUS PLANTS

    Directory of Open Access Journals (Sweden)

    A. Alle

    2002-03-01

    Full Text Available The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994. Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb method to optimally schedule the operation is less effective than the proposed approach.

  10. SIMULTANEOUS SCHEDULING AND OPERATIONAL OPTIMIZATION OF MULTIPRODUCT, CYCLIC CONTINUOUS PLANTS

    Directory of Open Access Journals (Sweden)

    Alle A.

    2002-01-01

    Full Text Available The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994. Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb method to optimally schedule the operation is less effective than the proposed approach.

  11. Refrigerator Optimal Scheduling to Minimise the Cost of Operation

    Directory of Open Access Journals (Sweden)

    Bálint Roland

    2016-12-01

    Full Text Available The cost optimal scheduling of a household refrigerator is presented in this work. The fundamental approach is the model predictive control methodology applied to the piecewise affine model of the refrigerator.

  12. Locomotive Schedule Optimization for Da-qin Heavy Haul Railway

    Directory of Open Access Journals (Sweden)

    Ruiye Su

    2015-01-01

    Full Text Available The main difference between locomotive schedule of heavy haul railways and that of regular rail transportation is the number of locomotives utilized for one train. One heavy-loaded train usually has more than one locomotive, but a regular train only has one. This paper develops an optimization model for the multilocomotive scheduling problem (MLSP through analyzing the current locomotive schedule of Da-qin Railway. The objective function of our paper is to minimize the total number of utilized locomotives. The MLSP is nondeterministic polynomial (NP hard. Therefore, we convert the multilocomotive traction problem into a single-locomotive traction problem. Then, the single-locomotive traction problem (SLTP can be converted into an assignment problem. The Hungarian algorithm is applied to solve the model and obtain the optimal locomotive schedule. We use the variance of detention time of locomotives at stations to evaluate the stability of locomotive schedule. In order to evaluate the effectiveness of the proposed optimization model, case studies for 20 kt and 30 kt heavy-loaded combined trains on Da-qin Railway are both conducted. Compared to the current schedules, the optimal schedules from the proposed models can save 62 and 47 locomotives for 20 kt and 30 kt heavy-loaded combined trains, respectively. Therefore, the effectiveness of the proposed model and its solution algorithm are both valid.

  13. Optimal Time-Abstract Schedulers for CTMDPs and Markov Games

    Directory of Open Access Journals (Sweden)

    Markus Rabe

    2010-06-01

    Full Text Available We study time-bounded reachability in continuous-time Markov decision processes for time-abstract scheduler classes. Such reachability problems play a paramount role in dependability analysis and the modelling of manufacturing and queueing systems. Consequently, their analysis has been studied intensively, and techniques for the approximation of optimal control are well understood. From a mathematical point of view, however, the question of approximation is secondary compared to the fundamental question whether or not optimal control exists. We demonstrate the existence of optimal schedulers for the time-abstract scheduler classes for all CTMDPs. Our proof is constructive: We show how to compute optimal time-abstract strategies with finite memory. It turns out that these optimal schedulers have an amazingly simple structure---they converge to an easy-to-compute memoryless scheduling policy after a finite number of steps. Finally, we show that our argument can easily be lifted to Markov games: We show that both players have a likewise simple optimal strategy in these more general structures.

  14. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules

    International Nuclear Information System (INIS)

    Cardoso, G.; Stadler, M.; Bozchalui, M.C.; Sharma, R.; Marnay, C.; Barbosa-Póvoa, A.; Ferrão, P.

    2014-01-01

    The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem. - Highlights: • This paper introduces a new EV aggregator model in the DER-CAM model and expands it with a stochastic formulation. • The model is used to analyze the impact of EVs in DER investment decisions in a large office building. • The uncertainty in EV driving patterns is considered through scenarios based on data from a daily commute driving survey. • Results indicate that EVs have a significant impact in optimal DER decisions, particularly when looking at short payback periods. • Furthermore, results indicate that uncertainty in EV driving schedules has little impact on DER investment decisions

  15. Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments.

    Science.gov (United States)

    Phunchongharn, Phond; Hossain, Ekram; Camorlinga, Sergio

    2011-11-01

    We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

  16. Enhanced OTSG economics optimizing CAPEX + OPEX + Schedule

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, Peter [KTI Engineered to Perform (Canada)

    2011-07-01

    This presentation gives an insight into the economics involved in producing once-through steam (OTSG) generators in Canada and it offers a proposal for improving returns in the industry. The objective of this presentation was to show the importance of developing OTSG manufacturing based on the future of this technology. The growing demand for OTSG by oil companies was presented, suggesting that this is a good time to put effort into developing more efficient designs. After the background of this technology was discussed, several economical aspects of the process were addressed; for instance, reducing capital expenditure (CAPEX) and operation expenditure (OPEX), outsourcing production, and project scheduling and execution. Initiatives proposed included: reducing plot size, producing larger OTSGs, reducing downtime, using alternative fuels, forming a global manufacturing network, and avoiding order blockage. Finally, the idea of designing a portable OTSG was proposed, showing its flexibility in oil extraction projects.

  17. Optimal topologies for maximizing network transmission capacity

    Science.gov (United States)

    Chen, Zhenhao; Wu, Jiajing; Rong, Zhihai; Tse, Chi K.

    2018-04-01

    It has been widely demonstrated that the structure of a network is a major factor that affects its traffic dynamics. In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery. We propose an approach for designing optimal network topologies against traffic congestion by link rewiring and apply them on the Barabási-Albert scale-free, static scale-free and Internet Autonomous System-level networks. Furthermore, we analyze the optimized networks using complex network parameters that characterize the structure of networks, and our simulation results suggest that an optimal network for traffic transmission is more likely to have a core-periphery structure. However, assortative mixing and the rich-club phenomenon may have negative impacts on network performance. Based on the observations of the optimized networks, we propose an efficient method to improve the transmission capacity of large-scale networks.

  18. Optimal mechanisms for single machine scheduling

    NARCIS (Netherlands)

    Heydenreich, B.; Mishra, D.; Müller, R.; Uetz, Marc Jochen; Papadimitriou, C.; Zhang, S.

    2008-01-01

    We study the design of optimal mechanisms in a setting here job-agents compete for being processed by a service provider that can handle one job at a time. Each job has a processing time and incurs a waiting cost. Jobs need to be compensated for waiting. We consider two models, one where only the

  19. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  20. Considering FACTS in Optimal Transmission Expansion Planning

    Directory of Open Access Journals (Sweden)

    K. Soleimani

    2017-10-01

    Full Text Available The expansion of power transmission systems is an important part of the expansion of power systems that requires enormous investment costs. Since the construction of new transmission lines is very expensive, it is necessary to choose the most efficient expansion plan that ensures system security with a minimal number of new lines. In this paper, the role of Flexible AC Transmission System (FACTS devices in the effective operation and expansion planning of transmission systems is examined. Effort was taken to implement a method based on sensitivity analysis to select the optimal number and location of FACTS devices, lines and other elements of the transmission system. Using this method, the transmission expansion plan for a 9 and a 39 bus power system was performed with and without the presence of FACTS with the use of DPL environment in Digsilent software 15.1. Results show that the use of these devices reduces the need for new transmission lines and minimizes the investment cost.

  1. Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

    Directory of Open Access Journals (Sweden)

    Imam Ahmad Ashari

    2016-11-01

    Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

  2. Optimizing transmission from distant wind farms

    International Nuclear Information System (INIS)

    Pattanariyankool, Sompop; Lave, Lester B.

    2010-01-01

    We explore the optimal size of the transmission line from distant wind farms, modeling the tradeoff between transmission cost and benefit from delivered wind power. We also examine the benefit of connecting a second wind farm, requiring additional transmission, in order to increase output smoothness. Since a wind farm has a low capacity factor, the transmission line would not be heavily loaded, on average; depending on the time profile of generation, for wind farms with capacity factor of 29-34%, profit is maximized for a line that is about 3/4 of the nameplate capacity of the wind farm. Although wind generation is inexpensive at a good site, transmitting wind power over 1600 km (about the distance from Wyoming to Los Angeles) doubles the delivered cost of power. As the price for power rises, the optimal capacity of transmission increases. Connecting wind farms lowers delivered cost when the wind farms are close, despite the high correlation of output over time. Imposing a penalty for failing to deliver minimum contracted supply leads to connecting more distant wind farms.

  3. Optimization models for flight test scheduling

    Science.gov (United States)

    Holian, Derreck

    As threats around the world increase with nations developing new generations of warfare technology, the Unites States is keen on maintaining its position on top of the defense technology curve. This in return indicates that the U.S. military/government must research, develop, procure, and sustain new systems in the defense sector to safeguard this position. Currently, the Lockheed Martin F-35 Joint Strike Fighter (JSF) Lightning II is being developed, tested, and deployed to the U.S. military at Low Rate Initial Production (LRIP). The simultaneous act of testing and deployment is due to the contracted procurement process intended to provide a rapid Initial Operating Capability (IOC) release of the 5th Generation fighter. For this reason, many factors go into the determination of what is to be tested, in what order, and at which time due to the military requirements. A certain system or envelope of the aircraft must be assessed prior to releasing that capability into service. The objective of this praxis is to aide in the determination of what testing can be achieved on an aircraft at a point in time. Furthermore, it will define the optimum allocation of test points to aircraft and determine a prioritization of restrictions to be mitigated so that the test program can be best supported. The system described in this praxis has been deployed across the F-35 test program and testing sites. It has discovered hundreds of available test points for an aircraft to fly when it was thought none existed thus preventing an aircraft from being grounded. Additionally, it has saved hundreds of labor hours and greatly reduced the occurrence of test point reflight. Due to the proprietary nature of the JSF program, details regarding the actual test points, test plans, and all other program specific information have not been presented. Generic, representative data is used for example and proof-of-concept purposes. Apart from the data correlation algorithms, the optimization associated

  4. Optimal load scheduling in commercial and residential microgrids

    Science.gov (United States)

    Ganji Tanha, Mohammad Mahdi

    Residential and commercial electricity customers use more than two third of the total energy consumed in the United States, representing a significant resource of demand response. Price-based demand response, which is in response to changes in electricity prices, represents the adjustments in load through optimal load scheduling (OLS). In this study, an efficient model for OLS is developed for residential and commercial microgrids which include aggregated loads in single-units and communal loads. Single unit loads which include fixed, adjustable and shiftable loads are controllable by the unit occupants. Communal loads which include pool pumps, elevators and central heating/cooling systems are shared among the units. In order to optimally schedule residential and commercial loads, a community-based optimal load scheduling (CBOLS) is proposed in this thesis. The CBOLS schedule considers hourly market prices, occupants' comfort level, and microgrid operation constraints. The CBOLS' objective in residential and commercial microgrids is the constrained minimization of the total cost of supplying the aggregator load, defined as the microgrid load minus the microgrid generation. This problem is represented by a large-scale mixed-integer optimization for supplying single-unit and communal loads. The Lagrangian relaxation methodology is used to relax the linking communal load constraint and decompose the independent single-unit functions into subproblems which can be solved in parallel. The optimal solution is acceptable if the aggregator load limit and the duality gap are within the bounds. If any of the proposed criteria is not satisfied, the Lagrangian multiplier will be updated and a new optimal load schedule will be regenerated until both constraints are satisfied. The proposed method is applied to several case studies and the results are presented for the Galvin Center load on the 16th floor of the IIT Tower in Chicago.

  5. Joint Throughput Maximization and Fair Uplink Transmission Scheduling in CDMA Systems

    Directory of Open Access Journals (Sweden)

    Symeon Papavassiliou

    2009-01-01

    Full Text Available We study the fundamental problem of optimal transmission scheduling in a code-division multiple-access wireless system in order to maximize the uplink system throughput, while satisfying the users quality-of-service (QoS requirements and maintaining fairness among them. The corresponding problem is expressed as a weighted throughput maximization problem, under certain power and QoS constraints, where the weights are the control parameters reflecting the fairness constraints. With the introduction of the power index capacity, it is shown that this optimization problem can be converted into a binary knapsack problem, where all the corresponding constraints are replaced by the power index capacities at some certain system power index. A two-step approach is followed to obtain the optimal solution. First, a simple method is proposed to find the optimal set of users to receive service for a given fixed target system load, and then the optimal solution is obtained as a global search within a certain range. Furthermore, a stochastic approximation method is presented to effectively identify the required control parameters. The performance evaluation reveals the advantages of our proposed policy over other existing ones and confirms that it achieves very high throughput while maintains fairness among the users, under different channel conditions and requirements.

  6. Optimal charging schedule of an electric vehicle fleet

    DEFF Research Database (Denmark)

    Hu, Junjie; You, Shi; Østergaard, Jacob

    2011-01-01

    In this paper, we propose an approach to optimize the charging schedule of an Electric Vehicle (EV) fleet both taking into account spot price and individual EV driving requirement with the goal of minimizing charging costs. A flexible and suitable mathematic model is introduced to characterize...

  7. Optimization of the solution of the problem of scheduling theory ...

    African Journals Online (AJOL)

    This article describes the genetic algorithm used to solve the problem related to the scheduling theory. A large number of different methods is described in the scientific literature. The main issue that faced the problem in question is that it is necessary to search the optimal solution in a large search space for the set of ...

  8. Verification and Optimization of a PLC Control Schedule

    NARCIS (Netherlands)

    Brinksma, Hendrik; Mader, Angelika H.; Havelund, K.; Penix, J.; Visser, W.

    We report on the use of the SPIN model checker for both the verification of a process control program and the derivation of optimal control schedules. This work was carried out as part of a case study for the EC VHS project (Verification of Hybrid Systems), in which the program for a Programmable

  9. On using priced timed automata to achieve optimal scheduling

    DEFF Research Database (Denmark)

    Rasmussen, Jacob Illum; Larsen, Kim Guldstrand; Subramani, K.

    2006-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European proj...... of so-called priced timed automata....

  10. Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

    Directory of Open Access Journals (Sweden)

    S Sarathambekai

    2017-03-01

    Full Text Available Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

  11. Coordination between Generation and Transmission Maintenance Scheduling by Means of Multi-agent Technique

    Science.gov (United States)

    Nagata, Takeshi; Tao, Yasuhiro; Utatani, Masahiro; Sasaki, Hiroshi; Fujita, Hideki

    This paper proposes a multi-agent approach to maintenance scheduling in restructured power systems. The restructuring of electric power industry has resulted in market-based approaches for unbundling a multitude of service provided by self-interested entities such as power generating companies (GENCOs), transmission providers (TRANSCOs) and distribution companies (DISCOs). The Independent System Operator (ISO) is responsible for the security of the system operation. The schedule submitted to ISO by GENCOs and TRANSCOs should satisfy security and reliability constraints. The proposed method consists of several GENCO Agents (GAGs), TARNSCO Agents (TAGs) and a ISO Agent(IAG). The IAG’s role in maintenance scheduling is limited to ensuring that the submitted schedules do not cause transmission congestion or endanger the system reliability. From the simulation results, it can be seen the proposed multi-agent approach could coordinate between generation and transmission maintenance schedules.

  12. Joint optimization of production scheduling and machine group preventive maintenance

    International Nuclear Information System (INIS)

    Xiao, Lei; Song, Sanling; Chen, Xiaohui; Coit, David W.

    2016-01-01

    Joint optimization models were developed combining group preventive maintenance of a series system and production scheduling. In this paper, we propose a joint optimization model to minimize the total cost including production cost, preventive maintenance cost, minimal repair cost for unexpected failures and tardiness cost. The total cost depends on both the production process and the machine maintenance plan associated with reliability. For the problems addressed in this research, any machine unavailability leads to system downtime. Therefore, it is important to optimize the preventive maintenance of machines because their performance impacts the collective production processing associated with all machines. Too lengthy preventive maintenance intervals may be associated with low scheduled machine maintenance cost, but may incur expensive costs for unplanned failure due to low machine reliability. Alternatively, too frequent preventive maintenance activities may achieve the desired high reliability machines, but unacceptably high scheduled maintenance cost. Additionally, product scheduling plans affect tardiness and maintenance cost. Two results are obtained when solving the problem; the optimal group preventive maintenance interval for machines, and the assignment of each job, including the corresponding start time and completion time. To solve this non-deterministic polynomial-time problem, random keys genetic algorithms are used, and a numerical example is solved to illustrate the proposed model. - Highlights: • Group preventive maintenance (PM) planning and production scheduling are jointed. • Maintenance interval and assignment of jobs are decided by minimizing total cost. • Relationships among system age, PM, job processing time are quantified. • Random keys genetic algorithms (GA) are used to solve the NP-hard problem. • Random keys GA and Particle Swarm Optimization (PSO) are compared.

  13. Transmission loss optimization in acoustic sandwich panels

    Science.gov (United States)

    Makris, S. E.; Dym, C. L.; MacGregor Smith, J.

    1986-06-01

    Considering the sound transmission loss (TL) of a sandwich panel as the single objective, different optimization techniques are examined and a sophisticated computer program is used to find the optimum TL. Also, for one of the possible case studies such as core optimization, closed-form expressions are given between TL and the core-design variables for different sets of skins. The significance of these functional relationships lies in the fact that the panel designer can bypass the necessity of using a sophisticated software package in order to assess explicitly the dependence of the TL on core thickness and density.

  14. Optimal Scheduling of Residential Microgrids Considering Virtual Energy Storage System

    Directory of Open Access Journals (Sweden)

    Weiliang Liu

    2018-04-01

    Full Text Available The increasingly complex residential microgrids (r-microgrid consisting of renewable generation, energy storage systems, and residential buildings require a more intelligent scheduling method. Firstly, aiming at the radiant floor heating/cooling system widely utilized in residential buildings, the mathematical relationship between the operative temperature and heating/cooling demand is established based on the equivalent thermodynamic parameters (ETP model, by which the thermal storage capacity is analyzed. Secondly, the radiant floor heating/cooling system is treated as virtual energy storage system (VESS, and an optimization model based on mixed-integer nonlinear programming (MINLP for r-microgrid scheduling is established which takes thermal comfort level and economy as the optimization objectives. Finally, the optimal scheduling results of two typical r-microgrids are analyzed. Case studies demonstrate that the proposed scheduling method can effectively employ the thermal storage capacity of radiant floor heating/cooling system, thus lowering the operating cost of the r-microgrid effectively while ensuring the thermal comfort level of users.

  15. Optimal scheduling for distribution network with redox flow battery storage

    International Nuclear Information System (INIS)

    Hosseina, Majid; Bathaee, Seyed Mohammad Taghi

    2016-01-01

    Highlights: • A novel method for optimal scheduling of storages in radial network is presented. • Peak shaving and load leveling are the main objectives. • Vanadium redox flow battery is considered as the energy storage unit. • Real data is used for simulation. - Abstract: There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This heuristic method is more suitable for scheduling and operation of distribution networks which require installation of storages. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.

  16. Scheduling with Bus Access Optimization for Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Eles, Petru; Doboli, Alex; Pop, Paul

    2000-01-01

    of control. Our goal is to derive a worst case delay by which the system completes execution, such that this delay is as small as possible; to generate a logically and temporally deterministic schedule; and to optimize parameters of the communication protocol such that this delay is guaranteed. We have......In this paper, we concentrate on aspects related to the synthesis of distributed embedded systems consisting of programmable processors and application-specific hardware components. The approach is based on an abstract graph representation that captures, at process level, both dataflow and the flow......, generates an efficient bus access scheme as well as the schedule tables for activation of processes and communications....

  17. Tramp ship routing and scheduling with integrated bunker optimization

    DEFF Research Database (Denmark)

    Vilhelmsen, Charlotte; Lusby, Richard Martin; Larsen, Jesper

    2014-01-01

    is referred to as bunker and bunker costs constitute a significant part of the daily operating costs. There can be great variations in bunker prices across bunker ports so it is important to carefully plan bunkering for each ship. As ships operate 24 hours a day, they must refuel during operations. Therefore...... and scheduling phase and present a mixed integer programming formulation for the integrated problem of optimally routing, scheduling and bunkering a tramp fleet. Aside from the integration of bunker, this model also extends standard tramp formulations by using load dependent costs, speed and bunker consumption...

  18. An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment

    Directory of Open Access Journals (Sweden)

    Whei-Min Lin

    2018-06-01

    Full Text Available This paper presents the scheduling dispatch of a microgrid (MG, while considering renewable energy, battery storage systems, and time-of-use price. For the risk evaluation of an MG, the Value-at-Risk (VAR is calculated by using the Historical Simulation Method (HSM. By considering the various confidence levels of the VAR, a scheduling dispatch model of the MG is formulated to achieve a reasonable trade-off between the risk and cost. An Improved Bee Swarm Optimization (IBSO is proposed to solve the scheduling dispatch model of the MG. In the IBSO procedure, the Sin-wave Weight Factor (SWF and Forward-Backward Control Factor (FBCF are embedded in the bee swarm of the BSO to improve the movement behaviors of each bee, specifically, its search efficiency and accuracy. The effectiveness of the IBSO is demonstrated via a real MG case and the results are compared with other methods. In either a grid-connected scenario or a stand-alone scenario, an optimal scheduling dispatch of MGs is carried out, herein, at various confidence levels of risk. The simulation results provide more information for handling uncertain environments when analyzing the VAR of MGs.

  19. Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Nadia ALAM

    2013-08-01

    Full Text Available In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

  20. Using Optimization Models for Scheduling in Enterprise Resource Planning Systems

    Directory of Open Access Journals (Sweden)

    Frank Herrmann

    2016-03-01

    Full Text Available Companies often use specially-designed production systems and change them from time to time. They produce small batches in order to satisfy specific demands with the least tardiness. This imposes high demands on high-performance scheduling algorithms which can be rapidly adapted to changes in the production system. As a solution, this paper proposes a generic approach: solutions were obtained using a widely-used commercially-available tool for solving linear optimization models, which is available in an Enterprise Resource Planning System (in the SAP system for example or can be connected to it. In a real-world application of a flow shop with special restrictions this approach is successfully used on a standard personal computer. Thus, the main implication is that optimal scheduling with a commercially-available tool, incorporated in an Enterprise Resource Planning System, may be the best approach.

  1. Optimal scheduling in call centers with a callback option

    OpenAIRE

    Legros , Benjamin; Jouini , Oualid; Koole , Ger

    2016-01-01

    International audience; We consider a call center model with a callback option, which allows to transform an inbound call into an outbound one. A delayed call, with a long anticipated waiting time, receives the option to be called back. We assume a probabilistic customer reaction to the callback offer (option). The objective of the system manager is to characterize the optimal call scheduling that minimizes the expected waiting and abandonment costs. For the single-server case, we prove that ...

  2. Optimal Results and Numerical Simulations for Flow Shop Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Tao Ren

    2012-01-01

    Full Text Available This paper considers the m-machine flow shop problem with two objectives: makespan with release dates and total quadratic completion time, respectively. For Fm|rj|Cmax, we prove the asymptotic optimality for any dense scheduling when the problem scale is large enough. For Fm‖ΣCj2, improvement strategy with local search is presented to promote the performance of the classical SPT heuristic. At the end of the paper, simulations show the effectiveness of the improvement strategy.

  3. Resource-Optimal Scheduling Using Priced Timed Automata

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Rasmussen, Jacob Illum; Subramani, K.

    2004-01-01

    In this paper, we show how the simple structure of the linear programs encountered during symbolic minimum-cost reachability analysis of priced timed automata can be exploited in order to substantially improve the performance of the current algorithm. The idea is rooted in duality of linear......-80 percent performance gain. As a main application area, we show how to solve energy-optimal task graph scheduling problems using the framework of priced timed automata....

  4. Global Optimization of Nonlinear Blend-Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Pedro A. Castillo Castillo

    2017-04-01

    Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.

  5. A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids

    Directory of Open Access Journals (Sweden)

    Nian Liu

    2016-12-01

    Full Text Available With the development of microgrids (MGs, interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG with a battery energy storage system (BESS and renewable energy resources (RESs. The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM, a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency.

  6. Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

    Directory of Open Access Journals (Sweden)

    Ghulam Hafeez

    2018-03-01

    Full Text Available With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV units to reduce electricity cost and peak to average ratio (PAR in demand-side management. For this purpose, we adopted genetic algorithm (GA, binary particle swarm optimization (BPSO, wind-driven optimization (WDO, and our proposed genetic WDO (GWDO algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP and inclined block rate (IBR were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1 load scheduling without renewable energy sources (RESs and energy storage system (ESS, (2 load scheduling with RESs, and (3 load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

  7. Optimal scheduling of micro grids based on single objective programming

    Science.gov (United States)

    Chen, Yue

    2018-04-01

    Faced with the growing demand for electricity and the shortage of fossil fuels, how to optimally optimize the micro-grid has become an important research topic to maximize the economic, technological and environmental benefits of the micro-grid. This paper considers the role of the battery and the micro-grid and power grid to allow the exchange of power not exceeding 150kW preconditions, the main study of the economy to load for the goal is to minimize the electricity cost (abandonment of wind), to establish an optimization model, and to solve the problem by genetic algorithm. The optimal scheduling scheme is obtained and the utilization of renewable energy and the impact of the battery involved in regulation are analyzed.

  8. Terminal Control Area Aircraft Scheduling and Trajectory Optimization Approaches

    Directory of Open Access Journals (Sweden)

    Samà Marcella

    2017-01-01

    Full Text Available Aviation authorities are seeking optimization methods to better use the available infrastructure and better manage aircraft movements. This paper deals with the realtime scheduling of take-off and landing aircraft at a busy terminal control area and with the optimization of aircraft trajectories during the landing procedures. The first problem aims to reduce the propagation of delays, while the second problem aims to either minimize the travel time or reduce the fuel consumption. Both problems are particularly complex, since the first one is NP-hard while the second one is nonlinear and a combined solution needs to be computed in a short-time during operations. This paper proposes a framework for the lexicographic optimization of the two problems. Computational experiments are performed for the Milano Malpensa airport and show the existing gaps between the performance indicators of the two problems when different lexicographic optimization approaches are considered.

  9. A probabilistic computational framework for bridge network optimal maintenance scheduling

    International Nuclear Information System (INIS)

    Bocchini, Paolo; Frangopol, Dan M.

    2011-01-01

    This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.

  10. Fog computing job scheduling optimization based on bees swarm

    Science.gov (United States)

    Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

    2018-04-01

    Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

  11. Optimal residential smart appliances scheduling considering distribution network constraints

    Directory of Open Access Journals (Sweden)

    Yu-Ree Kim

    2016-01-01

    Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.

  12. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  13. Flow shop scheduling algorithm to optimize warehouse activities

    Directory of Open Access Journals (Sweden)

    P. Centobelli

    2016-01-01

    Full Text Available Successful flow-shop scheduling outlines a more rapid and efficient process of order fulfilment in warehouse activities. Indeed the way and the speed of order processing and, in particular, the operations concerning materials handling between the upper stocking area and a lower forward picking one must be optimized. The two activities, drops and pickings, have considerable impact on important performance parameters for Supply Chain wholesaler companies. In this paper, a new flow shop scheduling algorithm is formulated in order to process a greater number of orders by replacing the FIFO logic for the drops activities of a wholesaler company on a daily basis. The System Dynamics modelling and simulation have been used to simulate the actual scenario and the output solutions. Finally, a t-Student test validates the modelled algorithm, granting that it can be used for all wholesalers based on drop and picking activities.

  14. Routing and Scheduling Optimization Model of Sea Transportation

    Science.gov (United States)

    barus, Mika debora br; asyrafy, Habib; nababan, Esther; mawengkang, Herman

    2018-01-01

    This paper examines the routing and scheduling optimization model of sea transportation. One of the issues discussed is about the transportation of ships carrying crude oil (tankers) which is distributed to many islands. The consideration is the cost of transportation which consists of travel costs and the cost of layover at the port. Crude oil to be distributed consists of several types. This paper develops routing and scheduling model taking into consideration some objective functions and constraints. The formulation of the mathematical model analyzed is to minimize costs based on the total distance visited by the tanker and minimize the cost of the ports. In order for the model of the problem to be more realistic and the cost calculated to be more appropriate then added a parameter that states the multiplier factor of cost increases as the charge of crude oil is filled.

  15. Research on optimizing pass schedule of tandem cold mill

    International Nuclear Information System (INIS)

    Lu, C.; Wang, J.S.; Zhao, Q.L.; Liu, X.H.; Wang, G.D.

    2000-01-01

    In this paper, research on pass schedule of tandem cold mill (TCM) is carried out. According to load (reduction, rolling force, motor power) balance, non-linear equations set with variables of inter-stand thickness is constructed. The pass schedule optimization is carried out by solving the non-linear equations set. As the traditional method, the Newton-Raphson method is used for solving the non-linear equations set. In this paper a new simple method is brought up. On basis of the monotone relations between thickness and load, the inter-stands thickness is adjusted dynamically. The solution of non-linear equations set can be converged by iterative calculation. This method can avoid the derivative calculation used by traditional method. So, this method is simple and calculation speed is high. It is suitable for on-line control. (author)

  16. A PSO approach for preventive maintenance scheduling optimization

    International Nuclear Information System (INIS)

    Pereira, C.M.N.A.; Lapa, C.M.F.; Mol, A.C.A.; Luz, A.F. da

    2009-01-01

    This work presents a Particle Swarm Optimization (PSO) approach for preventive maintenance policy optimization, focused in reliability and cost. The probabilistic model for reliability and cost evaluation is developed in such a way that flexible intervals between maintenance are allowed. As PSO is skilled for realcoded continuous spaces, a non-conventional codification has been developed in order to allow PSO to solve scheduling problems (which is discrete) with variable number of maintenance interventions. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR has been considered. Results demonstrate ability in finding optimal solutions, for which expert knowledge had to be automatically discovered by PSO. (author)

  17. A robust optimization approach for energy generation scheduling in microgrids

    International Nuclear Information System (INIS)

    Wang, Ran; Wang, Ping; Xiao, Gaoxi

    2015-01-01

    Highlights: • A new uncertainty model is proposed for better describing unstable energy demands. • An optimization problem is formulated to minimize the cost of microgrid operations. • Robust optimization algorithms are developed to transform and solve the problem. • The proposed scheme can prominently reduce energy expenses. • Numerical results provide useful insights for future investment policy making. - Abstract: In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and combined heat and power (CHP) generators. In such systems, the fluctuant net demands (i.e., the electricity demands not balanced by renewable energies) and heat demands impose unprecedented challenges. To cope with the uncertainty nature of net demand and heat demand, a new flexible uncertainty model is developed. Specifically, we introduce reference distributions according to predictions and field measurements and then define uncertainty sets to confine net and heat demands. The model allows the net demand and heat demand distributions to fluctuate around their reference distributions. Another difficulty existing in this problem is the indeterminate electricity market prices. We develop chance constraint approximations and robust optimization approaches to firstly transform and then solve the prime problem. Numerical results based on real-world data evaluate the impacts of different parameters. It is shown that our energy generation scheduling strategy performs well and the integration of combined heat and power (CHP) generators effectively reduces the system expenditure. Our research also helps shed some illuminations on the investment policy making for microgrids.

  18. Optimization and Flight Schedules of Pioneer Routes in Papua Province

    Science.gov (United States)

    Ronting, Y.; Adisasmita, S. A.; Hamid, S.; Hustim, M.

    2018-04-01

    The province of Papua has a very varied topography, ranging from swampy lowlands, hills, and plateaus up steep hills. The total area of land is 410,660 km2, which consists of 28 counties and one city, 389 districts and 5.420 villages. The population of Papua Province in 2017 was 3.265.202 people with an average growth of 4.21% per year. The transportation services is still low, especially in the mountainous region, which is isolated and could only be reached by an air transportation mode, causing a considerable price disparity between coastal and mountainous areas. The purpose of this paper is to develop the route optimization and pioneer flight schedules models as an airbridge. This research is conducted by collecting primary data and secondary data. Data is based on field surveys; interviews; discussions with airport authority, official government, etc; and also from various agencies. The analytical tools used to optimization flight schedule and route are analyzed by add-in solver in Microsoft Excel. The results of the analysis we can get a more optimal route so that it can save transportation costs by 7.26%.

  19. Multi-objective group scheduling optimization integrated with preventive maintenance

    Science.gov (United States)

    Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

    2017-11-01

    This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

  20. Optimal Operation of Energy Storage in Power Transmission and Distribution

    Science.gov (United States)

    Akhavan Hejazi, Seyed Hossein

    In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit's charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider

  1. Resource allocation in IT projects: using schedule optimization

    Directory of Open Access Journals (Sweden)

    Michael Chilton

    2014-01-01

    Full Text Available Resource allocation is the process of assigning resources to tasks throughout the life of a project. Despite sophisticated software packages devoted to keeping track of tasks, resources and resource assignments, it is often the case that project managers find some resources over-allocated and therefore unable to complete the assigned work in the allotted amount of time. Most scheduling software has provisions for leveling resources, but the techniques for doing so simply add time to the schedule and may cause delays in tasks that are critical to the project in meeting deadlines. This paper presents a software application that ensures that resources are properly balanced at the beginning of the project and eliminates the situation in which resources become over-allocated. It can be used in a multi-project environment and reused throughout the project as tasks, resource assignments and availability, and the project scope change. The application utilizes the bounded enumeration technique to formulate an optimal schedule for which both the task sequence and resource availability are taken into account. It is run on a database server to reduce the running time and make it a viable application for practitioners.

  2. Microgrid optimal scheduling considering impact of high penetration wind generation

    Science.gov (United States)

    Alanazi, Abdulaziz

    The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

  3. Parameters optimization for magnetic resonance coupling wireless power transmission.

    Science.gov (United States)

    Li, Changsheng; Zhang, He; Jiang, Xiaohua

    2014-01-01

    Taking maximum power transmission and power stable transmission as research objectives, optimal design for the wireless power transmission system based on magnetic resonance coupling is carried out in this paper. Firstly, based on the mutual coupling model, mathematical expressions of optimal coupling coefficients for the maximum power transmission target are deduced. Whereafter, methods of enhancing power transmission stability based on parameters optimal design are investigated. It is found that the sensitivity of the load power to the transmission parameters can be reduced and the power transmission stability can be enhanced by improving the system resonance frequency or coupling coefficient between the driving/pick-up coil and the transmission/receiving coil. Experiment results are well conformed to the theoretical analysis conclusions.

  4. WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules

    International Nuclear Information System (INIS)

    Jeong, J; Deasy, J O

    2014-01-01

    Purpose: Concurrent chemo-radiation therapy (CCRT) has become a more common cancer treatment option with a better tumor control rate for several tumor sites, including head and neck and lung cancer. In this work, possible optimal chemotherapy schedules were investigated by implementing chemotherapy cell-kill into a tumor response model of RT. Methods: The chemotherapy effect has been added into a published model (Jeong et al., PMB (2013) 58:4897), in which the tumor response to RT can be simulated with the effects of hypoxia and proliferation. Based on the two-compartment pharmacokinetic model, the temporal concentration of chemotherapy agent was estimated. Log cell-kill was assumed and the cell-kill constant was estimated from the observed increase in local control due to concurrent chemotherapy. For a simplified two cycle CCRT regime, several different starting times and intervals were simulated with conventional RT regime (2Gy/fx, 5fx/wk). The effectiveness of CCRT was evaluated in terms of reduction in radiation dose required for 50% of control to find the optimal chemotherapy schedule. Results: Assuming the typical slope of dose response curve (γ50=2), the observed 10% increase in local control rate was evaluated to be equivalent to an extra RT dose of about 4 Gy, from which the cell-kill rate of chemotherapy was derived to be about 0.35. Best response was obtained when chemotherapy was started at about 3 weeks after RT began. As the interval between two cycles decreases, the efficacy of chemotherapy increases with broader range of optimal starting times. Conclusion: The effect of chemotherapy has been implemented into the resource-conservation tumor response model to investigate CCRT. The results suggest that the concurrent chemotherapy might be more effective when delayed for about 3 weeks, due to lower tumor burden and a larger fraction of proliferating cells after reoxygenation

  5. REPNET: project scheduling and workflow optimization for Construction Projects

    Directory of Open Access Journals (Sweden)

    Marco Alvise Bragadin

    2013-10-01

    Full Text Available Project planning and control are core processes for construction management. In practice project planning is achieved by network - based techniques like Precedence Diagramming Method (PDM.Indeed many researchers and practitioners claims that networking techniques as such do not provide a suitable model for construction projects. Construction process modeling should incorporate for specific features of resource flows through project activities. So an improved resource scheduling method for construction is developed, called REPNET, based on a precedence network plotted on a resource–space chart and presented with a flow-line chart. The heuristics of REPNET are used to carry out resource timing while optimizing processes flows and resource usage. The method has been tested on a sample project.

  6. Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns

    Directory of Open Access Journals (Sweden)

    Jinwei Gu

    2014-01-01

    Full Text Available This paper studies the problem of scheduling a set of jobs on a single machine subject to stochastic breakdowns, where jobs have to be restarted if preemptions occur because of breakdowns. The breakdown process of the machine is independent of the jobs processed on the machine. The processing times required to complete the jobs are constants if no breakdown occurs. The machine uptimes are independently and identically distributed (i.i.d. and are subject to a uniform distribution. It is proved that the Longest Processing Time first (LPT rule minimizes the expected makespan. For the large-scale problem, it is also showed that the Shortest Processing Time first (SPT rule is optimal to minimize the expected total completion times of all jobs.

  7. Seeking optimality in fruit pulping schedules: A case study*

    Directory of Open Access Journals (Sweden)

    J.H. Van Vuuren

    2014-01-01

    Full Text Available The process of scheduling fruit pulping for the production of fruit juices is of great importance in the beverage industry. Decisions have to be made regarding available processing time, the disposal of fruit that will not be pulped before stock loss due to spoilage, the fulfilment of customer demand and an optimal financial position. Sheduling depends on the capacity of the work force, pulping machine limitations and delivery deadlines. However, the situation is often encountered where the plant manager has to decide which fruit batches (usually from stock piles of overwhelming proportions during the harvesting season are to be pulped in order to minimize losses due to fruit deterioration. Such decisions are usually done manually, based on intuition and experience. A mathematical model is presented here which constructs a pulping strategy while minimising cascading financial losses associated with fruit grade drops within the stock pile. It is shown in particular that a minimisation of fruit losses is not a good criterion for optimality, and that substantial financial gains may be accomplished when minimising financial losses in stead of fruit losses, which is currently standard practice at most fruit pulping plants.

  8. Optimal Algorithms and a PTAS for Cost-Aware Scheduling

    NARCIS (Netherlands)

    L. Chen; N. Megow; R. Rischke; L. Stougie (Leen); J. Verschae

    2015-01-01

    htmlabstractWe consider a natural generalization of classical scheduling problems in which using a time unit for processing a job causes some time-dependent cost which must be paid in addition to the standard scheduling cost. We study the scheduling objectives of minimizing the makespan and the

  9. Optimal Temporal Decoupling in Task Scheduling with Preferences

    NARCIS (Netherlands)

    Endhoven, L.; Klos, T.B.; Witteveen, C.

    2011-01-01

    Multi-agent planning and scheduling concerns finding a joint plan to achieve some set of common goals with several independent agents each aiming to find a plan or schedule for their part of the goals. To avoid conflicts in these individual plans or schedules decoupling is used. Such a decoupling

  10. Optimal scheduling for distributed hybrid system with pumped hydro storage

    International Nuclear Information System (INIS)

    Kusakana, Kanzumba

    2016-01-01

    Highlights: • Pumped hydro storage is proposed for isolated hybrid PV–Wind–Diesel systems. • Optimal control is developed to dispatch power flow economically. • A case study is conducted using the model for an isolated load. • Effects of seasons on the system’s optimal scheduling are examined through simulation. - Abstract: Photovoltaic and wind power generations are currently seen as sustainable options of in rural electrification, particularly in standalone applications. However the variable character of solar and wind resources as well as the variable load demand prevent these generation systems from being totally reliable without suitable energy storage system. Several research works have been conducted on the use of photovoltaic and wind systems in rural electrification; however most of these works have not considered other ways of storing energy except for conventional battery storage systems. In this paper, an energy dispatch model that satisfies the load demand, taking into account the intermittent nature of the solar and wind energy sources and variations in demand, is presented for a hybrid system consisting of a photovoltaic unit, a wind unit, a pumped hydro storage system and a diesel generator. The main purpose of the developed model is to minimize the hybrid system’s operation cost while optimizing the system’s power flow considering the different component’s operational constraints. The simulations have been performed using “fmincon” implemented in Matlab. The model have been applied to two test examples; the simulation results are analyzed and compared to the case where the diesel generator is used alone to supply the given load demand. The results show that using the developed control model for the proposed hybrid system, fuel saving can be achieved compared to the case where the diesel is used alone to supply the same load patters.

  11. Distributed decision-making in electric power system transmission maintenance scheduling using multi-agent systems (MAS)

    Science.gov (United States)

    Zhang, Zhong

    In this work, motivated by the need to coordinate transmission maintenance scheduling among a multiplicity of self-interested entities in restructured power industry, a distributed decision support framework based on multiagent negotiation systems (MANS) is developed. An innovative risk-based transmission maintenance optimization procedure is introduced. Several models for linking condition monitoring information to the equipment's instantaneous failure probability are presented, which enable quantitative evaluation of the effectiveness of maintenance activities in terms of system cumulative risk reduction. Methodologies of statistical processing, equipment deterioration evaluation and time-dependent failure probability calculation are also described. A novel framework capable of facilitating distributed decision-making through multiagent negotiation is developed. A multiagent negotiation model is developed and illustrated that accounts for uncertainty and enables social rationality. Some issues of multiagent negotiation convergence and scalability are discussed. The relationships between agent-based negotiation and auction systems are also identified. A four-step MAS design methodology for constructing multiagent systems for power system applications is presented. A generic multiagent negotiation system, capable of inter-agent communication and distributed decision support through inter-agent negotiations, is implemented. A multiagent system framework for facilitating the automated integration of condition monitoring information and maintenance scheduling for power transformers is developed. Simulations of multiagent negotiation-based maintenance scheduling among several independent utilities are provided. It is shown to be a viable alternative solution paradigm to the traditional centralized optimization approach in today's deregulated environment. This multiagent system framework not only facilitates the decision-making among competing power system entities, but

  12. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jiaxi Wang

    2016-01-01

    Full Text Available The shunting schedule of electric multiple units depot (SSED is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.

  13. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    Science.gov (United States)

    Jin, Junchen

    2016-01-01

    The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

  14. Optimizing agent-based transmission models for infectious diseases.

    Science.gov (United States)

    Willem, Lander; Stijven, Sean; Tijskens, Engelbert; Beutels, Philippe; Hens, Niel; Broeckhove, Jan

    2015-06-02

    Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations. We have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26% up to more than 70%. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference. Investment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.

  15. Optimal Scheduling of Doctors Outpatient Departments Based on Patients’ Behavior

    Directory of Open Access Journals (Sweden)

    Zongwei Ren

    2016-01-01

    Full Text Available The low operational efficiency in the field of medical and health care has become a serious problem in China; the long time that the patients have to wait for is the main phenomenon of difficult medical service. Medical industry is service-oriented and its main purpose is to make profits, so the benefits and competitiveness of a hospital depend on patient satisfaction. This paper makes a survey on a large hospital in Harbin of China and collects relevant data and then uses the prospect theory to analyze patients’ and doctors’ behavioral characteristics and the model of patient satisfaction is established based on fuzzy theory with a triplet α/β/γ. The optimal scheduling of clinic is described as a problem with the rule of first come, first served which maximizes patient satisfaction for the main goal and minimizes operating costs for the secondary goal. And the corresponding mathematical model is established. Finally, a solution method named plant growth simulation algorithm (PGSA is presented. Then, by means of calculating of the example and comparing with genetic algorithm, the results show that the optimum can be reached; meanwhile the efficiency of the presented algorithm is better than the genetic algorithm.

  16. Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization

    Directory of Open Access Journals (Sweden)

    Carrie Ka Yuk Lin

    2017-01-01

    Full Text Available This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

  17. TRU Waste Management Program. Cost/schedule optimization analysis

    International Nuclear Information System (INIS)

    Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.; Hastings, G.A.

    1985-10-01

    This Current Year Work Plan presents in detail a description of the activities to be performed by the Joint Integration Office Rockwell International (JIO/RI) during FY86. It breaks down the activities into two major work areas: Program Management and Program Analysis. Program Management is performed by the JIO/RI by providing technical planning and guidance for the development of advanced TRU waste management capabilities. This includes equipment/facility design, engineering, construction, and operations. These functions are integrated to allow transition from interim storage to final disposition. JIO/RI tasks include program requirements identification, long-range technical planning, budget development, program planning document preparation, task guidance development, task monitoring, task progress information gathering and reporting to DOE, interfacing with other agencies and DOE lead programs, integrating public involvement with program efforts, and preparation of reports for DOE detailing program status. Program Analysis is performed by the JIO/RI to support identification and assessment of alternatives, and development of long-term TRU waste program capabilities. These analyses include short-term analyses in response to DOE information requests, along with performing an RH Cost/Schedule Optimization report. Systems models will be developed, updated, and upgraded as needed to enhance JIO/RI's capability to evaluate the adequacy of program efforts in various fields. A TRU program data base will be maintained and updated to provide DOE with timely responses to inventory related questions

  18. Optimal OFDMA Downlink Scheduling Under a Control Signaling Cost Constraint

    OpenAIRE

    Larsson, Erik G.

    2010-01-01

    This paper proposes a new algorithm for downlink scheduling in OFDMA systems. The method maximizes the throughput, taking into account the amount of signaling needed to transmit scheduling maps to the users. A combinatorial problem is formulated and solved via a dynamic programming approach reminiscent of the Viterbi algorithm. The total computational complexity of the algorithm is upper boundedby O(K^4N) where K is the number of users that are being considered for scheduling in a frame and N...

  19. Simultaneous optimization of planning and scheduling in an oil refinery

    NARCIS (Netherlands)

    Zondervan, E.; van Boekel, T.P.J.; Fransoo, J.C.; Haan, de A.B.; Postikopoulos, E.N.; Georgiadis, M.C.; Kokossis, A.

    2011-01-01

    In earlier work we have developed and tested a scheduling model [1] in the AIMMS software. In this follow-up contribution we will develop a planning model. Next we will identify the information flow between scheduling model and the planning model. Lastly we will integrate the two models in a

  20. Optimal pricing of transmission and distribution services in electricity supply

    International Nuclear Information System (INIS)

    Farmer, E.D.; Cory, B.J.; Perera, B.L.P.P.

    1995-01-01

    A new strategy for the separate pricing of transmission and distribution services in electricity supply is formulated and evaluated. The proposed methodology is a multivariate transmission generalisation of the method of peak load pricing previously applied to the optimal time-of-use pricing of generation on a power system with diverse generation technologies and with elastic demand. The method allocates both capacity and operational costs on a time-of-use basis, in an optimal manner, that avoids cross-subsidisation both between differing supply system participants and differing times of usage. The method is shown to promote the optimal development of the transmission, distribution or interconnecting systems, rewarding justified investments in transmission capacity and discouraging overinvestment. It also leads to appropriate returns on invested capital without significant 'revenue reconciliation'. This contrasts with SRMC pricing as is shown by a comparative revenue evaluation. It is concluded that the method has wide potential application in electricity supply. (author)

  1. Topology-optimized broadband surface relief transmission grating

    DEFF Research Database (Denmark)

    Andkjær, Jacob; Ryder, Christian P.; Nielsen, Peter C.

    2014-01-01

    We propose a design methodology for systematic design of surface relief transmission gratings with optimized diffraction efficiency. The methodology is based on a gradient-based topology optimization formulation along with 2D frequency domain finite element simulations for TE and TM polarized plane...

  2. OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

    Directory of Open Access Journals (Sweden)

    Dhananjay Kumar

    2016-01-01

    Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

  3. PARTICAL SWARM OPTIMIZATION OF TASK SCHEDULING IN CLOUD COMPUTING

    OpenAIRE

    Payal Jaglan*, Chander Diwakar

    2016-01-01

    Resource provisioning and pricing modeling in cloud computing makes it an inevitable technology both on developer and consumer end. Easy accessibility of software and freedom of hardware configuration increase its demand in IT industry. It’s ability to provide a user-friendly environment, software independence, quality, pricing index and easy accessibility of infrastructure via internet. Task scheduling plays an important role in cloud computing systems. Task scheduling in cloud computing mea...

  4. Second Order Cone Programming (SOCP) Relaxation Based Optimal Power Flow with Hybrid VSC-HVDC Transmission and Active Distribution Networks

    DEFF Research Database (Denmark)

    Ding, Tao; Li, Cheng; Yang, Yongheng

    2017-01-01

    The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...

  5. A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan

    International Nuclear Information System (INIS)

    Lian Zhigang; Gu Xingsheng; Jiao Bin

    2008-01-01

    It is well known that the flow-shop scheduling problem (FSSP) is a branch of production scheduling and is NP-hard. Now, many different approaches have been applied for permutation flow-shop scheduling to minimize makespan, but current algorithms even for moderate size problems cannot be solved to guarantee optimality. Some literatures searching PSO for continuous optimization problems are reported, but papers searching PSO for discrete scheduling problems are few. In this paper, according to the discrete characteristic of FSSP, a novel particle swarm optimization (NPSO) algorithm is presented and successfully applied to permutation flow-shop scheduling to minimize makespan. Computation experiments of seven representative instances (Taillard) based on practical data were made, and comparing the NPSO with standard GA, we obtain that the NPSO is clearly more efficacious than standard GA for FSSP to minimize makespan

  6. Sensitivity of the optimal parameter settings for a LTE packet scheduler

    NARCIS (Netherlands)

    Fernandez-Diaz, I.; Litjens, R.; van den Berg, C.A.; Dimitrova, D.C.; Spaey, K.

    Advanced packet scheduling schemes in 3G/3G+ mobile networks provide one or more parameters to optimise the trade-off between QoS and resource efficiency. In this paper we study the sensitivity of the optimal parameter setting for packet scheduling in LTE radio networks with respect to various

  7. Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

    KAUST Repository

    Liang, Faming; Cheng, Yichen; Lin, Guang

    2014-01-01

    cooling schedule, for example, a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural

  8. The optimization of wireless power transmission: design and realization.

    Science.gov (United States)

    Jia, Zhiwei; Yan, Guozheng; Liu, Hua; Wang, Zhiwu; Jiang, Pingping; Shi, Yu

    2012-09-01

    A wireless power transmission system is regarded as a practical way of solving power-shortage problems in multifunctional active capsule endoscopes. The uniformity of magnetic flux density, frequency stability and orientation stability are used to evaluate power transmission stability, taking into consideration size and safety constraints. Magnetic field safety and temperature rise are also considered. Test benches are designed to measure the relevent parameters. Finally, a mathematical programming model in which these constraints are considered is proposed to improve transmission efficiency. To verify the feasibility of the proposed method, various systems for a wireless active capsule endoscope are designed and evaluated. The optimal power transmission system has the capability to supply continuously at least 500 mW of power with a transmission efficiency of 4.08%. The example validates the feasibility of the proposed method. Introduction of novel designs enables further improvement of this method. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Monitoring Strategies in Permeable Pavement Systems to Optimize Maintenance Scheduling

    Science.gov (United States)

    As the surface in a permeable pavement system clogs and performance decreases, maintenance is required to preserve the design function. Currently, guidance is limited for scheduling maintenance on an as needed basis. Previous research has shown that surface clogging in a permea...

  10. optimal scheduling of petroleum products distribution in nigeria

    African Journals Online (AJOL)

    MECHANICAL ENGINEERING

    The study reveals that any variation in supply, demand and ... and storage depots for easy shipment of the products from ... The system should be robust yet simple to support routine ..... (10)Klabjan, D. Topics in airline crew scheduling and ...

  11. An optimal algorithm for preemptive on-line scheduling

    NARCIS (Netherlands)

    Chen, B.; Vliet, van A.; Woeginger, G.J.

    1995-01-01

    We investigate the problem of on-line scheduling jobs on m identical parallel machines where preemption is allowed. The goal is to minimize the makespan. We derive an approximation algorithm with worst-case guarantee mm/(mm - (m - 1)m) for every m 2, which increasingly tends to e/(e - 1) ˜ 1.58 as m

  12. Scheduling with Optimized Communication for Time-Triggered Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    1999-01-01

    We present an approach to process scheduling for synthesis of safety-critical distributed embedded systems.Our system model captures both the flow of data and that of control. The communication model is based on a time-triggered protocol. We take into consideration overheads due to communication...

  13. Optimal Power Scheduling for an Islanded Hybrid Microgrid

    DEFF Research Database (Denmark)

    Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi

    2016-01-01

    model, wherein the disconnection of the load and not charging the battery when there is surplus of energy are penalized while physical constraints and requirements for a feasible deployment in the real system are considered. The proposed scheduling scheme is tested using a real-time control platform (d...

  14. Simulation and Optimization Methodologies for Military Transportation Network Routing and Scheduling and for Military Medical Services

    National Research Council Canada - National Science Library

    Rodin, Ervin Y

    2005-01-01

    The purpose of this present research was to develop a generic model and methodology for analyzing and optimizing large-scale air transportation networks including both their routing and their scheduling...

  15. Optimal Transmission Line Switching under Geomagnetic Disturbances

    International Nuclear Information System (INIS)

    Lu, Mowen; Nagarajan, Harsha; Yamangil, Emre; Bent, Russell; Backhaus, Scott

    2017-01-01

    Recently, there have been increasing concerns about how geomagnetic disturbances (GMDs) impact electrical power systems. Geomagnetically-induced currents (GICs) can saturate transformers, induce hot spot heating and increase reactive power losses. These effects can potentially cause catastrophic damage to transformers and severely impact the ability of a power system to deliver power. To address this problem, we develop a model of GIC impacts to power systems that includes 1) GIC thermal capacity of transformers as a function of normal Alternating Current (AC) and 2) reactive power losses as a function of GIC. We also use this model to derive an optimization problem that protects power systems from GIC impacts through line switching, generator dispatch, and load shedding. We then employ state-of-the-art convex relaxations of AC power flow equations to lower bound the objective. We demonstrate the approach on a modified RTS96 system and UIUC 150-bus system and show that line switching is an effective means to mitigate GIC impacts. We also provide a sensitivity analysis of decisions with respect to GMD direction.

  16. Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

    Science.gov (United States)

    Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

    2014-01-01

    A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

  17. Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

    Science.gov (United States)

    Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

    2014-01-01

    A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

  18. Optimizing Training Event Schedules at Naval Air Station Fallon

    Science.gov (United States)

    2018-03-01

    Time VBA Visual Basic for Applications WTI Weapons and Tactics Instructor xiii THIS PAGE INTENTIONALLY LEFT BLANK xiv Executive Summary Naval Air...emitter inventory for each site. Constraint (3.6) ensures scheduled flight events have access to an emitter, at the proper location, required for the...flight event requests and their respective requirements into a specificmacro-enabled excel worksheet (Microsoftl, 2017). A series of VBA ( VBA , 2017

  19. Preemptive Online Scheduling: Optimal Algorithms for All Speeds

    Czech Academy of Sciences Publication Activity Database

    Ebenlendr, Tomáš; Jawor, W.; Sgall, Jiří

    2009-01-01

    Roč. 53, č. 4 (2009), s. 504-522 ISSN 0178-4617 R&D Projects: GA MŠk(CZ) 1M0545; GA ČR GA201/05/0124; GA AV ČR IAA1019401 Institutional research plan: CEZ:AV0Z10190503 Keywords : anline algorithms * scheduling Subject RIV: IN - Informatics, Computer Science Impact factor: 0.917, year: 2009

  20. Optimal and online preemptive scheduling on uniformly related machines

    Czech Academy of Sciences Publication Activity Database

    Ebenlendr, Tomáš; Sgall, J.

    2009-01-01

    Roč. 12, č. 5 (2009), s. 517-527 ISSN 1094-6136 R&D Projects: GA MŠk(CZ) 1M0545; GA AV ČR IAA100190902; GA AV ČR IAA1019401 Institutional research plan: CEZ:AV0Z10190503 Keywords : online scheduling * preemption * uniformly related machines Subject RIV: IN - Informatics, Computer Science Impact factor: 1.265, year: 2009

  1. Military Free Fall Scheduling And Manifest Optimization Model

    Science.gov (United States)

    2016-12-01

    zone. As interest in qualifying more personnel increased, the course expanded. By the mid-1990s, Reyes explains, a new location was required to better...Since 2005, the Chilean Professional Soccer Association has used operations research techniques to schedule professional leagues in Chile . These...a new parachute the students are using, the RA-1. The RA-1 parachute has a longer glide ratio, which means the rate of descent is slower than with

  2. An optimal dynamic interval preventive maintenance scheduling for series systems

    International Nuclear Information System (INIS)

    Gao, Yicong; Feng, Yixiong; Zhang, Zixian; Tan, Jianrong

    2015-01-01

    This paper studies preventive maintenance (PM) with dynamic interval for a multi-component system. Instead of equal interval, the time of PM period in the proposed dynamic interval model is not a fixed constant, which varies from interval-down to interval-up. It is helpful to reduce the outage loss on frequent repair parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency, when compared to a periodic PM scheme. According to the definition of dynamic interval, the reliability of system is analyzed from the failure mechanisms of its components and the different effects of non-periodic PM actions on the reliability of the components. Following the proposed model of reliability, a novel framework for solving the non-periodical PM schedule with dynamic interval based on the multi-objective genetic algorithm is proposed. The framework denotes the strategies include updating strategy, deleting strategy, inserting strategy and moving strategy, which is set to correct the invalid population individuals of the algorithm. The values of the dynamic interval and the selections of PM action for the components on every PM stage are determined by achieving a certain level of system availability with the minimum total PM-related cost. Finally, a typical rotary table system of NC machine tool is used as an example to describe the proposed method. - Highlights: • A non-periodic preventive maintenance scheduling model is proposed. • A framework for solving the non-periodical PM schedule problem is developed. • The interval of non-periodic PM is flexible and schedule can be better adjusted. • Dynamic interval leads to more efficient solutions than fixed interval does

  3. Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma.

    Science.gov (United States)

    Boas, F Edward; Do, Bao; Louie, John D; Kothary, Nishita; Hwang, Gloria L; Kuo, William T; Hovsepian, David M; Kantrowitz, Mark; Sze, Daniel Y

    2015-01-01

    To optimize surveillance schedules for the detection of recurrent hepatocellular carcinoma (HCC) after liver-directed therapy. New methods have emerged that allow quantitative analysis and optimization of surveillance schedules for diseases with substantial rates of recurrence such as HCC. These methods were applied to 1,766 consecutive chemoembolization, radioembolization, and radiofrequency ablation procedures performed on 910 patients between 2006 and 2011. Computed tomography or magnetic resonance imaging performed just before repeat therapy was set as the time of "recurrence," which included residual and locally recurrent tumor as well as new liver tumors. Time-to-recurrence distribution was estimated by Kaplan-Meier method. Average diagnostic delay (time between recurrence and detection) was calculated for each proposed surveillance schedule using the time-to-recurrence distribution. An optimized surveillance schedule could then be derived to minimize the average diagnostic delay. Recurrence is 6.5 times more likely in the first year after treatment than in the second. Therefore, screening should be much more frequent in the first year. For eight time points in the first 2 years of follow-up, the optimal schedule is 2, 4, 6, 8, 11, 14, 18, and 24 months. This schedule reduces diagnostic delay compared with published schedules and is cost-effective. The calculated optimal surveillance schedules include shorter-interval follow-up when there is a higher probability of recurrence and longer-interval follow-up when there is a lower probability. Cost can be optimized for a specified acceptable diagnostic delay or diagnostic delay can be optimized within a specified acceptable cost. Copyright © 2015 SIR. Published by Elsevier Inc. All rights reserved.

  4. On Several Fundamental Problems of Optimization, Estimation, and Scheduling in Wireless Communications

    Science.gov (United States)

    Gao, Qian

    compared with the conventional decoupled system with the same spectrum efficiency to demonstrate the power efficiency. Crucial lighting requirements are included as optimization constraints. To control non-linear distortion, the optical peak-to-average-power ratio (PAPR) of LEDs can be individually constrained. With a SVD-based pre-equalizer designed and employed, our scheme can achieve lower BER than counterparts applying zero-forcing (ZF) or linear minimum-mean-squared-error (LMMSE) based post-equalizers. Besides, a binary switching algorithm (BSA) is applied to improve BER performance. The third part looks into a problem of two-phase channel estimation in a relayed wireless network. The channel estimates in every phase are obtained by the linear minimum mean squared error (LMMSE) method. Inaccurate estimate of the relay to destination (RtD) channel in phase 1 could affect estimate of the source to relay (StR) channel in phase 2, which is made erroneous. We first derive a close-form expression for the averaged Bayesian mean-square estimation error (ABMSE) for both phase estimates in terms of the length of source and relay training slots, based on which an iterative searching algorithm is then proposed that optimally allocates training slots to the two phases such that estimation errors are balanced. Analysis shows how the ABMSE of the StD channel estimation varies with the lengths of relay training and source training slots, the relay amplification gain, and the channel prior information respectively. The last part deals with a transmission scheduling problem in a uplink multiple-input-multiple-output (MIMO) wireless network. Code division multiple access (CDMA) is assumed as a multiple access scheme and pseudo-random codes are employed for different users. We consider a heavy traffic scenario, in which each user always has packets to transmit in the scheduled time slots. If the relay is scheduled for transmission together with users, then it operates in a full

  5. Optimal Scheduling of a Multi-Carrier Energy Hub Supplemented By Battery Energy Storage Systems

    DEFF Research Database (Denmark)

    Javadi, Mohammad Sadegh; Anvari-Moghaddam, Amjad; Guerrero, Josep M.

    2017-01-01

    This paper introduces a management model for optimal scheduling of a multi-carrier energy hub. In the proposed hub, three types of assets are considered: dispersed generating systems (DGs) such as micro-combined heat and power (mCHP) units, storage devices such as battery-based electrical storage...... systems (ESSs), and heating/cooling devices such as electrical heater, heat-pumps and absorption chillers. The optimal scheduling and management of the examined energy hub assets in line with electrical transactions with distribution network is modeled as a mixed-integer non-linear optimization problem....... In this regard, optimal operating points of DG units as well as ESSs are calculated based on a cost-effective strategy. Degradation cost of ESSs is also taken into consideration for short-term scheduling. Simulation results demonstrate that including well-planned energy storage options together with optimal...

  6. DAILY SCHEDULING OF SMALL HYDRO POWER PLANTS DISPATCH WITH MODIFIED PARTICLES SWARM OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Sinvaldo Rodrigues Moreno

    2015-04-01

    Full Text Available This paper presents a new approach for short-term hydro power scheduling of reservoirs using an algorithm-based Particle Swarm Optimization (PSO. PSO is a population-based algorithm designed to find good solutions to optimization problems, its characteristics have encouraged its adoption to tackle a variety of problems in different fields. In this paper the authors consider an optimization problem related to a daily scheduling of small hydro power dispatch. The goal is construct a feasible solution that maximize the cascade electricity production, following the environmental constraints and water balance. The paper proposes an improved Particle Swarm Optimization (PSO algorithm, which takes advantage of simplicity and facility of implementation. The algorithm was successfully applied to the optimization of the daily schedule strategies of small hydro power plants, considering maximum water utilization and all constraints related to simultaneous water uses. Extensive computational tests and comparisons with other heuristics methods showed the effectiveness of the proposed approach.

  7. Packet-Scheduling Algorithm by the Ratio of Transmit Power to the Transmission Bits in 3GPP LTE Downlink

    Directory of Open Access Journals (Sweden)

    Gil Gye-Tae

    2010-01-01

    Full Text Available Packet scheduler plays the central role in determining the overall performance of the 3GPP long-term evolution (LTE based on packet-switching operation. In this paper, a novel minimum transmit power-based (MP packet-scheduling algorithm is proposed that can achieve power-efficient transmission to the UEs while providing both system throughput gain and fairness improvement. The proposed algorithm is based on a new scheduling metric focusing on the ratio of the transmit power per bit and allocates the physical resource block (PRB to the UE that requires the least ratio of the transmit power per bit. Through computer simulation, the performance of the proposed MP packet-scheduling algorithm is compared with the conventional packet-scheduling algorithms by two primary criteria: fairness and throughput. The simulation results show that the proposed algorithm outperforms the conventional algorithms in terms of the fairness and throughput.

  8. Scheduling algorithm for data relay satellite optical communication based on artificial intelligent optimization

    Science.gov (United States)

    Zhao, Wei-hu; Zhao, Jing; Zhao, Shang-hong; Li, Yong-jun; Wang, Xiang; Dong, Yi; Dong, Chen

    2013-08-01

    Optical satellite communication with the advantages of broadband, large capacity and low power consuming broke the bottleneck of the traditional microwave satellite communication. The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical satellite communication. Considering the resources, missions and restraints of Data Relay Satellite Optical Communication System, a model of optical communication resources scheduling is established and a scheduling algorithm based on artificial intelligent optimization is put forwarded. According to the multi-relay-satellite, multi-user-satellite, multi-optical-antenna and multi-mission with several priority weights, the resources are scheduled reasonable by the operation: "Ascertain Current Mission Scheduling Time" and "Refresh Latter Mission Time-Window". The priority weight is considered as the parameter of the fitness function and the scheduling project is optimized by the Genetic Algorithm. The simulation scenarios including 3 relay satellites with 6 optical antennas, 12 user satellites and 30 missions, the simulation result reveals that the algorithm obtain satisfactory results in both efficiency and performance and resources scheduling model and the optimization algorithm are suitable in multi-relay-satellite, multi-user-satellite, and multi-optical-antenna recourses scheduling problem.

  9. Congestion game scheduling for virtual drug screening optimization

    Science.gov (United States)

    Nikitina, Natalia; Ivashko, Evgeny; Tchernykh, Andrei

    2018-02-01

    In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.

  10. Cross-layer optimized rate adaptation and scheduling for multiple-user wireless video streaming

    NARCIS (Netherlands)

    Ozcelebi, T.; Sunay, M.O.; Tekalp, A.M.; Civanlar, M.R.

    2007-01-01

    We present a cross-layer optimized video rate adaptation and user scheduling scheme for multi-user wireless video streaming aiming for maximum quality of service (QoS) for each user,, maximum system video throughput, and QoS fairness among users. These objectives are jointly optimized using a

  11. Optimal Scheduling for Energy Harvesting Transmitters with Hybrid Energy Storage

    OpenAIRE

    Ozel, Omur; Shahzad, Khurram; Ulukus, Sennur

    2013-01-01

    We consider data transmission with an energy harvesting transmitter which has a hybrid energy storage unit composed of a perfectly efficient super-capacitor (SC) and an inefficient battery. The SC has finite space for energy storage while the battery has unlimited space. The transmitter can choose to store the harvested energy in the SC or in the battery. The energy is drained from the SC and the battery simultaneously. In this setting, we consider the offline throughput maximization problem ...

  12. Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

    Directory of Open Access Journals (Sweden)

    Litian Duan

    2016-11-01

    Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.

  13. RSM 1.0 - A RESUPPLY SCHEDULER USING INTEGER OPTIMIZATION

    Science.gov (United States)

    Viterna, L. A.

    1994-01-01

    RSM, Resupply Scheduling Modeler, is a fully menu-driven program that uses integer programming techniques to determine an optimum schedule for replacing components on or before the end of a fixed replacement period. Although written to analyze the electrical power system on the Space Station Freedom, RSM is quite general and can be used to model the resupply of almost any system subject to user-defined resource constraints. RSM is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more computationally intensive, integer programming was required for accuracy when modeling systems with small quantities of components. Input values for component life cane be real numbers, RSM converts them to integers by dividing the lifetime by the period duration, then reducing the result to the next lowest integer. For each component, there is a set of constraints that insure that it is replaced before its lifetime expires. RSM includes user-defined constraints such as transportation mass and volume limits, as well as component life, available repair crew time and assembly sequences. A weighting factor allows the program to minimize factors such as cost. The program then performs an iterative analysis, which is displayed during the processing. A message gives the first period in which resources are being exceeded on each iteration. If the scheduling problem is unfeasible, the final message will also indicate the first period in which resources were exceeded. RSM is written in APL2 for IBM PC series computers and compatibles. A stand-alone executable version of RSM is provided; however, this is a "packed" version of RSM which can only utilize the memory within the 640K DOS limit. This executable requires at least 640K of memory and DOS 3.1 or higher. Source code for an APL2/PC workspace version is also provided. This version of RSM can make full use of any

  14. Trusted Service Scheduling and Optimization Strategy Design of Service Recommendation

    Directory of Open Access Journals (Sweden)

    Xiaona Xia

    2017-01-01

    Full Text Available More and more Web services raise the demands of personalized service recommendation; there exist some recommendation technologies, which improve the qualities of service recommendation by using service ranking and collaborative filtering. However, privacy and security are also important issues in service scheduling process; social relationships have been the key factors of interpersonal communication; service selection based on user preferences has become an inevitable trend. Starting from user demand preferences, this paper analyzes social topology and service demand information and obtains trusted social relationships; then we construct the fusion model of service historical preferences and potential ones; according to social service recommendation demands, TSRSR algorithm has completed designing. Through experiments, TSRSR algorithm is much better than the others, which can effectively improve potential preferences’ learning. Furthermore, the research results of this paper have more significance to study the security and privacy of service recommendation.

  15. A Degree Distribution Optimization Algorithm for Image Transmission

    Science.gov (United States)

    Jiang, Wei; Yang, Junjie

    2016-09-01

    Luby Transform (LT) code is the first practical implementation of digital fountain code. The coding behavior of LT code is mainly decided by the degree distribution which determines the relationship between source data and codewords. Two degree distributions are suggested by Luby. They work well in typical situations but not optimally in case of finite encoding symbols. In this work, the degree distribution optimization algorithm is proposed to explore the potential of LT code. Firstly selection scheme of sparse degrees for LT codes is introduced. Then probability distribution is optimized according to the selected degrees. In image transmission, bit stream is sensitive to the channel noise and even a single bit error may cause the loss of synchronization between the encoder and the decoder. Therefore the proposed algorithm is designed for image transmission situation. Moreover, optimal class partition is studied for image transmission with unequal error protection. The experimental results are quite promising. Compared with LT code with robust soliton distribution, the proposed algorithm improves the final quality of recovered images obviously with the same overhead.

  16. Optimal transmission planning under the Mexican new electricity market

    International Nuclear Information System (INIS)

    Zenón, Eric; Rosellón, Juan

    2017-01-01

    This paper addresses electricity transmission planning under the new industry and institutional structure of the Mexican electricity market, which has engaged in a deep reform process after decades of a state-owned-vertically-integrated-non-competitive-closed industry. Under this new structure, characterized by a nodal pricing system and an independent system operator (ISO), we analyze welfare-optimal network expansion with two modeling strategies. In a first model, we propose the use of an incentive price-cap mechanism to promote the expansion of Mexican networks. In a second model, we study centrally-planned grid expansion in Mexico by an ISO within a power-flow model. We carry out comparisons of these models which provide us with hints to evaluate the actual transmission planning process proposed by Mexican authorities (PRODESEN). We obtain that the PRODESEN plan appears to be a convergent welfare-optimal planning process. - Highlights: • We model transmission planning (PRODESEN) in the Mexican new electricity market. • We propose a first model with a price-cap mechanism to promote network expansion. • In a second power-flow model, we study centrally-planned grid expansions. • The PRODESEN appears to be a convergent welfare-optimal planning process. • Incentive regulation could further help to implement such an optimal process.

  17. HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

    Science.gov (United States)

    Hua, Hook; Mrozinski, Joseph J.; Elfes, Alberto; Adumitroaie, Virgil; Shelton, Kacie E.; Smith, Jeffrey H.; Lincoln, William P.; Weisbin, Charles R.

    2012-01-01

    HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

  18. Scheduling home-appliances to optimize energy consumption

    DEFF Research Database (Denmark)

    Rossello Busquet, Ana

    In order to optimize the energy consumption, energy demand peaks should be avoided, and energy consumption should be smoothly distributed over time. This can be achieved by setting a maximum energy consumption per user’s household. In other words, the overall consumption of the user’s appliances...

  19. TaskMaster: a prototype graphical user interface to a schedule optimization model

    OpenAIRE

    Banham, Stephen R.

    1990-01-01

    Approved for public release, distribution is unlimited This thesis investigates the use of current graphical interface techniques to build more effective computer-user interfaces to Operations Research (OR) schedule optimization models. The design is directed at the scheduling decision maker who possesses limited OR experience. The feasibility and validity of building an interface for this kind of user is demonstrated in the development of a prototype graphical user interface called TaskMa...

  20. Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway

    Directory of Open Access Journals (Sweden)

    Wenliang Zhou

    2015-01-01

    Full Text Available For providing passengers with periodic operation trains and making trains’ time distribution better fit that of passengers, the multiperiod mixed train schedule is first proposed in this paper. It makes each type of train having same origin, destination, route, and stop stations operate based on a periodic basis and allows different types of train to have various operation periods. Then a model of optimizing multiperiod mixed train schedule is built to minimize passengers generalized travel costs with the constraints of trains of same type operating periodically, safe interval requirements of trains’ departure, and arrival times, and so forth. And its heuristic algorithm is designed to optimize the multiperiod mixed train schedule beginning with generating an initial solution by scheduling all types of train type by type and then repeatedly improving their periodic schedules until the objective value cannot be reduced or the iteration number reaches its maximum. Finally, example results illustrate that the proposed model and algorithm can effectively gain a better multiperiod mixed train schedule. However, its passengers deferred times and advanced times are a little higher than these of an aperiodic train schedule.

  1. Constraint optimization model of a scheduling problem for a robotic arm in automatic systems

    DEFF Research Database (Denmark)

    Kristiansen, Ewa; Smith, Stephen F.; Kristiansen, Morten

    2014-01-01

    are characteristics of the painting process application itself. Unlike spot-welding, painting tasks require movement of the entire robot arm. In addition to minimizing intertask duration, the scheduler must strive to maximize painting quality and the problem is formulated as a multi-objective optimization problem....... The scheduling model is implemented as a stand-alone module using constraint programming, and integrated with a larger automatic system. The results of a number of simulation experiments with simple parts are reported, both to characterize the functionality of the scheduler and to illustrate the operation...... of the entire software system for automatic generation of robot programs for painting....

  2. Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis

    Directory of Open Access Journals (Sweden)

    Joshua Kiddy K. Asamoah

    2017-01-01

    Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.

  3. A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Liping Liu

    2018-01-01

    Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

  4. Optimization of time–temperature schedule for nitridation of silicon ...

    Indian Academy of Sciences (India)

    pact was optimized by kinetic study of the reaction, 3Si + 2N2 = Si3N4 at four different temperatures (1250°C,. 1300°C, 1350°C and 1400°C). ... Reaction sintered silicon nitride; nitridation; reaction kinetics. 1. Introduction. Formation of ..... cation of silica layer resulted in active oxidation of silicon at high temperature to ...

  5. Hybrid glowworm swarm optimization for task scheduling in the cloud environment

    Science.gov (United States)

    Zhou, Jing; Dong, Shoubin

    2018-06-01

    In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

  6. Efficient operation scheduling for adsorption chillers using predictive optimization-based control methods

    Science.gov (United States)

    Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz

    2017-10-01

    Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.

  7. Optimization of scheduling system for plant watering using electric cars in agro techno park

    Science.gov (United States)

    Oktavia Adiwijaya, Nelly; Herlambang, Yudha; Slamin

    2018-04-01

    Agro Techno Park in University of Jember is a special area used for the development of agriculture, livestock and fishery. In this plantation, the process of watering the plants is according to the frequency of each plant needs. This research develops the optimization of plant watering scheduling system using edge coloring of graph. This research was conducted in 3 stages, namely, data collection phase, analysis phase, and system development stage. The collected data was analyzed and then converted into a graph by using bipartite adjacency matrix representation. The development phase is conducted to build a web-based watering schedule optimization system. The result of this research showed that the schedule system is optimal because it can maximize the use of all electric cars to water the plants and minimize the number of idle cars.

  8. Optimal Electric Vehicle Scheduling: A Co-Optimized System and Customer Perspective

    Science.gov (United States)

    Maigha

    Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivising the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle.

  9. Joint Optimization in UMTS-Based Video Transmission

    Directory of Open Access Journals (Sweden)

    Attila Zsiros

    2007-01-01

    Full Text Available A software platform is exposed, which was developed to enable demonstration and capacity testing. The platform simulates a joint optimized wireless video transmission. The development succeeded within the frame of the IST-PHOENIX project and is based on the system optimization model of the project. One of the constitutive parts of the model, the wireless network segment, is changed to a detailed, standard UTRA network simulation module. This paper consists of (1 a brief description of the projects simulation chain, (2 brief description of the UTRAN system, and (3 the integration of the two segments. The role of the UTRAN part in the joint optimization is described, with the configuration and control of this element. Finally, some simulation results are shown. In the conclusion, we show how our simulation results translate into real-world performance gains.

  10. Optimal hydro scheduling and offering strategies considering price uncertainty and risk management

    International Nuclear Information System (INIS)

    Catalão, J.P.S.; Pousinho, H.M.I.; Contreras, J.

    2012-01-01

    Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system. -- Highlights: ► A mixed-integer nonlinear programming approach is considered for optimal hydro scheduling. ► Market uncertainty is introduced in the model via price scenarios. ► Risk management is included using conditional value-at-risk. ► Plant scheduling and pool offering by the hydro power producer are simultaneously considered. ► A realistic cascaded hydro system is solved.

  11. An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems.

    Directory of Open Access Journals (Sweden)

    Hajara Idris

    Full Text Available The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

  12. Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

    KAUST Repository

    Liang, Faming

    2014-04-03

    Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to use this much CPU time. This article proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, for example, a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors. Supplementary materials for this article are available online.

  13. Optimization-based sale transactions and hydrothermal scheduling

    International Nuclear Information System (INIS)

    Prasannan, B.; Luh, P.B.; Zhang, L.

    1996-01-01

    Selling and purchasing power are important activities for utilities because of potential savings. When a selling utility presents an offer including prices, power levels and durations, a purchasing utility selects power levels and durations within the offered range subject to relevant constraints. The decisionmaking process is complicated because transactions are coupled with system demand and reserve, therefore decisions have to be made in conjunction with the commitment and dispatching of units. Furthermore, transaction decisions have to be made in almost real time in view of the competitiveness of the power market caused by deregulation. In this paper, transactions are analyzed from a selling utility's viewpoint for a system consisting of thermal, hydro and pumped-storage units. To effectively solve the problem, linear sale revenues are approximated by nonlinear functions, and non-profitable options are identified and eliminated from consideration. The multipliers are then updated at the high level by using a modified subgradient method to obtain near optimal solutions quickly. Testing results show that the algorithm produces good sale offers efficiently

  14. Optimal control of diarrhea transmission in a flood evacuation zone

    Science.gov (United States)

    Erwina, N.; Aldila, D.; Soewono, E.

    2014-03-01

    Evacuation of residents and diarrhea disease outbreak in evacuation zone have become serious problem that frequently happened during flood periods. Limited clean water supply and infrastructure in evacuation zone contribute to a critical spread of diarrhea. Transmission of diarrhea disease can be reduced by controlling clean water supply and treating diarrhea patients properly. These treatments require significant amount of budget, which may not be fulfilled in the fields. In his paper, transmission of diarrhea disease in evacuation zone using SIRS model is presented as control optimum problem with clean water supply and rate of treated patients as input controls. Existence and stability of equilibrium points and sensitivity analysis are investigated analytically for constant input controls. Optimum clean water supply and rate of treatment are found using optimum control technique. Optimal results for transmission of diarrhea and the corresponding controls during the period of observation are simulated numerically. The optimum result shows that transmission of diarrhea disease can be controlled with proper combination of water supply and rate of treatment within allowable budget.

  15. A Distributed Particle Swarm Optimization Zlgorithmfor Flexible Job-hop Scheduling Problem

    Directory of Open Access Journals (Sweden)

    LIU Sheng--hui

    2017-06-01

    Full Text Available According to the characteristics of the Flexible job shop scheduling problem the minimum makespan as measures we proposed a distributed particle swarm optimization algorithm aiming to solve flexible job shop scheduling problem. The algorithm adopts the method of distributed ideas to solve problems and we are established for two multi agent particle swarm optimization model in this algorithm it can solve the traditional particle swarm optimization algorithm when making decisions in real time according to the emergencies. Finally some benthmark problems were experimented and the results are compared with the traditional algorithm. Experimental results proved that the developed distributed PSO is enough effective and efficient to solve the FJSP and it also verified the reasonableness of the multi}gent particle swarm optimization model.

  16. Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

    Science.gov (United States)

    Juliandri, Dedy; Mawengkang, Herman; Bu'ulolo, F.

    2018-01-01

    Vehicle Routing Problem (VRP) is an important element of many logistic systems which involve routing and scheduling of vehicles from a depot to a set of customers node. This is a hard combinatorial optimization problem with the objective to find an optimal set of routes used by a fleet of vehicles to serve the demands a set of customers It is required that these vehicles return to the depot after serving customers’ demand. The problem incorporates time windows, fleet and driver scheduling, pick-up and delivery in the planning horizon. The goal is to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the overall costs of all routes over the planning horizon. We model the problem as a linear mixed integer program. We develop a combination of heuristics and exact method for solving the model.

  17. Operational Excellence through Schedule Optimization and Production Simulation of Application Specific Integrated Circuits.

    Energy Technology Data Exchange (ETDEWEB)

    Flory, John Andrew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Padilla, Denise D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gauthier, John H. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Zwerneman, April Marie [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Miller, Steven P [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-05-01

    Upcoming weapon programs require an aggressive increase in Application Specific Integrated Circuit (ASIC) production at Sandia National Laboratories (SNL). SNL has developed unique modeling and optimization tools that have been instrumental in improving ASIC production productivity and efficiency, identifying optimal operational and tactical execution plans under resource constraints, and providing confidence in successful mission execution. With ten products and unprecedented levels of demand, a single set of shared resources, highly variable processes, and the need for external supplier task synchronization, scheduling is an integral part of successful manufacturing. The scheduler uses an iterative multi-objective genetic algorithm and a multi-dimensional performance evaluator. Schedule feasibility is assessed using a discrete event simulation (DES) that incorporates operational uncertainty, variability, and resource availability. The tools provide rapid scenario assessments and responses to variances in the operational environment, and have been used to inform major equipment investments and workforce planning decisions in multiple SNL facilities.

  18. Optimal methodology for a machining process scheduling in spot electricity markets

    International Nuclear Information System (INIS)

    Yusta, J.M.; Torres, F.; Khodr, H.M.

    2010-01-01

    Electricity spot markets have introduced hourly variations in the price of electricity. These variations allow the increase of the energy efficiency by the appropriate scheduling and adaptation of the industrial production to the hourly cost of electricity in order to obtain the maximum profit for the industry. In this article a mathematical optimization model simulates costs and the electricity demand of a machining process. The resultant problem is solved using the generalized reduced gradient approach, to find the optimum production schedule that maximizes the industry profit considering the hourly variations of the price of electricity in the spot market. Different price scenarios are studied to analyze the impact of the spot market prices for electricity on the optimal scheduling of the machining process and on the industry profit. The convenience of the application of the proposed model is shown especially in cases of very high electricity prices.

  19. Optimal trajectory planning and train scheduling for urban rail transit systems

    CERN Document Server

    Wang, Yihui; van den Boom, Ton; De Schutter, Bart

    2016-01-01

    This book contributes to making urban rail transport fast, punctual and energy-efficient –significant factors in the importance of public transportation systems to economic, environmental and social requirements at both municipal and national levels. It proposes new methods for shortening passenger travel times and for reducing energy consumption, addressing two major topics: (1) train trajectory planning: the authors derive a nonlinear model for the operation of trains and present several approaches for calculating optimal and energy-efficient trajectories within a given schedule; and (2) train scheduling: the authors develop a train scheduling model for urban rail systems and optimization approaches with which to balance total passenger travel time with energy efficiency and other costs to the operator. Mixed-integer linear programming and pseudospectral methods are among the new methods proposed for single- and multi-train systems for the solution of the nonlinear trajectory planning problem which involv...

  20. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

    Science.gov (United States)

    Shah, Rahul H.

    Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the

  1. Optimal joint scheduling of electrical and thermal appliances in a smart home environment

    International Nuclear Information System (INIS)

    Shirazi, Elham; Zakariazadeh, Alireza; Jadid, Shahram

    2015-01-01

    Highlights: • Thermal appliances are scheduled based on desired temperature and energy prices. • A discomfort index has been introduced within the home energy scheduling model. • Appliances are scheduled based on activity probability and desired options. • Starting probability depends on the social random factor and consumption behavior. - Abstract: With the development of home area network, residents have the opportunity to schedule their power usage in the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, a home energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented in which electrical and thermal appliances are jointly scheduled. The proposed method aims at minimizing the electricity cost of a residential customer by scheduling various type of appliances considering the residents consumption behavior, seasonal probability, social random factor, discomfort index and appliances starting probability functions. In this model, the home central controller receives the electricity price information, environmental factors data as well as the resident desired options in order to optimally schedule appliances including electrical and thermal. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, photovoltaic panel, combined heat and power unit, boiler and electrical and thermal storages over a 24-h period. The results show that the scheduling of different appliances can be reached simultaneously by using the proposed formulation. Moreover, simulation results evidenced that the proposed home energy management model exhibits a lower cost and, therefore, is more economical.

  2. A derived heuristics based multi-objective optimization procedure for micro-grid scheduling

    Science.gov (United States)

    Li, Xin; Deb, Kalyanmoy; Fang, Yanjun

    2017-06-01

    With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.

  3. Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

    DEFF Research Database (Denmark)

    Pedersen, Michael Berliner; Crainic, Teodor Gabriel

    We present a modeling approach for optimizing intermodal trains schedules based on an infrastructure divided into time-dependent train paths. The formulation can be generalized to a capacitated multi commodity network design model with additional design balance constraints. We present a Tabu Search...

  4. Optimization of Temperature Schedule Parameters on Heat Supply in Power-and-Heat Supply Systems

    Directory of Open Access Journals (Sweden)

    V. A. Sednin

    2009-01-01

    Full Text Available The paper considers problems concerning optimization of a temperature schedule in the district heating systems with steam-turbine thermal power stations having average initial steam parameters. It has been shown in the paper that upkeeping of an optimum network water temperature permits to increase an energy efficiency of heat supply due to additional systematic saving of fuel. 

  5. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies

    NARCIS (Netherlands)

    Paterakis, N.G.; Erdinç, O.; Bakirtzis, A.G.; Catalao, J.P.S.

    2015-01-01

    In this paper, a detailed home energy management system structure is developed to determine the optimal dayahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets

  6. Robust Optimization-Based Generation Self-Scheduling under Uncertain Price

    Directory of Open Access Journals (Sweden)

    Xiao Luo

    2011-01-01

    Full Text Available This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.

  7. Optimal routes scheduling for municipal waste disposal garbage trucks using evolutionary algorithm and artificial immune system

    Directory of Open Access Journals (Sweden)

    Bogna MRÓWCZYŃSKA

    2011-01-01

    Full Text Available This paper describes an application of an evolutionary algorithm and an artificial immune systems to solve a problem of scheduling an optimal route for waste disposal garbage trucks in its daily operation. Problem of an optimisation is formulated and solved using both methods. The results are presented for an area in one of the Polish cities.

  8. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

    Science.gov (United States)

    Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

    2015-12-01

    For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

  9. Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

    International Nuclear Information System (INIS)

    Zio, E.; Baraldi, P.; Pedroni, N.

    2009-01-01

    Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set. In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM' s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM. The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled

  10. Optimizing donor scheduling before recruitment: An effective approach to increasing apheresis platelet collections.

    Science.gov (United States)

    Lokhandwala, Parvez M; Shike, Hiroko; Wang, Ming; Domen, Ronald E; George, Melissa R

    2018-01-01

    Typical approach for increasing apheresis platelet collections is to recruit new donors. Here, we investigated the effectiveness of an alternative strategy: optimizing donor scheduling, prior to recruitment, at a hospital-based blood donor center. Analysis of collections, during the 89 consecutive months since opening of donor center, was performed. Linear regression and segmented time-series analyses were performed to calculate growth rates of collections and to test for statistical differences, respectively. Pre-intervention donor scheduling capacity was 39/month. In the absence of active donor recruitment, during the first 29 months, the number of collections rose gradually to 24/month (growth-rate of 0.70/month). However, between month-30 and -55, collections exhibited a plateau at 25.6 ± 3.0 (growth-rate of -0.09/month) (pcollection days/week (month-72). Consequently, the scheduling capacity increased to 130/month. Post-interventions, apheresis platelet collections between month-56 and -81 exhibited a spontaneous renewed growth at a rate of 0.62/month (pcollections. Apheresis platelet collections plateau at nearly 2/3rd of the scheduling capacity. Optimizing the scheduling capacity prior to active donor recruitment is an effective strategy to increase platelet collections at a hospital-based donor center.

  11. Permutation flow-shop scheduling problem to optimize a quadratic objective function

    Science.gov (United States)

    Ren, Tao; Zhao, Peng; Zhang, Da; Liu, Bingqian; Yuan, Huawei; Bai, Danyu

    2017-09-01

    A flow-shop scheduling model enables appropriate sequencing for each job and for processing on a set of machines in compliance with identical processing orders. The objective is to achieve a feasible schedule for optimizing a given criterion. Permutation is a special setting of the model in which the processing order of the jobs on the machines is identical for each subsequent step of processing. This article addresses the permutation flow-shop scheduling problem to minimize the criterion of total weighted quadratic completion time. With a probability hypothesis, the asymptotic optimality of the weighted shortest processing time schedule under a consistency condition (WSPT-CC) is proven for sufficiently large-scale problems. However, the worst case performance ratio of the WSPT-CC schedule is the square of the number of machines in certain situations. A discrete differential evolution algorithm, where a new crossover method with multiple-point insertion is used to improve the final outcome, is presented to obtain high-quality solutions for moderate-scale problems. A sequence-independent lower bound is designed for pruning in a branch-and-bound algorithm for small-scale problems. A set of random experiments demonstrates the performance of the lower bound and the effectiveness of the proposed algorithms.

  12. The nurse scheduling problem: a goal programming and nonlinear optimization approaches

    Science.gov (United States)

    Hakim, L.; Bakhtiar, T.; Jaharuddin

    2017-01-01

    Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.

  13. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

    International Nuclear Information System (INIS)

    Kim, Minsun; Stewart, Robert D.; Phillips, Mark H.

    2015-01-01

    Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T d ), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D mean ≤ 45 Gy), lungs (D mean ≤ 20 Gy), cord (D max ≤ 45 Gy), esophagus (D max ≤ 63 Gy), and unspecified tissues (D 05 ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D 95 of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T d (3–100 days), tumor lag-time (T k = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D 95 were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T d and T k used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating tumors with T d less than 10 days, there was no

  14. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Minsun, E-mail: mk688@uw.edu; Stewart, Robert D. [Department of Radiation Oncology, University of Washington, Seattle, Washington 98195-6043 (United States); Phillips, Mark H. [Departments of Radiation Oncology and Neurological Surgery, University of Washington, Seattle, Washington 98195-6043 (United States)

    2015-11-15

    Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating

  15. Transmission Dynamics and Optimal Control of Malaria in Kenya

    Directory of Open Access Journals (Sweden)

    Gabriel Otieno

    2016-01-01

    Full Text Available This paper proposes and analyses a mathematical model for the transmission dynamics of malaria with four-time dependent control measures in Kenya: insecticide treated bed nets (ITNs, treatment, indoor residual spray (IRS, and intermittent preventive treatment of malaria in pregnancy (IPTp. We first considered constant control parameters and calculate the basic reproduction number and investigate existence and stability of equilibria as well as stability analysis. We proved that if R0≤1, the disease-free equilibrium is globally asymptotically stable in D. If R0>1, the unique endemic equilibrium exists and is globally asymptotically stable. The model also exhibits backward bifurcation at R0=1. If R0>1, the model admits a unique endemic equilibrium which is globally asymptotically stable in the interior of feasible region D. The sensitivity results showed that the most sensitive parameters are mosquito death rate and mosquito biting rates. We then consider the time-dependent control case and use Pontryagin’s Maximum Principle to derive the necessary conditions for the optimal control of the disease using the proposed model. The existence of optimal control problem is proved. Numerical simulations of the optimal control problem using a set of reasonable parameter values suggest that the optimal control strategy for malaria control in endemic areas is the combined use of treatment and IRS; for epidemic prone areas is the use of treatment and IRS; for seasonal areas is the use of treatment; and for low risk areas is the use of ITNs and treatment. Control programs that follow these strategies can effectively reduce the spread of malaria disease in different malaria transmission settings in Kenya.

  16. Optimal treatment scheduling of ionizing radiation and sunitinib improves the antitumor activity and allows dose reduction

    International Nuclear Information System (INIS)

    Kleibeuker, Esther A; Hooven, Matthijs A ten; Castricum, Kitty C; Honeywell, Richard; Griffioen, Arjan W; Verheul, Henk M; Slotman, Ben J; Thijssen, Victor L

    2015-01-01

    The combination of radiotherapy with sunitinib is clinically hampered by rare but severe side effects and varying results with respect to clinical benefit. We studied different scheduling regimes and dose reduction in sunitinib and radiotherapy in preclinical tumor models to improve potential outcome of this combination treatment strategy. The chicken chorioallantoic membrane (CAM) was used as an angiogenesis in vivo model and as a xenograft model with human tumor cells (HT29 colorectal adenocarcinoma, OE19 esophageal adenocarcinoma). Treatment consisted of ionizing radiation (IR) and sunitinib as single therapy or in combination, using different dose-scheduling regimes. Sunitinib potentiated the inhibitory effect of IR (4 Gy) on angiogenesis. In addition, IR (4 Gy) and sunitinib (4 days of 32.5 mg/kg per day) inhibited tumor growth. Ionizing radiation induced tumor cell apoptosis and reduced proliferation, whereas sunitinib decreased tumor angiogenesis and reduced tumor cell proliferation. When IR was applied before sunitinib, this almost completely inhibited tumor growth, whereas concurrent IR was less effective and IR after sunitinib had no additional effect on tumor growth. Moreover, optimal scheduling allowed a 50% dose reduction in sunitinib while maintaining comparable antitumor effects. This study shows that the therapeutic efficacy of combination therapy improves when proper dose-scheduling is applied. More importantly, optimal treatment regimes permit dose reductions in the angiogenesis inhibitor, which will likely reduce the side effects of combination therapy in the clinical setting. Our study provides important leads to optimize combination treatment in the clinical setting

  17. Resource planning and scheduling of payload for satellite with particle swarm optimization

    Science.gov (United States)

    Li, Jian; Wang, Cheng

    2007-11-01

    The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces. A particle swarm optimization algorithm is proposed to address the problem with an adaptive mutation operator selection, where the swarm is divided into groups with different probabilities to employ various mutation operators viz., differential evolution, Gaussian and random mutation operators. The probabilities are adjusted adaptively by comparing the effectiveness of the groups to select a proper operator. The simulation results have shown the feasibility and effectiveness of the method.

  18. A priority-based heuristic algorithm (PBHA for optimizing integrated process planning and scheduling problem

    Directory of Open Access Journals (Sweden)

    Muhammad Farhan Ausaf

    2015-12-01

    Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.

  19. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

  20. System and method for optimal load and source scheduling in context aware homes

    Science.gov (United States)

    Shetty, Pradeep; Foslien Graber, Wendy; Mangsuli, Purnaprajna R.; Kolavennu, Soumitri N.; Curtner, Keith L.

    2018-01-23

    A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

  1. Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Kuo-Yang Wu

    2013-01-01

    Full Text Available We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS in the Flexible Manufacturing System (FMS used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA, the Immune Genetic Algorithm (IGA, and the Particle Swarm Optimization (PSO algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

  2. Optimizing the Steel Plate Storage Yard Crane Scheduling Problem Using a Two Stage Planning/Scheduling Approach

    DEFF Research Database (Denmark)

    Hansen, Anders Dohn; Clausen, Jens

    This paper presents the Steel Plate Storage Yard Crane Scheduling Problem. The task is to generate a schedule for two gantry cranes sharing tracks. The schedule must comply with a number of constraints and at the same time be cost efficient. We propose some ideas for a two stage planning...

  3. Joint Scheduling Optimization of Virtual Power Plants and Equitable Profit Distribution Using Shapely Value Theory

    Directory of Open Access Journals (Sweden)

    Zhong-fu Tan

    2018-01-01

    Full Text Available The installation capacity of wind and solar photovoltaic power is continually increasing, which makes renewable energy grid connection and power generation an important link of China’s power structure optimization. A virtual power plant (VPP is an important way to help distributed energy resource grid connection and promote renewable energy industry development. To study the economic scheduling problem of various distributed energy resources and the profit distribution problem of VPP alliance, this study builds a separate operation scheduling model for individual VPP and a joint operation scheduling model for VPP alliance, as well as the profit distribution model. The case study verifies the feasibility and effectiveness of the proposed model. The sensitivity analysis provides information about VPP decision-making in accordance with the policy environment development trend.

  4. Joint optimization scheduling for water conservancy projects in complex river networks

    Directory of Open Access Journals (Sweden)

    Qin Liu

    2017-01-01

    Full Text Available In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

  5. Optimization of operating schedule of machines in granite industry using evolutionary algorithms

    International Nuclear Information System (INIS)

    Loganthurai, P.; Rajasekaran, V.; Gnanambal, K.

    2014-01-01

    Highlights: • Operating time of machines in granite industries was studied. • Operating time has been optimized using evolutionary algorithms such as PSO, DE. • The maximum demand has been reduced. • Hence the electricity cost of the industry and feeder stress have been reduced. - Abstract: Electrical energy consumption cost plays an important role in the production cost of any industry. The electrical energy consumption cost is calculated as two part tariff, the first part is maximum demand cost and the second part is energy consumption cost or unit cost (kW h). The maximum demand cost can be reduced without affecting the production. This paper focuses on the reduction of maximum demand by proper operating schedule of major equipments. For this analysis, various granite industries are considered. The major equipments in granite industries are cutting machine, polishing machine and compressor. To reduce the maximum demand, the operating time of polishing machine is rescheduled by optimization techniques such as Differential Evolution (DE) and particle swarm optimization (PSO). The maximum demand costs are calculated before and after rescheduling. The results show that if the machines are optimally operated, the cost is reduced. Both DE and PSO algorithms reduce the maximum demand cost at the same rate for all the granite industries. However, the optimum scheduling obtained by DE reduces the feeder power flow than the PSO scheduling

  6. A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

    DEFF Research Database (Denmark)

    Sousa, Tiago; Morais, Hugo; Vale, Zita

    2015-01-01

    In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power...... at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power...... scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present...

  7. Preventive maintenance optimization for a multi-component system under changing job shop schedule

    International Nuclear Information System (INIS)

    Zhou Xiaojun; Lu Zhiqiang; Xi Lifeng

    2012-01-01

    Variability and small lot size is a common feature for many discrete manufacturing processes designed to meet a wide array of customer needs. Because of this, job shop schedule often has to be continuously updated in reaction to changes in production plan. Generally, the aim of preventive maintenance is to ensure production effectiveness and therefore the preventive maintenance models must have the ability to be adaptive to changes in job shop schedule. In this paper, a dynamic opportunistic preventive maintenance model is developed for a multi-component system with considering changes in job shop schedule. Whenever a job is completed, preventive maintenance opportunities arise for all the components in the system. An optimal maintenance practice is dynamically determined by maximizing the short-term cumulative opportunistic maintenance cost savings for the system. The numerical example shows that the scheme obtained by the proposed model can effectively address the preventive maintenance scheduling problem caused by the changes in job shop schedule and is more efficient than the ones based on two other commonly used preventive maintenance models.

  8. An Improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

    International Nuclear Information System (INIS)

    Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S.

    2011-01-01

    Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.

  9. An improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

    Energy Technology Data Exchange (ETDEWEB)

    Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S. [Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

    2011-02-15

    Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems. (author)

  10. Collaborative Optimization of Stop Schedule Plan and Ticket Allotment for the Intercity Train

    Directory of Open Access Journals (Sweden)

    Xichun Chen

    2016-01-01

    Full Text Available As regards the ticket allotment issue of the intercity passenger corridor designed for different train grades, the matching relationship between the ticket allotment and the passenger flow demand is studied. The passenger flow conversion equation which is based on the collaborative optimization of the intercity train stop schedule plan and ticket allotment is established. Then the mathematical model aiming at the maximum revenue of intercity train system and the highest satisfaction from the passengers is established. The particle swarm harmony search algorithm is designed to solve the model. The example verifies the effectiveness of the model and algorithm, which indicates that, through the collaborative optimization of the stop schedule plan and ticket allotment for different grades intercity trains, the sectional utilization rate of the train can be improved; meanwhile, the optimum matching between the intercity train revenue and the passenger satisfaction can be realized.

  11. Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

    Science.gov (United States)

    Ge, Junwei; Cai, Yu; Fang, Yiqiu

    2018-05-01

    This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

  12. BINARY PARTICLE SWARM OPTIMIZATION APPROACH FOR RANDOM GENERATION OUTAGE MAINTENANCE SCHEDULING

    Directory of Open Access Journals (Sweden)

    K. Suresh

    2013-01-01

    Full Text Available This paper presents a methodology for maintenance scheduling (MS of generators using binary particle swarm optimization (BPSO based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP for a power system. The capacity outage probability table (COPT is the initial step in creating maintenance schedule using the probabilistic levelized risk method. This paper proposes BPSO method which is used to construct the COPT. In order to mitigate the effects of probabilistic levelized risk method, BPSO based probabilistic levelized risk method is embarked on a MS problem. In order to validate the effectiveness of the proposed algorithm, case study results for simple five unit system can accomplish a significant levelization in the reliability indices that make possible to evaluate system generation system adequacy in the MS horizon of the power system. The proposed method shows better performance compared with other optimization methods and conventional method with improved search performance.

  13. A New Software for Management, Scheduling, and Optimization for the Light Hydrocarbon Pipeline Network System of Daqing Oilfield

    Directory of Open Access Journals (Sweden)

    Yongtu Liang

    2014-01-01

    Full Text Available This paper presents the new software which specifically developed based on Visual Studio 2010 for Daqing Oilfield China includes the most complex light hydrocarbon pipeline network system in Asia, has become a powerful auxiliary tool to manage field data, makes scheduling plans for batching operation, and optimizes pumping plans. Firstly, DMM for recording and managing field data is summarized. Then, the batch scheduling simulation module called SSM for the difficult batch-scheduling issues of the multiple-source pipeline network system is introduced. Finally, SOM, that is Scheduling Optimization Module, is indicated for solving the problem of the pumps being started up/shut-down frequently.

  14. High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience

    OpenAIRE

    Dingjun Chen; Shaoquan Ni; Chang’an Xu; Hongxia Lv; Simin Wang

    2016-01-01

    The stop-schedules for passenger trains are important to the operation planning of high-speed trains, and they decide the quality of passenger service and the transportation efficiency. This paper analyzes the specific manifestation of passenger travel convenience and proposes the concepts of interstation accessibility and degree of accessibility. In consideration of both the economic benefits of railway corporations and the travel convenience of passengers, a multitarget optimization model i...

  15. Optimization of horizontal microcode within and beyond basic blocks: an application of processor scheduling with resources

    Energy Technology Data Exchange (ETDEWEB)

    Fisher, J.A.

    1979-10-01

    Microprogram optimization is the rearrangement of microcode written vertically, with one operation issued per step, into legal horizontal microinstructions, in which several operations are issued each instruction cycle. The rearrangement is done in a way that approximately minimizes the running time of the code. This problem is identified with the problem of processor scheduling with resource constraints. Thus, the problem of optimizing basic blocks of microcode can be seen to be np-complete; however, approximate methods for basic blocks which have good records in other, similar scheduling environments can be used. In priority list scheduling the tasks are ordered according to some evaluation function, and then schedules are found by repeated scans of the list. Several evaluation functions are shown to perform very well on large samples of various classes of random data-precedence graphs with characteristics similar to those derived from microprograms. A method of spotting resource bottlenecks in the derived data-precedence graph enables one to obtain a resource-considerate evaluation function, in which tasks which contribute directly to or precede bottlenecks have their priorities raised. The complexity of the calculations necessary to compute the lower bound was greatly reduced. A method is suggested for optimizing beyond basic blocks. Groups of basic blocks are treated as if they were one block; the information necessary to control the motion of tasks between blocks is encoded as data-precedence constraints on the conditional tasks. Long paths of code can thus be optimized, with no back branches, by the same methods used for basic blocks. 9 figures, 6 tables.

  16. Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

    DEFF Research Database (Denmark)

    Tan, Kang Miao; Ramachandaramurthy, Vigna K.; Yong, Jia Ying

    2017-01-01

    -to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance...... of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various...

  17. Reliability-based optimization of maintenance scheduling of mechanical components under fatigue

    Science.gov (United States)

    Beaurepaire, P.; Valdebenito, M.A.; Schuëller, G.I.; Jensen, H.A.

    2012-01-01

    This study presents the optimization of the maintenance scheduling of mechanical components under fatigue loading. The cracks of damaged structures may be detected during non-destructive inspection and subsequently repaired. Fatigue crack initiation and growth show inherent variability, and as well the outcome of inspection activities. The problem is addressed under the framework of reliability based optimization. The initiation and propagation of fatigue cracks are efficiently modeled using cohesive zone elements. The applicability of the method is demonstrated by a numerical example, which involves a plate with two holes subject to alternating stress. PMID:23564979

  18. Application of genetic algorithms to the maintenance scheduling optimization in a nuclear system basing on reliability

    International Nuclear Information System (INIS)

    Lapa, Celso M. Franklin; Pereira, Claudio M.N.A.; Mol, Antonio C. de Abreu

    1999-01-01

    This paper presents a solution based on genetic algorithm and probabilistic safety analysis that can be applied in the optimization of the preventive maintenance politic of nuclear power plant safety systems. The goal of this approach is to improve the average availability of the system through the optimization of the preventive maintenance scheduling politic. The auxiliary feed water system of a two loops pressurized water reactor is used as a sample case, in order to demonstrate the effectiveness of the proposed method. The results, when compared to those obtained by some standard maintenance politics, reveal quantitative gains and operational safety levels. (author)

  19. Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

    Directory of Open Access Journals (Sweden)

    Naoufal Rouky

    2019-01-01

    Full Text Available This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP, where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO meta-heuristic hybridized with a Variable Neighborhood Descent (VND local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

  20. TRADING-OFF CONSTRAINTS IN THE PUMP SCHEDULING OPTIMIZATION OF WATER DISTRIBUTION NETWORKS

    Directory of Open Access Journals (Sweden)

    Gencer Genço\\u011Flu

    2016-01-01

    Full Text Available Pumps are one of the essential components of water supply systems. Depending of the topography, a water supply system may completely rely on pumping. They may consume non-negligible amount of water authorities' budgets during operation. Besides their energy costs, maintaining the healthiness of pumping systems is another concern for authorities. This study represents a multi-objective optimization method for pump scheduling problem. The optimization objective contains hydraulic and operational constraints. Switching of pumps and usage of electricity tariff are assumed to be key factors for operational reliability and energy consumption and costs of pumping systems. The local optimals for systems operational reliability, energy consumptions and energy costs are investigated resulting from trading-off pump switch and electricity tariff constraints within given set of boundary conditions. In the study, a custom made program is employed that combines genetic algorithm based optimization module with hydraulic network simulation software -EPANET. Developed method is applied on the case study network; N8-3 pressure zone of the Northern Supply of Ankara (Turkey Water Distribution Network. This work offers an efficient method for water authorities aiming to optimize pumping schedules considering expenditures and operational reliability mutually.

  1. Optimal scheduling of biocide dosing for seawater-cooled power and desalination plants

    KAUST Repository

    Mahfouz, Abdullah Bin

    2011-02-13

    Thermal desalination systems are typically integrated with power plants to exploit the excess heat resulting from the power-generation units. Using seawater in cooling the power plant and the desalination system is a common practice in many parts of the world where there is a shortage of freshwater. Biofouling is one of the major problems associated with the usage of seawater in cooling systems. Because of the dynamic variation in the power and water demands as well as the changes in the characteristics of seawater and the process, there is a need to develop an optimal policy for scheduling biocide usage and cleaning maintenance of the heat exchangers. The objective of this article is to introduce a systematic procedure for the optimization of scheduling the dosing of biocide and dechlorination chemicals as well as cleaning maintenance for a power production/thermal desalination plant. A multi-period optimization formulation is developed and solved to determine: the optimal levels of dosing and dechlorination chemicals; the timing of maintenance to clean the heat-exchange surfaces; and the dynamic dependence of the biofilm growth on the applied doses, the seawater-biocide chemistry, the process conditions, and seawater characteristics for each time period. The technical, economic, and environmental considerations of the system are accounted for. A case study is solved to elucidate the applicability of the developed optimization approach. © 2011 Springer-Verlag.

  2. Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

    Science.gov (United States)

    Abdullahi, Mohammed; Ngadi, Md Asri

    2016-01-01

    Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

  3. Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

    Directory of Open Access Journals (Sweden)

    Mohammed Abdullahi

    Full Text Available Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS has been shown to perform competitively with Particle Swarm Optimization (PSO. The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA based SOS (SASOS in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

  4. Design of synchromesh mechanism to optimization manual transmission's electric vehicle

    Science.gov (United States)

    Zainuri, Fuad; Sumarsono, Danardono A.; Adhitya, Muhammad; Siregar, Rolan

    2017-03-01

    Significant research has been attempted on a vehicle that lead to the development of transmission that can reduce energy consumption and improve vehicle efficiency. Consumers also expect safety, convenience, and competitive prices. Automatic transmission (AT), continuously variable transmission (CVT), and dual clutch transmission (DCT) is the latest transmission developed for road vehicle. From literature reviews that have been done that this transmission is less effective on electric cars which use batteries as a power source compared to type manual transmission, this is due to the large power losses when making gear changes. Zeroshift system is the transmission can do shift gears with no time (zero time). It was developed for the automatic manual transmission, and this transmission has been used on racing vehicles to eliminate deceleration when gear shift. Zeroshift transmission still use the clutch to change gear in which electromechanical be used to replace the clutch pedal. Therefore, the transmission is too complex for the transmission of electric vehicles, but its mechanism is considered very suitable to increase the transmission efficiency. From this idea, a new innovation design transmission will be created to electric car. The combination synchromesh with zeroshift mechanism for the manual transmission is a transmission that is ideal for improving the transmission efficiency. Installation synchromesh on zeroshift mechanism is expected to replace the function of the clutch MT, and assisted with the motor torque setting when to change gear. Additionally to consider is the weight of the transmission, ease of manufacturing, ease of installation with an electric motor, as well as ease of use by drivers is a matter that must be done to obtain a new transmission system that is suitable for electric cars.

  5. Proposed algorithm to improve job shop production scheduling using ant colony optimization method

    Science.gov (United States)

    Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

    2017-12-01

    This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

  6. Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional value-at-risk framework

    International Nuclear Information System (INIS)

    Hemmati, Reza; Saboori, Hedayat; Saboori, Saeid

    2016-01-01

    In recent decades, wind power resources have been integrated in the power systems increasingly. Besides confirmed benefits, utilization of large share of this volatile source in power generation portfolio has been faced system operators with new challenges in terms of uncertainty management. It is proved that energy storage systems are capable to handle projected uncertainty concerns. Risk-neutral methods have been proposed in the previous literature to schedule storage units considering wind resources uncertainty. Ignoring risk of the cost distributions with non-desirable properties may result in experiencing high costs in some unfavorable scenarios with high probability. In order to control the risk of the operator decisions, this paper proposes a new risk-constrained two-stage stochastic programming model to make optimal decisions on energy storage and thermal units in a transmission constrained hybrid wind-thermal power system. Risk-aversion procedure is explicitly formulated using the conditional value-at-risk measure, because of possessing distinguished features compared to the other risk measures. The proposed model is a mixed integer linear programming considering transmission network, thermal unit dynamics, and storage devices constraints. The simulations results demonstrate that taking the risk of the problem into account will affect scheduling decisions considerably depend on the level of the risk-aversion. - Highlights: • Risk of the operation decisions is handled by using risk-averse programming. • Conditional value-at-risk is used as risk measure. • Optimal risk level is obtained based on the cost/benefit analysis. • The proposed model is a two-stage stochastic mixed integer linear programming. • The unit commitment is integrated with ESSs and wind power penetration.

  7. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

    Directory of Open Access Journals (Sweden)

    Nadeem Javaid

    2017-10-01

    Full Text Available In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP technique and two heuristic optimization techniques: genetic algorithm (GA and binary particle swarm optimization (BPSO for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

  8. Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2017-01-01

    Full Text Available As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

  9. Optimal distribution of reactivity excess in a system of reactors operating at a variable loading schedule

    International Nuclear Information System (INIS)

    Bolsunov, A.A.; Zagrebaev, A.M.; Naumov, V.I.

    1979-01-01

    Considered is the task of reactivity excess distribution optimization in the system of reactors for the purpose of minimazing the summary power production losses at the fixed loading schedule. Mathematical formulation of the task is presented. Given are the curves, characterizing the dependence of possible degree of the reactor power drop on reactivity excees for non-stationary Xe poisoning at different nominal density of neutron flux. Analyzing the results, it is concluded that in case, when the reactors differ only in neutron flux density the reactor with lower neutron flux density should be involved in the variable operation schedule first as the poisoning of this reactor will be less, and therefore, the losses of the system power production will be less. It is advisable to reserve the reactivity excess in the reactor with greater power or in the reactor with higher burnup rate. It is stressed that the obtained results of the optimization task solution point out the possibility of obtaining the certain ecomonic effect and permit to correct the requirements on mobility of separate power units at system approach to NPP operation in a variable loading schedule

  10. Performance deterioration modeling and optimal preventive maintenance strategy under scheduled servicing subject to mission time

    Directory of Open Access Journals (Sweden)

    Li Dawei

    2014-08-01

    Full Text Available Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintenance strategy. In order to model the maintenance effect of servicing, this study analyzes the deterioration characteristics of system under scheduled servicing. And then the deterioration model is established from the failure mechanism by compound Poisson process. On the basis of the system damage value and failure mechanism, the failure rate refresh factor is proposed to describe the maintenance effect of servicing. A maintenance strategy is developed which combines the benefits of scheduled servicing and preventive maintenance. Then the optimization model is given to determine the optimal servicing period and preventive maintenance time, with an objective to minimize the system expected life-cycle cost per unit time and a constraint on system survival probability for the duration of mission time. Subject to mission time, it can control the ability of accomplishing the mission at any time so as to ensure the high dependability. An example of water pump rotor relating to scheduled servicing is introduced to illustrate the failure rate refresh factor and the proposed maintenance strategy. Compared with traditional methods, the numerical results show that the failure rate refresh factor can describe the maintenance effect of servicing more intuitively and objectively. It also demonstrates that this maintenance strategy can prolong the lifetime, reduce the total lifetime maintenance cost and guarantee the dependability of system.

  11. Optimal Cluster Mill Pass Scheduling With an Accurate and Rapid New Strip Crown Model

    International Nuclear Information System (INIS)

    Malik, Arif S.; Grandhi, Ramana V.; Zipf, Mark E.

    2007-01-01

    Besides the requirement to roll coiled sheet at high levels of productivity, the optimal pass scheduling of cluster-type reversing cold mills presents the added challenge of assigning mill parameters that facilitate the best possible strip flatness. The pressures of intense global competition, and the requirements for increasingly thinner, higher quality specialty sheet products that are more difficult to roll, continue to force metal producers to commission innovative flatness-control technologies. This means that during the on-line computerized set-up of rolling mills, the mathematical model should not only determine the minimum total number of passes and maximum rolling speed, it should simultaneously optimize the pass-schedule so that desired flatness is assured, either by manual or automated means. In many cases today, however, on-line prediction of strip crown and corresponding flatness for the complex cluster-type rolling mills is typically addressed either by trial and error, by approximate deflection models for equivalent vertical roll-stacks, or by non-physical pattern recognition style models. The abundance of the aforementioned methods is largely due to the complexity of cluster-type mill configurations and the lack of deflection models with sufficient accuracy and speed for on-line use. Without adequate assignment of the pass-schedule set-up parameters, it may be difficult or impossible to achieve the required strip flatness. In this paper, we demonstrate optimization of cluster mill pass-schedules using a new accurate and rapid strip crown model. This pass-schedule optimization includes computations of the predicted strip thickness profile to validate mathematical constraints. In contrast to many of the existing methods for on-line prediction of strip crown and flatness on cluster mills, the demonstrated method requires minimal prior tuning and no extensive training with collected mill data. To rapidly and accurately solve the multi-contact problem

  12. Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

    Directory of Open Access Journals (Sweden)

    Monika

    2016-01-01

    Full Text Available Cloud computing is an incipient innovation which broadly spreads among analysts. It furnishes clients with foundation, stage and programming as enhancement which is easily available by means of web. A cloud is a sort of parallel and conveyed framework comprising of a gathering of virtualized PCs that are utilized to execute various tasks to accomplish good execution time, accomplish due date and usage of its assets. The scheduling issue can be seen as the finding an ideal task of assignments over the accessible arrangement of assets with the goal that we can accomplish the wanted objectives for tasks. This paper presents an optimal algorithm for scheduling tasks to get their waiting time as a QoS parameter. The algorithm is simulated using Cloudsim simulator and experiments are carried out to help clients to make sense of the bottleneck of utilizing no. of virtual machine parallely.

  13. MDTM: Optimizing Data Transfer using Multicore-Aware I/O Scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Liang [Fermilab; Demar, Phil [Fermilab; Wu, Wenji [Fermilab; Kim, Bockjoo [Florida U.

    2017-05-09

    Bulk data transfer is facing significant challenges in the coming era of big data. There are multiple performance bottlenecks along the end-to-end path from the source to destination storage system. The limitations of current generation data transfer tools themselves can have a significant impact on end-to-end data transfer rates. In this paper, we identify the issues that lead to underperformance of these tools, and present a new data transfer tool with an innovative I/O scheduler called MDTM. The MDTM scheduler exploits underlying multicore layouts to optimize throughput by reducing delay and contention for I/O reading and writing operations. With our evaluations, we show how MDTM successfully avoids NUMA-based congestion and significantly improves end-to-end data transfer rates across high-speed wide area networks.

  14. MDTM: Optimizing Data Transfer using Multicore-Aware I/O Scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Liang [Fermilab; Demar, Phil [Fermilab; Wu, Wenji [Fermilab; Kim, Bockjoo [Florida U.

    2017-01-01

    Bulk data transfer is facing significant challenges in the coming era of big data. There are multiple performance bottlenecks along the end-to-end path from the source to destination storage system. The limitations of current generation data transfer tools themselves can have a significant impact on end-to-end data transfer rates. In this paper, we identify the issues that lead to underperformance of these tools, and present a new data transfer tool with an innovative I/O scheduler called MDTM. The MDTM scheduler exploits underlying multicore layouts to optimize throughput by reducing delay and contention for I/O reading and writing operations. With our evaluations, we show how MDTM successfully avoids NUMA-based congestion and significantly improves end-to-end data transfer rates across high-speed wide area networks.

  15. Solving Multi-Resource Constrained Project Scheduling Problem using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Hsiang-Hsi Huang

    2015-01-01

    Full Text Available This paper applied Ant Colony Optimization (ACO to develop a resource constraints scheduling model to achieve the resource allocation optimization and the shortest completion time of a project under resource constraints and the activities precedence requirement for projects. Resource leveling is also discussed and has to be achieved under the resource allocation optimization in this research. Testing cases and examples adopted from the international test bank were studied for verifying the effectiveness of the proposed model. The results showed that the solutions of different cases all have a better performance within a reasonable time. These can be obtained through ACO algorithm under the same constrained conditions. A program was written for the proposed model that is able to automatically produce the project resource requirement figure after the project duration is solved.

  16. Metroplex Optimization Model Expansion and Analysis: The Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM)

    Science.gov (United States)

    Sherry, Lance; Ferguson, John; Hoffman, Karla; Donohue, George; Beradino, Frank

    2012-01-01

    This report describes the Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM) that is designed to provide insights into airline decision-making with regards to markets served, schedule of flights on these markets, the type of aircraft assigned to each scheduled flight, load factors, airfares, and airline profits. The main inputs to the model are hedged fuel prices, airport capacity limits, and candidate markets. Embedded in the model are aircraft performance and associated cost factors, and willingness-to-pay (i.e. demand vs. airfare curves). Case studies demonstrate the application of the model for analysis of the effects of increased capacity and changes in operating costs (e.g. fuel prices). Although there are differences between airports (due to differences in the magnitude of travel demand and sensitivity to airfare), the system is more sensitive to changes in fuel prices than capacity. Further, the benefits of modernization in the form of increased capacity could be undermined by increases in hedged fuel prices

  17. Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T

    2011-08-04

    The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.

  18. Optimization of Beam Transmission of PAL-PNF Electron Linac

    Energy Technology Data Exchange (ETDEWEB)

    Shin, S. G.; Kim, S. K.; Kim, E. A. [Pohang University of Science and Technology, Pohang (Korea, Republic of)

    2012-05-15

    The PNF (Pohang Neutron Facility) electron Linac is providing converted neutrons and photons from electron beams to users for nuclear physics experiments and high energy gamma-ray exposures. This linac is capable of producing 100 MeV electron beams with a beam current of pulsed 100 mA. The pulse length is 2 {mu}s and the pulse repetition rate is typically 30 Hz. This linac consists of two SLAC-type S-band accelerating columns and the thermionic RF gun. They are powered by one klystron and the matching pulse modulator. The electron beams emitted from the RF gun are bunched as they pass through the alpha magnet and are injected into the accelerating column thereafter. In this paper, we discuss procedures and results of the beam transmission optimization with technical details of the accelerator system. We also briefly discuss the future upgrade plan to obtain short-pulse or electron beams for neutron TOF experiments by adopting a triode type thermionic DC electron gun

  19. Improved teaching-learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

    Science.gov (United States)

    Buddala, Raviteja; Mahapatra, Siba Sankar

    2017-11-01

    Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

  20. A Review On Job Shop Scheduling Using Non-Conventional Optimization Algorithm

    OpenAIRE

    K.Mallikarjuna; Venkatesh.G

    2014-01-01

    A great deal of research has been focused on solving job shop scheduling problem (∫J), over the last four decades, resulting in a wide variety of approaches. Recently much effort has been concentrated on hybrid methods to solve ∫J, as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that lead to combinatorial optimization methods and a meta-strategy which guides the search out of local optima. In this p...

  1. Optimal scheduling for electric heat booster under day-ahead electricity and heat pricing

    DEFF Research Database (Denmark)

    Cai, Hanmin; You, Shi; Bindner, Henrik W.

    2017-01-01

    Multi-energy system (MES) operation calls for active management of flexible resources across energy sectors to improve efficiency and meet challenging environmental targets. Electric heat booster, a solution for Domestic Hot Water (DHW) preparation under Low-Temperature-District-Heating (LTDH......) context, is identified as one of aforementioned flexible resources for electricity and heat sectors. This paper extends the concept of optimal load scheduling under day-ahead pricing from electricity sector only to both electricity and heat sectors. A case study constructing day-ahead energy prices...

  2. Optimal Scheduling of Railway Track Possessions in Large-Scale Projects with Multiple Construction Works

    DEFF Research Database (Denmark)

    Li, Rui; Roberti, Roberto

    2017-01-01

    satisfying different operational constraints and minimizing the total construction cost. To find an optimal solution of the RTPSP, this paper proposes an approach that, first, transfers the nominal market prices into track-possession-based real prices, and then generates a schedule of the construction works...... by solving a mixed-integer linear-programming model for the given track blocking proposal. The proposed approach is tested on a real-life case study from the Danish railway infrastructure manager. The results show that, in 2 h of computing time, the approach is able to provide solutions that are within 0...

  3. A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Mostafa Khorramizadeh

    2015-01-01

    Full Text Available The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems.

  4. Optimal Research and Numerical Simulation for Scheduling No-Wait Flow Shop in Steel Production

    Directory of Open Access Journals (Sweden)

    Huawei Yuan

    2013-01-01

    Full Text Available This paper considers the m-machine flow shop scheduling problem with the no-wait constraint to minimize total completion time which is the typical model in steel production. First, the asymptotic optimality of the Shortest Processing Time (SPT first rule is proven for this problem. To further evaluate the performance of the algorithm, a new lower bound with performance guarantee is designed. At the end of the paper, numerical simulations show the effectiveness of the proposed algorithm and lower bound.

  5. Optimized Scheduling of Smart Meter Data Access for Real-time Voltage Quality Monitoring

    DEFF Research Database (Denmark)

    Kemal, Mohammed Seifu; Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter

    2018-01-01

    Abstract—Active low-voltage distribution grids that support high integration of distributed generation such as photovoltaics and wind turbines require real-time voltage monitoring. At the same time, countries in Europe such as Denmark have close to 100% rollout of smart metering infrastructure....... The metering infrastructure has limitations to provide real-time measurements with small-time granularity. This paper presents an algorithm for optimized scheduling of smart meter data access to provide real-time voltage quality monitoring. The algorithm is analyzed using a real distribution grid in Denmark...

  6. Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qihua Wang

    2017-11-01

    Full Text Available Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

  7. An Optimal Domestic Electric Vehicle Charging Strategy for Reducing Network Transmission Loss While Taking Seasonal Factors into Consideration

    Directory of Open Access Journals (Sweden)

    Yuancheng Zhao

    2018-01-01

    Full Text Available With the rapid growth of domestic electric vehicle charging loads, the peak-valley gap and power fluctuation rate of power systems increase sharply, which can lead to the increase of network losses and energy efficiency reduction. This paper tries to regulate network loads and reduce power system transmission loss by optimizing domestic electric vehicle charging loads. In this paper, a domestic electric vehicle charging loads model is first developed by analyzing the key factors that can affect users’ charging behavior. Subsequently, the Monte Carlo method is proposed to simulate the power consumption of a cluster of domestic electric vehicles. After that, an optimal electric vehicle charging strategy based on the 0-1 integer programming is presented to regulate network daily loads. Finally, by taking the IEEE33 distributed power system as an example, this paper tries to verify the efficacy of the proposed optimal charging strategy and the necessity for considering seasonal factors when scheduling electric vehicle charging loads. Simulation results show that the proposed 0-1 integer programming method does have good performance in reducing the network peak-valley gap, voltage fluctuation rate, and transmission loss. Moreover, it has some potential to further reduce power system transmission loss when seasonal factors are considered.

  8. Adaptive track scheduling to optimize concurrency and vectorization in GeantV

    International Nuclear Information System (INIS)

    Apostolakis, J; Brun, R; Carminati, F; Gheata, A; Novak, M; Wenzel, S; Bandieramonte, M; Bitzes, G; Canal, P; Elvira, V D; Jun, S Y; Lima, G; Licht, J C De Fine; Duhem, L; Sehgal, R; Shadura, O

    2015-01-01

    The GeantV project is focused on the R and D of new particle transport techniques to maximize parallelism on multiple levels, profiting from the use of both SIMD instructions and co-processors for the CPU-intensive calculations specific to this type of applications. In our approach, vectors of tracks belonging to multiple events and matching different locality criteria must be gathered and dispatched to algorithms having vector signatures. While the transport propagates tracks and changes their individual states, data locality becomes harder to maintain. The scheduling policy has to be changed to maintain efficient vectors while keeping an optimal level of concurrency. The model has complex dynamics requiring tuning the thresholds to switch between the normal regime and special modes, i.e. prioritizing events to allow flushing memory, adding new events in the transport pipeline to boost locality, dynamically adjusting the particle vector size or switching between vector to single track mode when vectorization causes only overhead. This work requires a comprehensive study for optimizing these parameters to make the behaviour of the scheduler self-adapting, presenting here its initial results. (paper)

  9. Fuzzy linear programming based optimal fuel scheduling incorporating blending/transloading facilities

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Babic, B.; Milosevic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [EPRI, Palo Alto, CA (United States). Power System Control; Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

    1996-05-01

    In this paper the blending/transloading facilities are modeled using an interactive fuzzy linear programming (FLP), in order to allow the decision-maker to solve the problem of uncertainty of input information within the fuel scheduling optimization. An interactive decision-making process is formulated in which decision-maker can learn to recognize good solutions by considering all possibilities of fuzziness. The application of the fuzzy formulation is accompanied by a careful examination of the definition of fuzziness, appropriateness of the membership function and interpretation of results. The proposed concept provides a decision support system with integration-oriented features, whereby the decision-maker can learn to recognize the relative importance of factors in the specific domain of optimal fuel scheduling (OFS) problem. The formulation of a fuzzy linear programming problem to obtain a reasonable nonfuzzy solution under consideration of the ambiguity of parameters, represented by fuzzy numbers, is introduced. An additional advantage of the FLP formulation is its ability to deal with multi-objective problems.

  10. Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer

    KAUST Repository

    Lima, Ricardo

    2015-01-01

    This work address the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two-stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. Two variants of a constraint generation algorithm are proposed, namely a primal and dual version, and they are used to solve two case studies based on two different producers. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

  11. Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer

    KAUST Repository

    Lima, Ricardo

    2015-01-07

    This work address the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two-stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. Two variants of a constraint generation algorithm are proposed, namely a primal and dual version, and they are used to solve two case studies based on two different producers. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

  12. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

    Science.gov (United States)

    Li, Xuejun; Xu, Jia; Yang, Yun

    2015-01-01

    Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

  13. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

    Directory of Open Access Journals (Sweden)

    Xuejun Li

    2015-01-01

    Full Text Available Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

  14. An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

    Science.gov (United States)

    Rash, James

    2014-01-01

    NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial-optimization

  15. Optimizing Air Transportation Service to Metroplex Airports. Par 2; Analysis Using the Airline Schedule Optimization Model (ASOM)

    Science.gov (United States)

    Donoue, George; Hoffman, Karla; Sherry, Lance; Ferguson, John; Kara, Abdul Qadar

    2010-01-01

    The air transportation system is a significant driver of the U.S. economy, providing safe, affordable, and rapid transportation. During the past three decades airspace and airport capacity has not grown in step with demand for air transportation; the failure to increase capacity at the same rate as the growth in demand results in unreliable service and systemic delay. This report describes the results of an analysis of airline strategic decision-making that affects geographic access, economic access, and airline finances, extending the analysis of these factors using historic data (from Part 1 of the report). The Airline Schedule Optimization Model (ASOM) was used to evaluate how exogenous factors (passenger demand, airline operating costs, and airport capacity limits) affect geographic access (markets-served, scheduled flights, aircraft size), economic access (airfares), airline finances (profit), and air transportation efficiency (aircraft size). This analysis captures the impact of the implementation of airport capacity limits, as well as the effect of increased hedged fuel prices, which serve as a proxy for increased costs per flight that might occur if auctions or congestion pricing are imposed; also incorporated are demand elasticity curves based on historical data that provide information about how passenger demand is affected by airfare changes.

  16. Speed Synchronization Control of Integrated Motor–Transmission Powertrain over CAN through Active Period-Scheduling Approach

    Directory of Open Access Journals (Sweden)

    Wanke Cao

    2017-11-01

    Full Text Available This paper deals with the speed synchronization control of integrated motor–transmission (IMT powertrain systems in pure electric vehicles (EVs over a controller area network (CAN subject to both network-induced delays and network congestion. A CAN has advantages over point-to-point communication; however, it imposes network-induced delays and network congestion into the control system, which can deteriorate the shifting quality and make system integration difficult. This paper presents a co-design scheme combining active period scheduling and discrete-time slip mode control (SMC to deal with both network-induced delays and network congestion of the CAN, which improves the speed synchronization control for high shifting quality and prevents network congestion for the system’s integration. The results of simulations and hardware-in-loop experiments show the effectiveness of the proposed scheme, which can ensure satisfactory speed synchronization performance while significantly reducing the network’s utilization.

  17. Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

    Directory of Open Access Journals (Sweden)

    Kang Miao Tan

    2017-11-01

    Full Text Available The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA. The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

  18. Modeling and optimization of tissue 10B concentration and dosimetry for arbitrary BPA-F infusion schedules in humans

    International Nuclear Information System (INIS)

    Kiger, W.S. III; Newton, T.H.; Palmer, M.R.

    2000-01-01

    Separate compartmental models have been derived for the concentration of 10 B resulting from BPA-F infusion in the central vascular space (i.e., blood or, more appropriately, plasma) and in glioblastoma multiforme and normal brain. By coupling the model for the temporal variation of 10 B concentration in the central vascular space with that for tissue, the dynamic behavior of the 10 B concentration and the resulting dosimetry in the relevant tissues and blood may be predicted for arbitrary infusion schedules. This coupled model may be used as a tool for identifying the optimal time for BNCT irradiation and optimal BPA-F infusion schedule (i.e., temporal targeting) in humans without the need for expensive and time-consuming pharmacokinetic studies for every infusion schedule considered. This model was used to analyze the concentration profiles resulting from a wide range of infusion schedules and their implications for dosimetry. (author)

  19. Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis.

    Science.gov (United States)

    Hemmati, Reza; Saboori, Hedayat

    2016-05-01

    Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics.

  20. Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis

    Science.gov (United States)

    Hemmati, Reza; Saboori, Hedayat

    2016-01-01

    Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics. PMID:27222741

  1. A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

    DEFF Research Database (Denmark)

    Ding, Tao; Li, Cheng; Huang, Can

    2018-01-01

    –slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....

  2. Optimal scheduling for enhanced coal bed methane production through CO2 injection

    International Nuclear Information System (INIS)

    Huang, Yuping; Zheng, Qipeng P.; Fan, Neng; Aminian, Kashy

    2014-01-01

    Highlights: • A novel deterministic optimization model for CO 2 -ECBM production scheduling. • Maximize the total profit from both sales of natural gas and CO 2 credits trading in the carbon market. • A stochastic model incorporating uncertainties and dynamics of NG price and CO 2 credit. - Abstract: Enhanced coal bed methane production with CO 2 injection (CO 2 -ECBM) is an effective technology for accessing the natural gas embedded in the traditionally unmineable coal seams. The revenue via this production process is generated not only by the sales of coal bed methane, but also by trading CO 2 credits in the carbon market. As the technology of CO 2 -ECBM becomes mature, its commercialization opportunities are also springing up. This paper proposes applicable mathematical models for CO 2 -ECBM production and compares the impacts of their production schedules on the total profit. A novel basic deterministic model for CO 2 -ECBM production including the technical and chemical details is proposed and then a multistage stochastic programming model is formulated in order to address uncertainties of natural gas price and CO 2 credit. Both models are nonlinear programming problems, which are solved by commercial nonlinear programming software BARON via GAMS. Numerical experiments show the benefits (e.g., expected profit gain) of using stochastic models versus deterministic models

  3. Optimal short-term operation schedule of a hydropower plant in a competitive electricity market

    International Nuclear Information System (INIS)

    Perez-Diaz, Juan I.; Wilhelmi, Jose R.; Arevalo, Luis A.

    2010-01-01

    This paper presents a dynamic programming model to solve the short-term scheduling problem of a hydropower plant that sells energy in a pool-based electricity market with the objective of maximizing the revenue. This is a nonlinear and non-concave problem subject to strong technical and strategic constraints, and in which discrete and continuous variables take part. The model described in this paper determines, in each hour of the planning horizon (typically from one day to one week), both the optimal number of units in operation (unit commitment) and the power to be generated by the committed units (generation dispatch). The power generated by each unit is considered as a nonlinear function of the actual water discharge and volume of the associated reservoir. The dependence of the units' efficiency and operating limits with the available gross head is also accounted for in this model. The application of this model to a real hydropower plant demonstrates its capabilities in providing the operation schedule that maximizes the revenue of the hydro plant while satisfying several constraints of different classes. In addition, the use of this model as a supporting tool to estimate the economic feasibility of a hydropower plant development project is also analyzed in the paper. (author)

  4. Joint optimization of green vehicle scheduling and routing problem with time-varying speeds

    Science.gov (United States)

    Zhang, Dezhi; Wang, Xin; Ni, Nan; Zhang, Zhuo

    2018-01-01

    Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions. PMID:29466370

  5. Schedule Optimization of Imaging Missions for Multiple Satellites and Ground Stations Using Genetic Algorithm

    Science.gov (United States)

    Lee, Junghyun; Kim, Heewon; Chung, Hyun; Kim, Haedong; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee

    2018-04-01

    In this paper, we propose a method that uses a genetic algorithm for the dynamic schedule optimization of imaging missions for multiple satellites and ground systems. In particular, the visibility conflicts of communication and mission operation using satellite resources (electric power and onboard memory) are integrated in sequence. Resource consumption and restoration are considered in the optimization process. Image acquisition is an essential part of satellite missions and is performed via a series of subtasks such as command uplink, image capturing, image storing, and image downlink. An objective function for optimization is designed to maximize the usability by considering the following components: user-assigned priority, resource consumption, and image-acquisition time. For the simulation, a series of hypothetical imaging missions are allocated to a multi-satellite control system comprising five satellites and three ground stations having S- and X-band antennas. To demonstrate the performance of the proposed method, simulations are performed via three operation modes: general, commercial, and tactical.

  6. Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

    Directory of Open Access Journals (Sweden)

    Jianfei Ye

    2015-01-01

    Full Text Available In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

  7. Comparison of two dose and three dose human papillomavirus vaccine schedules: cost effectiveness analysis based on transmission model.

    Science.gov (United States)

    Jit, Mark; Brisson, Marc; Laprise, Jean-François; Choi, Yoon Hong

    2015-01-06

    To investigate the incremental cost effectiveness of two dose human papillomavirus vaccination and of additionally giving a third dose. Cost effectiveness study based on a transmission dynamic model of human papillomavirus vaccination. Two dose schedules for bivalent or quadrivalent human papillomavirus vaccines were assumed to provide 10, 20, or 30 years' vaccine type protection and cross protection or lifelong vaccine type protection without cross protection. Three dose schedules were assumed to give lifelong vaccine type and cross protection. United Kingdom. Males and females aged 12-74 years. No, two, or three doses of human papillomavirus vaccine given routinely to 12 year old girls, with an initial catch-up campaign to 18 years. Costs (from the healthcare provider's perspective), health related utilities, and incremental cost effectiveness ratios. Giving at least two doses of vaccine seems to be highly cost effective across the entire range of scenarios considered at the quadrivalent vaccine list price of £86.50 (€109.23; $136.00) per dose. If two doses give only 10 years' protection but adding a third dose extends this to lifetime protection, then the third dose also seems to be cost effective at £86.50 per dose (median incremental cost effectiveness ratio £17,000, interquartile range £11,700-£25,800). If two doses protect for more than 20 years, then the third dose will have to be priced substantially lower (median threshold price £31, interquartile range £28-£35) to be cost effective. Results are similar for a bivalent vaccine priced at £80.50 per dose and when the same scenarios are explored by parameterising a Canadian model (HPV-ADVISE) with economic data from the United Kingdom. Two dose human papillomavirus vaccine schedules are likely to be the most cost effective option provided protection lasts for at least 20 years. As the precise duration of two dose schedules may not be known for decades, cohorts given two doses should be closely

  8. Optimal Scheduling of Biogas-Solar-Wind Renewable Portfolio for Multi-Carrier Energy Supplies

    DEFF Research Database (Denmark)

    Zhou, Bin; Xu, Da; Li, Canbing

    2018-01-01

    the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient con-version and storage, while different energy demands of end......This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic...... interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate...

  9. Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

    Science.gov (United States)

    Li, Jun-qing; Pan, Quan-ke; Mao, Kun

    2014-01-01

    A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

  10. Short-term hydro-thermal scheduling using particle swarm optimization method

    International Nuclear Information System (INIS)

    Yu, Binghui; Yuan, Xiaohui; Wang, Jinwen

    2007-01-01

    The approaches based on different particle swarm optimization (PSO) techniques are applied to solve the short-term hydro-thermal scheduling problem. In the proposed methods, many constraints of the hydro-thermal system, such as power balance, water balance, reservoir volume limits and the operation limits of hydro and thermal plants, are considered. The feasibility of the proposed algorithm is demonstrated through an example system, and the results are compared with the results of a genetic algorithm and evolutionary programming approaches. The experimental results show that all the PSO algorithms have the ability to achieve nearly global solutions, but a local version of PSO with inertia weight appears to be the best amongst all the PSOs in terms of high quality solution

  11. Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

    Directory of Open Access Journals (Sweden)

    Jun-qing Li

    2014-01-01

    Full Text Available A hybrid algorithm which combines particle swarm optimization (PSO and iterated local search (ILS is proposed for solving the hybrid flowshop scheduling (HFS problem with preventive maintenance (PM activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

  12. Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.

    Science.gov (United States)

    Li, Jun-qing; Pan, Quan-ke; Mao, Kun

    2014-01-01

    A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

  13. Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Weizhe Zhang

    2014-01-01

    Full Text Available Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

  14. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    DEFF Research Database (Denmark)

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day...

  15. Optimizing link efficiency for gated DPCCH transmission on HSUPA

    DEFF Research Database (Denmark)

    Zarco, Carlos Ruben Delgado; Wigard, Jeroen; Kolding, T. E.

    2007-01-01

    consider the E-DCH performance degradation caused by gating on other radio procedures relying on the DPCCH, such as inner and outer loop power control. Our studies show that gating is beneficial for both for 2 and 10 ms transmission time intervals. The gains in terms of LE with a Vehicular A 30 kmph......To minimize the terminal's transmission power in bursty uplink traffic conditions, the evolved High-Speed Uplink Packet Access (HSUPA) concept in 3GPP WCDMA includes a feature known as Dedicated Physical Control Channel (DPCCH) gating. We present here a detailed link level study of gating from...... a link efficiency (LE) perspective; LE being expressed in bits per second per Watt. While the overall gain mechanisms of gating are well known, we show how special challenges related to discontinuous Enhanced Dedicated Channel (E-DCH) transmission can be addressed for high link and system performance. We...

  16. Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach

    International Nuclear Information System (INIS)

    Lee, T.-Y.

    2008-01-01

    This paper uses multi-pass iteration particle swarm optimization (MIPSO) to solve short term hydroelectric generation scheduling of a power system with wind turbine generators. MIPSO is a new algorithm for solving nonlinear optimal scheduling problems. A new index called iteration best (IB) is incorporated into particle swarm optimization (PSO) to improve solution quality. The concept of multi-pass dynamic programming is applied to modify PSO further and improve computation efficiency. The feasible operational regions of the hydro units and pumped storage plants over the whole scheduling time range must be determined before applying MIPSO to the problem. Wind turbine power generation then shaves the power system load curves. Next, MIPSO calculates hydroelectric generation scheduling. It begins with a coarse time stage and searching space and refines the time interval between two time stages and the search spacing pass by pass (iteration). With the cooperation of agents called particles, the near optimal solution of the scheduling problem can be effectively reached. The effects of wind speed uncertainty were also considered in this paper. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms

  17. Optimizing Water Use and Hydropower Production in Operational Reservoir System Scheduling with RiverWare

    Science.gov (United States)

    Magee, T. M.; Zagona, E. A.

    2017-12-01

    Practical operational optimization of multipurpose reservoir systems is challenging for several reasons. Each purpose has its own constraints which may conflict with those of other purposes. While hydropower generation typically provides the bulk of the revenue, it is also among the lowest priority purposes. Each river system has important details that are specific to the location such as hydrology, reservoir storage capacity, physical limitations, bottlenecks, and the continuing evolution of operational policy. In addition, reservoir operations models include discrete, nonlinear, and nonconvex physical processes and if-then operating policies. Typically, the forecast horizon for scheduling needs to be extended far into the future to avoid near term (e.g., a few hours or a day) scheduling decisions that result in undesirable future states; this makes the computational effort much larger than may be expected. Put together, these challenges lead to large and customized mathematical optimization problems which must be solved efficiently to be of practical use. In addition, the solution process must be robust in an operational setting. We discuss a unique modeling approach in RiverWare that meets these challenges in an operational setting. The approach combines a Preemptive Linear Goal Programming optimization model to handle prioritized policies complimented by preprocessing and postprocessing with Rulebased Simulation to improve the solution with regard to nonlinearities, discrete issues, and if-then logic. An interactive policy language with a graphical user interface allows modelers to customize both the optimization and simulation based on the unique aspects of the policy for their system while the routine physical aspect of operations are modeled automatically. The modeler is aided by a set of compiled predefined functions and functions shared by other modelers. We illustrate the success of the approach with examples from daily use at the Tennessee Valley

  18. Multiple time-scale optimization scheduling for islanded microgrids including PV, wind turbine, diesel generator and batteries

    DEFF Research Database (Denmark)

    Xiao, Zhao xia; Nan, Jiakai; Guerrero, Josep M.

    2017-01-01

    A multiple time-scale optimization scheduling including day ahead and short time for an islanded microgrid is presented. In this paper, the microgrid under study includes photovoltaics (PV), wind turbine (WT), diesel generator (DG), batteries, and shiftable loads. The study considers the maximum...... efficiency operation area for the diesel engine and the cost of the battery charge/discharge cycle losses. The day-ahead generation scheduling takes into account the minimum operational cost and the maximum load satisfaction as the objective function. Short-term optimal dispatch is based on minimizing...

  19. Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks.

    Science.gov (United States)

    Chen, Xi; Xu, Yixuan; Liu, Anfeng

    2017-04-19

    High transmission reliability, energy efficiency, and long lifetime are pivotal issues for wireless body area networks (WBANs. However, these performance metrics are not independent of each other, making it hard to obtain overall improvements through optimizing one single aspect. Therefore, a Cross Layer Design Optimal (CLDO) scheme is proposed to simultaneously optimize transmission reliability, energy efficiency, and lifetime of WBANs from several layers. Firstly, due to the fact that the transmission power of nodes directly influences the reliability of links, the optimized transmission power of different nodes is deduced, which is able to maximize energy efficiency in theory under the premise that requirements on delay and jitter are fulfilled. Secondly, a relay decision algorithm is proposed to choose optimized relay nodes. Using this algorithm, nodes will choose relay nodes that ensure a balance of network energy consumption, provided that all nodes transmit with optimized transmission power and the same packet size. Thirdly, the energy consumption of nodes is still unbalanced even with optimized transmission power because of their different locations in the topology of the network. In addition, packet size also has an impact on final performance metrics. Therefore, a synthesized cross layer method for optimization is proposed. With this method, the transmission power of nodes with more residual energy will be enhanced while suitable packet size is determined for different links in the network, leading to further improvements in the WBAN system. Both our comprehensive theoretical analysis and experimental results indicate that the performance of our proposed scheme is better than reported in previous studies. Relative to the relay selection and power control game (RSPCG) scheme, the CLDO scheme can enhance transmission reliability by more than 44.6% and prolong the lifetime by as much as 33.2%.

  20. Optimal treatment interruptions control of TB transmission model

    Science.gov (United States)

    Nainggolan, Jonner; Suparwati, Titik; Kawuwung, Westy B.

    2018-03-01

    A tuberculosis model which incorporates treatment interruptions of infectives is established. Optimal control of individuals infected with active TB is given in the model. It is obtained that the control reproduction numbers is smaller than the reproduction number, this means treatment controls could optimize the decrease in the spread of active TB. For this model, controls on treatment of infection individuals to reduce the actively infected individual populations, by application the Pontryagins Maximum Principle for optimal control. The result further emphasized the importance of controlling disease relapse in reducing the number of actively infected and treatment interruptions individuals with tuberculosis.

  1. Improving management of patients with autism spectrum disorder having scheduled surgery: optimizing practice.

    Science.gov (United States)

    Thompson, Debbie Gearner; Tielsch-Goddard, Anna

    2014-01-01

    Surgical preparation for children with autism spectrum disorders can be a challenge to perioperative staff because of the unique individual needs and behaviors in this population. Most children with autism function best in predictable, routine environments, and being in the hospital and other health care settings can create a stressful situation. This prospective, descriptive, quality improvement project was conducted to optimize best practices for perioperative staff and better individualize the plan of care for the autistic child and his or her family. Forty-three patients with a diagnosis of autism or autistic spectrum disorder were seen over 6 months at a suburban pediatric hospital affiliated with a major urban pediatric hospital and had an upcoming scheduled surgery or procedure requiring anesthesia. Caregivers were interviewed before and after surgery to collect information to better help their child cope with their hospital visit. In an evaluation of project outcomes, data were tabulated and summarized and interview data were qualitatively coded for emerging themes to improve the perioperative process for the child. Findings showed that staff members were able to recognize potential and actual stressors and help identify individual needs of surgical patients with autism. The families were pleased and appreciative of the individual attention and focus on their child's special needs. Investigators also found increased staff interest in optimizing the surgical experience for autistic children. Copyright © 2014 National Association of Pediatric Nurse Practitioners. Published by Mosby, Inc. All rights reserved.

  2. Optimal Charging Schedule Planning and Economic Analysis for Electric Bus Charging Stations

    Directory of Open Access Journals (Sweden)

    Rong-Ceng Leou

    2017-04-01

    Full Text Available The battery capacity of electric buses (EB used for public transportation is greater than that of electric cars, and the charging power is also several times greater than that used in electric cars; this can result in high energy consumption and negatively impact power distribution networks. This paper proposes a framework to determine the optimal contracted power capacity and charging schedule of an EB charging station in such a way that energy costs can be reduced. A mathematical model of controlled charging, which includes the capacity and energy charges of the station, was developed to minimize costs. The constraints of the model include the charging characteristics of an EB and the operational guidelines of the bus company. A practical EB charging station was used to verify the proposed model. The financial viability of this EB charging station is also studied in this paper. The economic analysis model for this charging station considers investment and operational costs, and the operational revenue. Sensitivity analyses with respect to some key parameters are also performed in this paper. Based on actual operational routes and EB charging schemes, test results indicate that the EB charging station investment is feasible, and the planning model proposed can be used to determine optimal station power capacity and minimize energy costs.

  3. On the optimal scheduling of periodic tests and maintenance for reliable redundant components

    International Nuclear Information System (INIS)

    Courtois, Pierre-Jacques; Delsarte, Philippe

    2006-01-01

    Periodically, some m of the n redundant components of a dependable system may have to be taken out of service for inspection, testing or preventive maintenance. The system is then constrained to operate with lower (n-m) redundancy and thus with less reliability during these periods. However, more frequent periodic inspections decrease the probability that a component fail undetected in the time interval between successive inspections. An optimal time schedule of periodic preventive operations arises from these two conflicting factors, balancing the loss of redundancy during inspections against the reliability benefits of more frequent inspections. Considering no other factor than this decreased redundancy at inspection time, this paper demonstrates the existence of an optimal interval between inspections, which maximizes the mean time between system failures. By suitable transformations and variable identifications, an analytic closed form expression of the optimum is obtained for the general (m, n) case. The optimum is shown to be unique within the ranges of parameter values valid in practice; its expression is easy to evaluate and shown to be useful to analyze and understand the influence of these parameters. Inspections are assumed to be perfect, i.e. they cause no component failure by themselves and leave no failure undetected. In this sense, the optimum determines a lowest bound for the system failure rate that can be achieved by a system of n-redundant components, m of which require for inspection or maintenance recurrent periods of unavailability of length t. The model and its general closed form solution are believed to be new . Previous work had computed optimal values for an estimation of a time average of system unavailability, but by numerical procedures only and with different numerical approximations, other objectives and model assumptions (one component only inspected at a time), and taking into account failures caused by testing itself, repair and

  4. Magnetic shielding structure optimization design for wireless power transmission coil

    Science.gov (United States)

    Dai, Zhongyu; Wang, Junhua; Long, Mengjiao; Huang, Hong; Sun, Mingui

    2017-09-01

    In order to improve the performance of the wireless power transmission (WPT) system, a novel design scheme with magnetic shielding structure on the WPT coil is presented in this paper. This new type of shielding structure has great advantages on magnetic flux leakage reduction and magnetic field concentration. On the basis of theoretical calculation of coil magnetic flux linkage and characteristic analysis as well as practical application feasibility consideration, a complete magnetic shielding structure was designed and the whole design procedure was represented in detail. The simulation results show that the coil with the designed shielding structure has the maximum energy transmission efficiency. Compared with the traditional shielding structure, the weight of the new design is significantly decreased by about 41%. Finally, according to the designed shielding structure, the corresponding experiment platform is built to verify the correctness and superiority of the proposed scheme.

  5. Optimizing malarial epidemiological studies in areas of low transmission

    DEFF Research Database (Denmark)

    Amerasinghe, Priyanie H; Alifrangis, Michael; van der Hoek, Wim

    2005-01-01

    risk factor in this area was the location of houses relative to confirmed vector breeding sites. At the peak of the transmission season, the results pointed in the same direction, irrespective of the diagnostic method used. However, the importance of distance from the breeding site......Malaria risk factor studies have traditionally used microscopy readings of blood slides as the measure of malaria infection in humans, although alternatives are available. There is the need for an assessment of how the use of these alternative diagnostic approaches will influence the efficiency...... and significance of epidemiological studies. In an area of Sri Lanka with known risk factors for malaria, two cross-sectional surveys were done at the start and at the peak of transmission season. Microscopy was compared with enzyme-linked immunosorbent assays (ELISA) and polymerase chain reaction (PCR). The major...

  6. Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

    Science.gov (United States)

    Santosa, B.; Siswanto, N.; Fiqihesa

    2018-04-01

    This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

  7. Optimal deployment schedule of an active twist rotor for performance enhancement and vibration reduction in high-speed flights

    Directory of Open Access Journals (Sweden)

    Young H. YOU

    2017-08-01

    Full Text Available The best active twist schedules exploiting various waveform types are sought taking advantage of the global search algorithm for the reduction of hub vibration and/or power required of a rotor in high-speed conditions. The active twist schedules include two non-harmonic inputs formed based on segmented step functions as well as the simple harmonic waveform input. An advanced Particle Swarm assisted Genetic Algorithm (PSGA is employed for the optimizer. A rotorcraft Computational Structural Dynamics (CSD code CAMRAD II is used to perform the rotor aeromechanics analysis. A Computation Fluid Dynamics (CFD code is coupled with CSD for verification and some physical insights. The PSGA optimization results are verified against the parameter sweep study performed using the harmonic actuation. The optimum twist schedules according to the performance and/or vibration reduction strategy are obtained and their optimization gains are compared between the actuation cases. A two-phase non-harmonic actuation schedule demonstrates the best outcome in decreasing the power required while a four-phase non-harmonic schedule results in the best vibration reduction as well as the simultaneous reductions in the power required and vibration. The mechanism of reduction to the performance gains is identified illustrating the section airloads, angle-of-attack distribution, and elastic twist deformation predicted by the present approaches.

  8. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Maryam Mousavi

    Full Text Available Flexible manufacturing system (FMS enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs. An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA, particle swarm optimization (PSO, and hybrid GA-PSO to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  9. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Science.gov (United States)

    Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

    2017-01-01

    Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  10. Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties

    International Nuclear Information System (INIS)

    Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

    2016-01-01

    In this paper, a stochastic model is proposed for coordinated scheduling of combined heat and power units in micro grid considering wind turbine and photovoltaic units. Uncertainties of electrical market price; the speed of wind and solar radiation are considered using a scenario-based method. In the method, scenarios are generated using roulette wheel mechanism based on probability distribution functions of input random variables. Using this method, the probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective function, coordinated scheduling of combined heat and power, wind turbine, and photovoltaic units change this problem to a mixed integer nonlinear one. Therefore to solve this problem modified particle swarm optimization algorithm is employed. The mentioned uncertainties lead to an increase in profit. Moreover, the optimal coordinated scheduling of renewable energy resources and thermal units in micro grids increase the total profit. In order to evaluate the performance of the proposed method, its performance is executed on modified 33 bus distributed system as a micro grid. - Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Maximizing profits of micro grid is considered as objective function. • Considering the uncertainties of problem lead to profit increasing. • Optimal scheduling of renewable energy sources and thermal units increases profit.

  11. GA Based Optimal Control for Maximizing PV Penetration at Transmission

    OpenAIRE

    Veni Chandran, Chittesh

    2016-01-01

    Utilization of distributed energy resources (DER’s) like photo-voltaic generators, is one of the possible solution for present scenario of energy crisis. Most of the study suggest the implementation of PV power stations at distribution level. In this paper detailed theoretical analyses of the impact of large scale PV on transmission level is analysed. The preliminary section of this paper provides literature review with specifications of IEEE-14 bus network. Two methodology ie, constant load ...

  12. Residual Stress Developed During the Cure of Thermosetting Polymers: Optimizing Cure Schedule to Minimize Stress.

    Energy Technology Data Exchange (ETDEWEB)

    Kropka, Jamie Michael; Stavig, Mark E.; Jaramillo, Rex

    2016-06-01

    When thermosetting polymers are used to bond or encapsulate electrical, mechanical or optical assemblies, residual stress, which often affects the performance and/or reliability of these devices, develops within the structure. The Thin-Disk-on-Cylinder structural response test is demonstrated as a powerful tool to design epoxy encapsulant cure schedules to reduce residual stress, even when all the details of the material evolution during cure are not explicitly known. The test's ability to (1) distinguish between cohesive and adhesive failure modes and (2) demonstrate methodologies to eliminate failure and reduce residual stress, make choices of cure schedules that optimize stress in the encapsulant unambiguous. For the 828/DEA/GMB material in the Thin-Disk-on-Cylinder geometry, the stress associated with cure is significant and outweighs that associated with cool down from the final cure temperature to room temperature (for measured lid strain, Scure I > I I e+h erma * II) * The difference between the final cure temperature and 1 1 -- the temperature at which the material gels, Tf-T ge i, was demonstrated to be a primary factor in determining the residual stress associated with cure. Increasing T f -T ge i leads to a reduction in cure stress that is described as being associated with balancing some of the 828/DEA/GMB cure shrinkage with thermal expansion. The ability to tune residual stress associated with cure by controlling T f -T ge i would be anticipated to translate to other thermosetting encapsulation materials, but the times and temperatures appropriate for a given material may vary widely.

  13. Integrated Optimization of Service-Oriented Train Plan and Schedule on Intercity Rail Network with Varying Demand

    Directory of Open Access Journals (Sweden)

    Wenliang Zhou

    2015-01-01

    Full Text Available For a better service level of a train operating plan, we propose an integrated optimization method of train planning and train scheduling, which generally are optimized, respectively. Based on the cost analysis of both passengers travelling and enterprises operation, and the constraint analysis of trains operation, we construct a multiobjective function and build an integrated optimization model with the aim of reducing both passenger travel costs and enterprise operating costs. Then, a solving algorithm is established based on the simulated annealing algorithm. Finally, using as an example the Changzhutan intercity rail network, as an example we analyze the optimized results and the influence of the model parameters on the results.

  14. An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands

    Directory of Open Access Journals (Sweden)

    Kun Li

    2015-01-01

    Full Text Available This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

  15. Multi-region optimal deployment of renewable energy considering different interregional transmission scenarios

    International Nuclear Information System (INIS)

    Wang, Ge; Zhang, Qi; Mclellan, Benjamin C.; Li, Hailong

    2016-01-01

    Renewable energy is expected to play much more important role in future low-carbon energy system, however, renewable energy has problems with regard to load-following and regional imbalance. This study aims to plan the deployment of intermittent renewable energy in multiple regions considering the impacts of regional natural conditions and generation capacity mix as well as interregional transmission capacity using a multi-region dynamic optimization model. The model was developed to find optimized development paths toward future smart electricity systems with high level penetration of intermittent renewable energy considering regional differences and interregional transmission at national scale. As a case study, the model was applied to plan power generation in nine interconnected regions in Japan out to 2030. Four scenarios were proposed with different supporting policies for the interregional power transmission infrastructures and different nuclear power phase-out scenarios. The analysis results show that (i) the government's support for power transmission infrastructures is vital important to develop more intermittent renewable energy in appropriate regions and utilize renewable energy more efficiently; (ii) nuclear and renewable can complement rather than replace each other if enough interregional transmission capacity is provided. - Highlights: • Plan the optimal deployment of intermittent renewable energy in multiple regions. • A multi-region dynamic optimization model was developed. • The impacts of natural conditions and interregional transmission are studied. • The government's support for transmission is vital important for renewable energy. • Nuclear and renewable can complement rather than replace each other.

  16. Research on optimal investment path of transmission corridor under the global energy Internet

    Science.gov (United States)

    Huang, Yuehui; Li, Pai; Wang, Qi; Liu, Jichun; Gao, Han

    2018-02-01

    Under the background of the global energy Internet, the investment planning of transmission corridor from XinJiang to Germany is studied in this article, which passes through four countries: Kazakhstan, Russia, Belarus and Poland. Taking the specific situation of different countries into account, including the length of transmission line, unit construction cost, completion time, transmission price, state tariff, inflation rate and so on, this paper constructed a power transmission investment model. Finally, the dynamic programming method is used to simulate the example, and the optimal strategies under different objective functions are obtained.

  17. Optimal Value of Series Capacitors for Uniform Field Distribution in Transmission Line MRI Coils

    DEFF Research Database (Denmark)

    Zhurbenko, Vitaliy

    2016-01-01

    Transmission lines are often used as coils in high field magnetic resonance imaging (MRI). Due to the distributed nature of transmission lines, coils based on them produce inhomogeneous field. This work investigates application of series capacitors to improve field homogeneity along the coil....... The equations for optimal values of evenly distributed capacitors are derived and expressed in terms of the implemented transmission line parameters.The achieved magnetic field homogeneity is estimated under quasistatic approximation and compared to the regular transmission line resonator. Finally, a more...... practical case of a microstrip line coil with two series capacitors is considered....

  18. Optimization of output power and transmission efficiency of magnetically coupled resonance wireless power transfer system

    Science.gov (United States)

    Yan, Rongge; Guo, Xiaoting; Cao, Shaoqing; Zhang, Changgeng

    2018-05-01

    Magnetically coupled resonance (MCR) wireless power transfer (WPT) system is a promising technology in electric energy transmission. But, if its system parameters are designed unreasonably, output power and transmission efficiency will be low. Therefore, optimized parameters design of MCR WPT has important research value. In the MCR WPT system with designated coil structure, the main parameters affecting output power and transmission efficiency are the distance between the coils, the resonance frequency and the resistance of the load. Based on the established mathematical model and the differential evolution algorithm, the change of output power and transmission efficiency with parameters can be simulated. From the simulation results, it can be seen that output power and transmission efficiency of the two-coil MCR WPT system and four-coil one with designated coil structure are improved. The simulation results confirm the validity of the optimization method for MCR WPT system with designated coil structure.

  19. Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission

    Directory of Open Access Journals (Sweden)

    Sandeep Sachdeva

    2011-01-01

    reduce the error of load forecasting, fuzzy method has been used with Artificial Neural Network (ANN and OFDM transmission is used to get data from outer world and send outputs to outer world accurately and quickly. The error has been reduced to a considerable level in the range of 2-3%. For further reducing the error, Orthogonal Frequency Division Multiplexing (OFDM can be used with Reed-Solomon (RS encoding. Further studies are going on with Fuzzy Regression methods to reduce the error more.

  20. Price-based optimal control of power flow in electrical energy transmission networks

    NARCIS (Netherlands)

    Jokic, A.; Lazar, M.; Bosch, van den P.P.J.; Bemporad, A.; Bicchi, A.; Buttazzo, G.

    2007-01-01

    This article presents a novel control scheme for achieving optimal power balancing and congestion control in electrical energy transmission networks via nodal prices. We develop an explicit controller that guarantees economically optimal steady-state operation while respecting all line flow

  1. Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms

    International Nuclear Information System (INIS)

    Khorramdel, Benyamin; Raoofat, Mahdi

    2012-01-01

    Distributed Generators (DGs) in a microgrid may operate in three different reactive power control strategies, including PV, PQ and voltage droop schemes. This paper proposes a new stochastic programming approach for reactive power scheduling of a microgrid, considering the uncertainty of wind farms. The proposed algorithm firstly finds the expected optimal operating point of each DG in V-Q plane while the wind speed is a probabilistic variable. A multi-objective function with goals of loss minimization, reactive power reserve maximization and voltage security margin maximization is optimized using a four-stage multi-objective nonlinear programming. Then, using Monte Carlo simulation enhanced by scenario reduction technique, the proposed algorithm simulates actual condition and finds optimal operating strategy of DGs. Also, if any DGs are scheduled to operate in voltage droop scheme, the optimum droop is determined. Also, in the second part of the research, to enhance the optimality of the results, PSO algorithm is used for the multi-objective optimization problem. Numerical examples on IEEE 34-bus test system including two wind turbines are studied. The results show the benefits of voltage droop scheme for mitigating the impacts of the uncertainty of wind. Also, the results show preference of PSO method in the proposed approach. -- Highlights: ► Reactive power scheduling in a microgrid considering loss and voltage security. ► Stochastic nature of wind farms affects reactive power scheduling and is considered. ► Advantages of using the voltage droop characteristics of DGs in voltage security are shown. ► Power loss, voltage security and VAR reserve are three goals of a multi-objective optimization. ► Monte Carlo method with scenario reduction is used to determine optimal control strategy of DGs.

  2. Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

    Science.gov (United States)

    Izah Anuar, Nurul; Saptari, Adi

    2016-02-01

    This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

  3. Optimal and Fair Resource Allocation for Multiuser Wireless Multimedia Transmissions

    Directory of Open Access Journals (Sweden)

    Zhangyu Guan

    2009-01-01

    Full Text Available This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY and Application (APP layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.

  4. Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization

    Directory of Open Access Journals (Sweden)

    Maciej Malawski

    2015-01-01

    Full Text Available This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL and CMPL and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications in a variety of domains. The data used for evaluation come from the synthetic workflows and from general purpose cloud benchmarks, as well as from the data measured in our own experiments with Montage, an astronomical application, executed on Amazon EC2 cloud. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.

  5. Optimal RTP Based Power Scheduling for Residential Load in Smart Grid

    Science.gov (United States)

    Joshi, Hemant I.; Pandya, Vivek J.

    2015-12-01

    To match supply and demand, shifting of load from peak period to off-peak period is one of the effective solutions. Presently flat rate tariff is used in major part of the world. This type of tariff doesn't give incentives to the customers if they use electrical energy during off-peak period. If real time pricing (RTP) tariff is used, consumers can be encouraged to use energy during off-peak period. Due to advancement in information and communication technology, two-way communications is possible between consumers and utility. To implement this technique in smart grid, home energy controller (HEC), smart meters, home area network (HAN) and communication link between consumers and utility are required. HEC interacts automatically by running an algorithm to find optimal energy consumption schedule for each consumer. However, all the consumers are not allowed to shift their load simultaneously during off-peak period to avoid rebound peak condition. Peak to average ratio (PAR) is considered while carrying out minimization problem. Linear programming problem (LPP) method is used for minimization. The simulation results of this work show the effectiveness of the minimization method adopted. The hardware work is in progress and the program based on the method described here will be made to solve real problem.

  6. Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

    Science.gov (United States)

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

  7. Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling

    Directory of Open Access Journals (Sweden)

    Helio Yochihiro Fuchigami

    2018-04-01

    Full Text Available The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines, with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop for the considered problem.

  8. Variable Ratio Hydrostatic Transmission Simulator for Optimal Wind Power Drivetrains

    Directory of Open Access Journals (Sweden)

    Jose M. Garcia-Bravo

    2017-01-01

    Full Text Available This work presents a hydromechanical transmission coupled to an electric AC motor and DC generator to simulate a wind power turbine drive train. The goal of this project was to demonstrate and simulate the ability of a hydrostatic variable ratio system to produce constant electric power at varying wind speeds. The experimental results show that the system can maintain a constant voltage when a 40% variation in input speed is produced. An accompanying computer simulation of the system was built and experimentally validated showing a discrete error no larger than 12%. Both the simulation and the experimental results show that the electrical power output can be regulated further if an energy storage device is used to absorb voltage spikes produced by abrupt changes in wind speed or wind direction.

  9. A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

    Directory of Open Access Journals (Sweden)

    Zhigang Lian

    2010-01-01

    Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

  10. A Three-Stage Optimization Algorithm for the Stochastic Parallel Machine Scheduling Problem with Adjustable Production Rates

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2013-01-01

    Full Text Available We consider a parallel machine scheduling problem with random processing/setup times and adjustable production rates. The objective functions to be minimized consist of two parts; the first part is related with the due date performance (i.e., the tardiness of the jobs, while the second part is related with the setting of machine speeds. Therefore, the decision variables include both the production schedule (sequences of jobs and the production rate of each machine. The optimization process, however, is significantly complicated by the stochastic factors in the manufacturing system. To address the difficulty, a simulation-based three-stage optimization framework is presented in this paper for high-quality robust solutions to the integrated scheduling problem. The first stage (crude optimization is featured by the ordinal optimization theory, the second stage (finer optimization is implemented with a metaheuristic called differential evolution, and the third stage (fine-tuning is characterized by a perturbation-based local search. Finally, computational experiments are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also discussed.

  11. Optimizing an F-16 Squadron Weekly Pilot Schedule for the Turkish Air Force

    Science.gov (United States)

    2010-03-01

    disrupted schedules are rescheduled , minimizing the total number of changes with respect to the previous schedule’s objective function. Output...producing rosters for a nursing staff in a large general hospital (Dowsland, 1998) and afterwards Aickelin and Dowsland use an Indirect Genetic...algorithm to improve the solutions of the nurse scheduling problem which is similar to the fighter squadron pilot scheduling problem (Aickelin and

  12. Optimal Scheduling of Distributed Energy Resources and Responsive Loads in Islanded Microgrids Considering Voltage and Frequency Security Constraints

    DEFF Research Database (Denmark)

    Vahedipour-Dahraie, Mostafa; Najafi, Hamid Reza; Anvari-Moghaddam, Amjad

    2018-01-01

    in islanded MGs with regard to voltage and frequency security constraints. Based on the proposed model, scheduling of the controllable units in both supply and demand sides is done in a way not only to maximize the expected profit of MG operator (MGO), but also to minimize the energy payments of customers...... on the system’s performance in terms of voltage and frequency stability. Moreover, optimal coordination of DERs and responsive loads can increase the expected profit of MGO significantly. The effectiveness of the proposed scheduling approach is verified on an islanded MG test system over a 24-h period....

  13. Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Laxmi A. Bewoor

    2017-10-01

    Full Text Available The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

  14. Optimal information transmission in organizations: search and congestion

    Energy Technology Data Exchange (ETDEWEB)

    Arenas, A.; Cabrales, A.; Danon, L.; Diaz-Guilera, A.; Guimera, R.; Vega-Redondo, F.

    2008-01-01

    We propose a stylized model of a problem-solving organization whose internal communication structure is given by a fixed network. Problems arrive randomly anywhere in this network and must find their way to their respective specialized solvers by relying on local information alone. The organization handles multiple problems simultaneously. For this reason, the process may be subject to congestion. We provide a characterization of the threshold of collapse of the network and of the stock of floating problems (or average delay) that prevails below that threshold. We build upon this characterization to address a design problem: the determination of what kind of network architecture optimizes performance for any given problem arrival rate. We conclude that, for low arrival rates, the optimal network is very polarized (i.e. star-like or centralized), whereas it is largely homogeneous (or decentralized) for high arrival rates. These observations are in line with a common transformation experienced by information-intensive organizations as their work flow has risen in recent years.

  15. Optimization of Structural Design for Sustainable Construction of Transmission Tower Based on Topographical Algorithm

    International Nuclear Information System (INIS)

    Muda, Zakaria Che; Thiruchelvam, Sivadass; Mustapha, Kamal Nasharuddin; Omar, Rohayu Che; Usman, Fathoni; Alam, Md Ashrafu

    2013-01-01

    Optimization of transmission tower structures is traditionally based on either optimization of members sizes with fixed topographical shape or based on structural analysis modelling strategies without taking cognizance of fabrication and constructability issue facing the contractors . This paper look into an integrated optimum design approach strategies whereby size, shape and topology are combined together with the fabrication issues in the construction of the transmission tower. The topographical algorithm is based on changing the inclination degree of the legs of the tower at first with optimum individual members sizing and later rationalized member sizes are performed through member groupings for the ease fabrication and construction of the transmission tower. The optimum weight using topographical algorithm obtained for the transmission tower is 10,924 kg for singular members and 18,430 kg for element grouping at 10° inclination angle.

  16. Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

    Science.gov (United States)

    Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar

    2018-01-01

    Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Optimization of design and erection methods to minimize the construction time-schedule of EPR plants

    International Nuclear Information System (INIS)

    Pierrat, Michel; L'Huby, Yvan; Decelle, Alain

    1999-01-01

    This paper presents the results of the investigations made during the Basic Design of the EPR project (European Pressurized water Reactor) to shorten the construction schedule. A 57 months construction schedule can be reached for the first unit. The investigations concern both design and construction methods. (author)

  18. Scheduling and Optimization of Fault-Tolerant Embedded Systems with Transparency/Performance Trade-Offs

    DEFF Research Database (Denmark)

    Izosimov, Viacheslav; Pop, Paul; Eles, Petru

    2012-01-01

    In this article, we propose a strategy for the synthesis of fault-tolerant schedules and for the mapping of fault-tolerant applications. Our techniques handle transparency/performance trade-offs and use the faultoccurrence information to reduce the overhead due to fault tolerance. Processes...... and messages are statically scheduled, and we use process reexecution for recovering from multiple transient faults. We propose a finegrained transparent recovery, where the property of transparency can be selectively applied to processes and messages. Transparency hides the recovery actions in a selected part...... of the application so that they do not affect the schedule of other processes and messages. While leading to longer schedules, transparent recovery has the advantage of both improved debuggability and less memory needed to store the faulttolerant schedules....

  19. Optimized Matching Lift Unit Transmission Ratio of Engine Driven Ducted Fan

    Directory of Open Access Journals (Sweden)

    Xiao Senlin

    2018-01-01

    Full Text Available As a kind of VTOL technology, ducted fan is not only used by many kinds of aircrafts, but also one of the trends of the future aircraft lift system, and attracts more and more attention. For an engine driven ducted fan lift unit, involving the engine and ducted fan matching problem, the form of transmission and transmission ratio are the key design parameters. In order to design and develop a ducted fan aircraft reasonably, a thrust test platform was set up to connect the engine with the ducted fan through the belt driving. The matching relationship between the engine and the transmission system was experimentally studied and the optimal transmission ratio was determined. The results showed that the optimal transmission ratio for the engine 1 is 2.2:1, and for the engine 2, the optimal transmission ratio should be 2.95:1 based on the current ducted and movable blade aerofoil design. At this time, the lift will exceed 130 kg•f, meeting the aircraft's original design requirements.

  20. MODELLING TEMPORAL SCHEDULE OF URBAN TRAINS USING AGENT-BASED SIMULATION AND NSGA2-BASED MULTIOBJECTIVE OPTIMIZATION APPROACHES

    Directory of Open Access Journals (Sweden)

    M. Sahelgozin

    2015-12-01

    Full Text Available Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

  1. A rectangle bin packing optimization approach to the signal scheduling problem in the FlexRay static segment

    Institute of Scientific and Technical Information of China (English)

    Rui ZHAO; Gui-he QIN; Jia-qiao LIU

    2016-01-01

    As FlexRay communication protocol is extensively used in distributed real-time applications on vehicles, signal scheduling in FlexRay network becomes a critical issue to ensure the safe and efficient operation of time-critical applications. In this study, we propose a rectangle bin packing optimization approach to schedule communication signals with timing constraints into the FlexRay static segment at minimum bandwidth cost. The proposed approach, which is based on integer linear program-ming (ILP), supports both the slot assignment mechanisms provided by the latest version of the FlexRay specification, namely, the single sender slot multiplexing, and multiple sender slot multiplexing mechanisms. Extensive experiments on a synthetic and an automotive X-by-wire system case study demonstrate that the proposed approach has a well optimized performance.

  2. Susceptibility of optimal train schedules to stochastic disturbances of process times

    DEFF Research Database (Denmark)

    Larsen, Rune; Pranzo, Marco; D’Ariano, Andrea

    2013-01-01

    study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact...... and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced...

  3. Optimal stochastic scheduling of CHP-PEMFC, WT, PV units and hydrogen storage in reconfigurable micro grids considering reliability enhancement

    International Nuclear Information System (INIS)

    Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

    2017-01-01

    Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Uncertainties of wind speed, solar radiation and electricity market price are considered. • Profit maximization, emission and AENS minimization are considered as objective functions. • Modified firefly algorithm is employed to solve the problem. - Abstract: Nowadays the operation of renewable energy sources and combined heat and power (CHP) units is increased in micro grids; therefore, to reach optimal performance, optimal scheduling of these units is required. In this regard, in this paper a micro grid consisting of proton exchange membrane fuel cell-combined heat and power (PEMFC-CHP), wind turbines (WT) and photovoltaic (PV) units, is modeled to determine the optimal scheduling state of these units by considering uncertain behavior of renewable energy resources. For this purpose, a scenario-based method is used for modeling the uncertainties of electrical market price, the wind speed, and solar irradiance. It should be noted that the hydrogen storage strategy is also applied in this study for PEMFC-CHP units. Market profit, total emission production, and average energy not supplied (AENS) are the objective functions considered in this paper simultaneously. Consideration of the above-mentioned objective functions converts the proposed problem to a mixed integer nonlinear programming. To solve this problem, a multi-objective firefly algorithm is used. The uncertainties of parameters convert the mixed integer nonlinear programming problem to a stochastic mixed integer nonlinear programming problem. Moreover, optimal coordinated scheduling of renewable energy resources and thermal units in micro-grids improve the value of the objective functions. Simulation results obtained from a modified 33-bus distributed network as a micro grid illustrates the effectiveness of the proposed method.

  4. Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

    OpenAIRE

    Thiruvenkadam, T; Karthikeyani, V

    2014-01-01

    Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

  5. Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

    Directory of Open Access Journals (Sweden)

    Zunaira Nadeem

    2018-04-01

    Full Text Available In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO, genetic algorithm (GA, firefly algorithm (FA and optimal stopping rule (OSR theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

  6. Optimization of control strategies for epidemics in heterogeneous populations with symmetric and asymmetric transmission

    OpenAIRE

    Ndeffo Mbah , Martial L.; Gilligan , Christopher A.

    2010-01-01

    Abstract There is growing interest in incorporating economic factors into epidemiological models in order to identify optimal strategies for disease control when resources are limited. In this paper we consider how to optimize the control of a pathogen that is capable of infecting multiple hosts with different rates of transmission within and between species. Our objective is to find control strategies that maximize the discounted number of healthy individuals. We consider two clas...

  7. Optimal Day-ahead Charging Scheduling of Electric Vehicles through an Aggregative Game Model

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Huang, Shaojun

    2017-01-01

    The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable...... in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging...... scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved...

  8. Optimal Scheduling and Operating Target (OPTAR) Cost Model for Aircraft Carriers in the Fleet Response Plan

    National Research Council Canada - National Science Library

    York, Michael A

    2008-01-01

    .... This capability is developed through the Fleet Readiness Training Plan (FRTP) where the Navy's carriers are scheduled in staggered 32-month cycles consisting of four phases of progressive readiness levels...

  9. Robust optimization for load scheduling of a smart home with photovoltaic system

    International Nuclear Information System (INIS)

    Wang, Chengshan; Zhou, Yue; Jiao, Bingqi; Wang, Yamin; Liu, Wenjian; Wang, Dan

    2015-01-01

    Highlights: • Robust household load scheduling is presented for smart homes with PV system. • A robust counterpart is formulated to deal with PV output uncertainty. • The robust counterpart is finally transformed to a quadratic programming problem. • Load schedules with different robustness can be made by the proposed method. • Feed-in tariff and PV output would affect the significance of the proposed method. - Abstract: In this paper, a robust approach is developed to tackle the uncertainty of PV power output for load scheduling of smart homes integrated with household PV system. Specifically, a robust formulation is proposed and further transformed to an equivalent quadratic programming problem. Day-ahead load schedules with different robustness can be generated by solving the proposed robust formulation with different predefined parameters. The validity and advantage of the proposed approach has been verified by simulation results. Also, the effects of feed-in tariff and PV output have been evaluated

  10. Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

    Directory of Open Access Journals (Sweden)

    Yuqing Yang

    2015-09-01

    Full Text Available With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics.

  11. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Songfeng; Sun, Chengfu; Lu, Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-03-15

    This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms. (author)

  12. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Lu Songfeng [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Sun Chengfu, E-mail: ajason_369@sina.co [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-03-15

    This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

  13. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

    International Nuclear Information System (INIS)

    Lu Songfeng; Sun Chengfu; Lu Zhengding

    2010-01-01

    This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

  14. Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance.

    Science.gov (United States)

    Carter, Christine E; Grahn, Jessica A

    2016-01-01

    Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the "contextual interference effect." While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule

  15. Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance

    Science.gov (United States)

    Carter, Christine E.; Grahn, Jessica A.

    2016-01-01

    Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the “contextual interference effect.” While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule

  16. Optimizing music learning: Exploring how blocked and interleaved practice schedules affect advanced performance

    Directory of Open Access Journals (Sweden)

    Christine E Carter

    2016-08-01

    Full Text Available Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the contextual interference effect. While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (twelve minutes per piece and a second concerto exposition and technical excerpt to practice in an interleaved schedule (three minutes per piece, alternating until a total of twelve minutes of practice were completed on each piece. Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated

  17. Optimizing Fire Department Operations Through Work Schedule Analysis, Alternative Staffing, and Nonproductive Time Reduction

    Science.gov (United States)

    2014-09-01

    and disadvantages ; for example, although the majority of fire departments in the United States abide by the 24/48- schedule, this schedule also leads...costs. The local bargaining group also falls into these criteria. The local bargaining unit must be agreeable to the policy changes. They cannot... agreeable to the local bargaining group particularly when it is addressed in an article of the current bargaining agreement. Additionally, the local

  18. Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm

    OpenAIRE

    Lingna He; Qingshui Li; Linan Zhu

    2012-01-01

    In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the...

  19. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling

    International Nuclear Information System (INIS)

    He Yaoyao; Zhou Jianzhong; Xiang Xiuqiao; Chen Heng; Qin Hui

    2009-01-01

    The goal of this paper is to present a novel chaotic particle swarm optimization (CPSO) algorithm and compares the efficiency of three one-dimensional chaotic maps within symmetrical region for long-term cascaded hydroelectric system scheduling. The introduced chaotic maps improve the global optimal capability of CPSO algorithm. Moreover, a piecewise linear interpolation function is employed to transform all constraints into restrict upriver water level for implementing the maximum of objective function. Numerical results and comparisons demonstrate the effect and speed of different algorithms on a practical hydro-system.

  20. Optimal model-based deficit irrigation scheduling using AquaCrop: a simulation study with cotton, potato and tomato

    DEFF Research Database (Denmark)

    Linker, Raphael; Ioslovich, Ilya; Sylaios, Georgios

    2016-01-01

    -smooth behavior of the objective function and the fact that it involves multiple integer variables. We developed an optimization scheme for generating sub-optimal irrigation schedules that take implicitly into account the response of the crop to water stress, and used these as initial guesses for a full......Water shortage is the main limiting factor for agricultural productivity in many countries and improving water use efficiency in agriculture has been the focus of numerous studies. The usual approach to limit water consumption in agriculture is to apply water quotas and in such a situation farmers...... variables are the irrigation amounts for each day of the season. The objective function is the expected yield calculated with the use of a model. In the present work we solved this optimization problem for three crops modeled by the model AquaCrop. This optimization problem is non-trivial due to the non...

  1. Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

    Energy Technology Data Exchange (ETDEWEB)

    A.Badri; Jadid, S. [Department of Electrical Engineering, Iran University of Science and Technology (Iran); Rashidinejad, M. [Shahid Bahonar University, Kerman (Iran); Moghaddam, M.P. [Tarbiat Modarres University, Tehran (Iran)

    2008-06-15

    In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)

  2. Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

    International Nuclear Information System (INIS)

    Badri, A.; Jadid, S.; Rashidinejad, M.; Moghaddam, M.P.

    2008-01-01

    In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)

  3. SU-F-19A-08: Optimal Time Release Schedule of In-Situ Drug Release During Permanent Prostate Brachytherapy

    International Nuclear Information System (INIS)

    Cormack, R; Ngwa, W; Makrigiorgos, G; Tangutoori, S; Rajiv, K; Sridhar, S

    2014-01-01

    Purpose: Permanent prostate brachytherapy spacers can be used to deliver sustained doses of radiosentitizing drug directly to the target, in order to enhance the radiation effect. Implantable nanoplatforms for chemo-radiation therapy (INCeRTs) have a maximum drug capacity and can be engineered to control the drug release schedule. The optimal schedule for sensitization during continuous low dose rate irradiation is unknown. This work studies the optimal release schedule of drug for both traditional sensitizers, and those that work by suppressing DNA repair processes. Methods: Six brachytherapy treatment plans were used to model the anatomy, implant geometry and calculate the spatial distribution of radiation dose and drug concentrations for a range of drug diffusion parameters. Three state partial differential equations (cells healthy, damaged or dead) modeled the effect of continuous radiation (radiosensitivities α,β) and cellular repair (time tr) on a cell population. Radiosensitization was modeled as concentration dependent change in α,β or tr which with variable duration under the constraint of fixed total drug release. Average cell kill was used to measure effectiveness. Sensitization by means of both enhanced damage and reduced repair were studied. Results: Optimal release duration is dependent on the concentration of radiosensitizer compared to the saturation concentration (csat) above which additional sensitization does not occur. Long duration drug release when enhancing α or β maximizes cell death when drug concentrations are generally over csat. Short term release is optimal for concentrations below saturation. Sensitization by suppressing repair has a similar though less distinct trend that is more affected by the radiation dose distribution. Conclusion: Models of sustained local radiosensitization show potential to increase the effectiveness of radiation in permanent prostate brachytherapy. INCeRTs with high drug capacity produce the greatest

  4. STDP in adaptive neurons gives close-to-optimal information transmission

    Directory of Open Access Journals (Sweden)

    Guillaume Hennequin

    2010-12-01

    Full Text Available Spike-frequency adaptation is known to enhance the transmission of information in sensory spiking neurons, by rescaling the dynamic range for input processing, matching it to the temporal statistics of the sensory stimulus. Achieving maximal information transmission has also been recently postulated as a role for Spike-Timing Dependent Plasticity (STDP. However, the link between optimal plasticity and STDP in cortex remains loose, and so does the relationship between STDP and adaptation processes. We investigate how STDP, as described by recent minimal models derived from experimental data, influences the quality of information transmission in an adapting neuron. We show that a phenomenological model based on triplets of spikes yields almost the same information rate as an optimal model specially designed to this end. In contrast, the standard pair-based model of STDP does not improve information transmission as much. This result holds not only for additive STDP with hard weight bounds, known to produce bimodal distributions of synaptic weights, but also for weight-dependent STDP in the context of unimodal but skewed weight distributions. We analyze the similarities between the triplet model and the optimal learning rule, and find that the triplet effect is an important feature of the optimal model when the neuron is adaptive. If STDP is optimized for information transmission, it must take into account the dynamical properties of the postsynaptic cell, which might explain the target-cell specificity of STDP. In particular, it accounts for the differences found in vitro between STDP at excitatory synapses onto principal cells and those onto fast-spiking interneurons.

  5. Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss

    Directory of Open Access Journals (Sweden)

    Yongli Wang

    2018-06-01

    Full Text Available Integrated energy systems (IESs are considered a trending solution for the energy crisis and environmental problems. However, the diversity of energy sources and the complexity of the IES have brought challenges to the economic operation of IESs. Aiming at achieving optimal scheduling of components, an IES operation optimization model including photovoltaic, combined heat and power generation system (CHP and battery energy storage is developed in this paper. The goal of the optimization model is to minimize the operation cost under the system constraints. For the optimization process, an optimization principle is conducted, which achieves maximized utilization of photovoltaic by adjusting the controllable units such as energy storage and gas turbine, as well as taking into account the battery lifetime loss. In addition, an integrated energy system project is taken as a research case to validate the effectiveness of the model via the improved differential evolution algorithm (IDEA. The comparison between IDEA and a traditional differential evolution algorithm shows that IDEA could find the optimal solution faster, owing to the double variation differential strategy. The simulation results in three different battery states which show that the battery lifetime loss is an inevitable factor in the optimization model, and the optimized operation cost in 2016 drastically decreased compared with actual operation data.

  6. Mix-oriented manufacturing control (MOMC): a quasi-optimal procedure for dynamic scheduling control

    Science.gov (United States)

    Cristofari, Marco; Caron, Franco; McDuffie, Ernest L.

    1997-12-01

    The total system throughput (ST) is one of the most important decision variables at the planning/scheduling phase of a manufacturing system. Material requirement planning (MRP) and master production schedule (MPS) are based on the assumption that ST is known. All the subsequent developments (e.g. jobs- release, system work-load, input-product mix, etc.) depends on such an assumption. If this assumption is incorrect, the production activity control (PAC) will not be able to satisfy the planned targets during the scheduling phase. Delays and bottlenecks will be unavoidable in the system. In case of random flexible manufacturing system (FMS) (or, in general, job-shop production), the measure of ST can not be evaluated a priori without running simulations or observing the actual flow of the operations in the system. The way entities enter the system (sequencing and percentage of the input products) effects the value of ST in such a way that estimation based on historical data are highly risky. The methodology proposed in this paper allows the scheduler to assess the analytical functions which link ST, and other output performance variables, to the input product mix (IPM). This way the robustness of the scheduling plan can be verified before the actual release of the jobs into the system.

  7. A PMBGA to Optimize the Selection of Rules for Job Shop Scheduling Based on the Giffler-Thompson Algorithm

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2012-01-01

    Full Text Available Most existing research on the job shop scheduling problem has been focused on the minimization of makespan (i.e., the completion time of the last job. However, in the fiercely competitive market nowadays, delivery punctuality is more important for maintaining a high service reputation. So in this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. Several dispatching rules are adopted in the Giffler-Thompson algorithm for constructing active schedules. It is noticeable that the rule selections for scheduling consecutive operations are not mutually independent but actually interrelated. Under such circumstances, a probabilistic model-building genetic algorithm (PMBGA is proposed to optimize the sequence of selected rules. First, we use Bayesian networks to model the distribution characteristics of high-quality solutions in the population. Then, the new generation of individuals is produced by sampling the established Bayesian network. Finally, some elitist individuals are further improved by a special local search module based on parameter perturbation. The superiority of the proposed approach is verified by extensive computational experiments and comparisons.

  8. A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

    Directory of Open Access Journals (Sweden)

    M. Fera

    2018-09-01

    Full Text Available Additive Manufacturing (AM is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®, is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

  9. A Reconfigured Whale Optimization Technique (RWOT for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

    Directory of Open Access Journals (Sweden)

    Noha H. El-Amary

    2018-03-01

    Full Text Available This paper studies the effect on the rate of growth of carbon dioxide emission in seaports’ atmosphere of replacing a part of the fossil fuel electrical power generation by clean renewable electrical energies, through two different scheduling strategies. The increased rate of harmful greenhouse gas emissions due to conventional electrical power generation severely affects the whole global atmosphere. Carbon dioxide and other greenhouse gases emissions are responsible for a significant share of global warming. Developing countries participate in this environmental distortion to a great percentage. Two different suggested strategies for renewable electrical energy scheduling are discussed in this paper, to attain a sustainable green port by the utilization of two mutual sequential clean renewable energies, which are biomass and photovoltaic (PV energy. The first strategy, which is called the eco-availability mode, is a simple method. It is based on operating the renewable electrical energy sources during the available time of operation, taking into consideration the simple and basic technical issues only, without considering the sophisticated technical and economical models. The available operation time is determined by the environmental condition. This strategy is addressed to result on the maximum available Biomass and PV energy generation based on the least environmental and technical conditions (panel efficiency, minimum average daily sunshine hours per month, minimum average solar insolation per month. The second strategy, which is called the Intelligent Scheduling (IS mode, relies on an intelligent Reconfigured Whale Optimization Technique (RWOT based-model. In this strategy, some additional technical and economical issues are considered. The studied renewable electrical energy generation system is considered in two scenarios, which are with and without storage units. The objective (cost function of the scheduling optimization problem, for

  10. Joint optimization of CQI calculation and interference mitigation for user scheduling in MIMO-OFDM systems

    KAUST Repository

    Sadek, Mirette; Aï ssa, Sonia

    2011-01-01

    In MIMO-OFDM multiuser systems, user scheduling is employed as a means of multiple access. In a downlink scenario, users that share the same subcarriers of an OFDM symbol are separated through precoding in order to achieve space division multiple access (SDMA). User scheduling techniques rely on channel knowledge at the transmitter, namely, the so-called channel quality indicator (CQI). In this paper, we implement a leakage-based precoding algorithm whose purpose is twofold. First, it is used to compute a reliable CQI based on a group of precoding vectors that are adapted to the channel. Then, it implements user scheduling through using the optimum vectors for precoding, thus minimizing interference among users. We also introduce the concept of resource block size adaptivity. The resource block (RB) is defined as the least unit in an OFDM symbol that a user can be assigned to. We propose a variable RB size that adapts to the channel conditions. © 2011 IEEE.

  11. Optimization of workflow scheduling in Utility Management System with hierarchical neural network

    Directory of Open Access Journals (Sweden)

    Srdjan Vukmirovic

    2011-08-01

    Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

  12. Joint optimization of CQI calculation and interference mitigation for user scheduling in MIMO-OFDM systems

    KAUST Repository

    Sadek, Mirette

    2011-05-01

    In MIMO-OFDM multiuser systems, user scheduling is employed as a means of multiple access. In a downlink scenario, users that share the same subcarriers of an OFDM symbol are separated through precoding in order to achieve space division multiple access (SDMA). User scheduling techniques rely on channel knowledge at the transmitter, namely, the so-called channel quality indicator (CQI). In this paper, we implement a leakage-based precoding algorithm whose purpose is twofold. First, it is used to compute a reliable CQI based on a group of precoding vectors that are adapted to the channel. Then, it implements user scheduling through using the optimum vectors for precoding, thus minimizing interference among users. We also introduce the concept of resource block size adaptivity. The resource block (RB) is defined as the least unit in an OFDM symbol that a user can be assigned to. We propose a variable RB size that adapts to the channel conditions. © 2011 IEEE.

  13. Optimal production scheduling for energy efficiency improvement in biofuel feedstock preprocessing considering work-in-process particle separation

    International Nuclear Information System (INIS)

    Li, Lin; Sun, Zeyi; Yao, Xufeng; Wang, Donghai

    2016-01-01

    Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing. - Highlights: • A novel method to schedule production in biofuel feedstock preprocessing process. • Systems modeling approach is used. • Capable of optimize preprocessing to reduce energy waste and improve energy efficiency. • A numerical case is used to illustrate the effectiveness of the method. • Energy consumption per unit production can be significantly reduced.

  14. Optimizing Transmission Network Expansion Planning With The Mean Of Chaotic Differential Evolution Algorithm

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abdelaziz

    2015-08-01

    Full Text Available This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning TNEP using an AC model associated with reactive power planning RPP. The reliabilityredundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits can be subject to the cost weight and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliabilityredundancy optimization problems. Evolutionary algorithms EAs paradigms of evolutionary computation field are stochastic and robust meta-heuristics useful to solve reliabilityredundancy optimization problems. EAs such as genetic algorithm evolutionary programming evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm DEA population-based algorithm is an optimal algorithm with powerful global searching capability but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm CDE based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.

  15. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

    DEFF Research Database (Denmark)

    Soares, Joao; Vale, Zita; Canizes, Bruno

    2013-01-01

    This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming...... to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow...

  16. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

  17. An Optimal Joint User Association and Power Allocation Algorithm for Secrecy Information Transmission in Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Rong Chai

    2017-01-01

    Full Text Available In recent years, heterogeneous radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets. In this paper, we consider the downlink secure transmission of HetNets where the information transmission from base stations (BSs to legitimate users is subject to the interception of eavesdroppers. In particular, we stress the problem of joint user association and power allocation of the BSs. To achieve data transmission in a secure and energy efficient manner, we introduce the concept of secrecy energy efficiency which is defined as the ratio of the secrecy transmission rate and power consumption of the BSs and formulate the problem of joint user association and power allocation as an optimization problem which maximizes the joint secrecy energy efficiency of all the BSs under the power constraint of the BSs and the minimum data rate constraint of user equipment (UE. By equivalently transforming the optimization problem into two subproblems, that is, power allocation subproblem and user association subproblem of the BSs, and applying iterative method and Kuhn-Munkres (K-M algorithm to solve the two subproblems, respectively, the optimal user association and power allocation strategies can be obtained. Numerical results demonstrate that the proposed algorithm outperforms previously proposed algorithms.

  18. Cross-layer Energy Optimization Under Image Quality Constraints for Wireless Image Transmissions.

    Science.gov (United States)

    Yang, Na; Demirkol, Ilker; Heinzelman, Wendi

    2012-01-01

    Wireless image transmission is critical in many applications, such as surveillance and environment monitoring. In order to make the best use of the limited energy of the battery-operated cameras, while satisfying the application-level image quality constraints, cross-layer design is critical. In this paper, we develop an image transmission model that allows the application layer (e.g., the user) to specify an image quality constraint, and optimizes the lower layer parameters of transmit power and packet length, to minimize the energy dissipation in image transmission over a given distance. The effectiveness of this approach is evaluated by applying the proposed energy optimization to a reference ZigBee system and a WiFi system, and also by comparing to an energy optimization study that does not consider any image quality constraint. Evaluations show that our scheme outperforms the default settings of the investigated commercial devices and saves a significant amount of energy at middle-to-large transmission distances.

  19. Inter-terminal transfer between port terminals. A continuous mathematical programming model to optimize scheduling and deployment of transport units

    Energy Technology Data Exchange (ETDEWEB)

    Morales Fusco, P.; Pedrielli, G.; Zhou, C.; Hay Lee, L.; Peng Chew, E.

    2016-07-01

    In most large port cities, the challenge of inter-terminal transfers (ITT) prevails due to the long distance between multiple terminals. The quantity of containers requiring movement between terminals as they connect from pre-carrier to on-carrier is increasing with the formation of the mega-alliances. The paper proposes a continuous time mathematical programming model to optimize the deployment and schedule of trucks and barges to minimize the number of operating transporters, their makespan, costs and the distance travelled by the containers by choosing the right combination of transporters and container movements while fulfilling time window restrictions imposed on reception of the containers. A multi-step routing problem is developed where transporters can travel from one terminal to another and/or load or unload containers from a specific batch at each step. The model proves successful in identifying the costless schedule and means of transportation. And a sensibility analysis over the parameters used is provided. (Author)

  20. Monitoring Strategies in Permeable Pavement Systems to Optimize Maintenance Scheduling - abstract

    Science.gov (United States)

    As the surface in a permeable pavement system clogs and performance decreases, maintenance is required to preserve the design function. Currently, guidance is limited for scheduling maintenance on an as needed basis. Previous research has shown that surface clogging in a permea...

  1. Resource-Constrained Optimal Scheduling of Synchronous Dataflow Graphs via Timed Automata

    NARCIS (Netherlands)

    Ahmad, W.; de Groote, Robert; Holzenspies, P.K.F.; Stoelinga, Mariëlle Ida Antoinette; van de Pol, Jan Cornelis

    Synchronous dataflow (SDF) graphs are a widely used formalism for modelling, analysing and realising streaming applications, both on a single processor and in a multiprocessing context. Efficient schedules are essential to obtain maximal throughput under the constraint of available resources. This

  2. Product code optimization for determinate state LDPC decoding in robust image transmission.

    Science.gov (United States)

    Thomos, Nikolaos; Boulgouris, Nikolaos V; Strintzis, Michael G

    2006-08-01

    We propose a novel scheme for error-resilient image transmission. The proposed scheme employs a product coder consisting of low-density parity check (LDPC) codes and Reed-Solomon codes in order to deal effectively with bit errors. The efficiency of the proposed scheme is based on the exploitation of determinate symbols in Tanner graph decoding of LDPC codes and a novel product code optimization technique based on error estimation. Experimental evaluation demonstrates the superiority of the proposed system in comparison to recent state-of-the-art techniques for image transmission.

  3. Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Meng Xiong

    2015-08-01

    Full Text Available Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

  4. Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

    DEFF Research Database (Denmark)

    Sousa, Tiago; Ghazvini, Mohammad Ali Fotouhi; Morais, Hugo

    2015-01-01

    The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements....... This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles....

  5. A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip.

    Directory of Open Access Journals (Sweden)

    Cong Hu

    Full Text Available We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO, which incorporates Levy flights into multi-verse optimizer (MVO algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC. Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

  6. Optimal Capacity Proportion and Distribution Planning of Wind, Photovoltaic and Hydro Power in Bundled Transmission System

    Science.gov (United States)

    Ye, X.; Tang, Q.; Li, T.; Wang, Y. L.; Zhang, X.; Ye, S. Y.

    2017-05-01

    The wind, photovoltaic and hydro power bundled transmission system attends to become common in Northwest and Southwest of China. To make better use of the power complementary characteristic of different power sources, the installed capacity proportion of wind, photovoltaic and hydro power, and their capacity distribution for each integration node is a significant issue to be solved in power system planning stage. An optimal capacity proportion and capacity distribution model for wind, photovoltaic and hydro power bundled transmission system is proposed here, which considers the power out characteristic of power resources with different type and in different area based on real operation data. The transmission capacity limit of power grid is also considered in this paper. Simulation cases are tested referring to one real regional system in Southwest China for planning level year 2020. The results verify the effectiveness of the model in this paper.

  7. Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

    Science.gov (United States)

    Liu, Mengqi; Shan, Miyuan; Wu, Juan

    2014-01-01

    For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.

  8. A Mutualism Quantum Genetic Algorithm to Optimize the Flow Shop Scheduling with Pickup and Delivery Considerations

    Directory of Open Access Journals (Sweden)

    Jinwei Gu

    2015-01-01

    Full Text Available A mutualism quantum genetic algorithm (MQGA is proposed for an integrated supply chain scheduling with the materials pickup, flow shop scheduling, and the finished products delivery. The objective is to minimize the makespan, that is, the arrival time of the last finished product to the customer. In MQGA, a new symbiosis strategy named mutualism is proposed to adjust the size of each population dynamically by regarding the mutual influence relation of the two subpopulations. A hybrid Q-bit coding method and a local speeding-up method are designed to increase the diversity of genes, and a checking routine is carried out to ensure the feasibility of each solution; that is, the total physical space of each delivery batch could not exceed the capacity of the vehicle. Compared with the modified genetic algorithm (MGA and the quantum-inspired genetic algorithm (QGA, the effectiveness and efficiency of the MQGA are validated by numerical experiments.

  9. Unified approach for the optimization of energy and water in multipurpose batch plants using a flexible scheduling framework

    CSIR Research Space (South Africa)

    Adekola, O

    2013-05-01

    Full Text Available & Engineering Chemistry Research Vol. 52(25)/ pp 8488-8506 Unified Approach for the Optimization of Energy and Water in Multipurpose Batch Plants Using a Flexible Scheduling Framework Omobolanle Adekola,† Jane D. Stamp,† Thokozani Majozi,*,†,‡ Anurag... Garg,§ and Santanu Bandyopadhyay⊥ †Department of Chemical Engineering, University of Pretoria, Lynnwood Road, Pretoria, 0002, South Africa ‡Modelling and Digital S ien e, S , Meiring aud oad, retoria, 02, South Africa §Centre...

  10. Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.

    Science.gov (United States)

    Kostal, Lubomir; Kobayashi, Ryota

    2015-10-01

    Information theory quantifies the ultimate limits on reliable information transfer by means of the channel capacity. However, the channel capacity is known to be an asymptotic quantity, assuming unlimited metabolic cost and computational power. We investigate a single-compartment Hodgkin-Huxley type neuronal model under the spike-rate coding scheme and address how the metabolic cost and the decoding complexity affects the optimal information transmission. We find that the sub-threshold stimulation regime, although attaining the smallest capacity, allows for the most efficient balance between the information transmission and the metabolic cost. Furthermore, we determine post-synaptic firing rate histograms that are optimal from the information-theoretic point of view, which enables the comparison of our results with experimental data. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  11. Optimization of nas lemoore scheduling to support a growing aircraft population

    Science.gov (United States)

    2017-03-01

    service it. Both heuristics model the amount of fuel available in the trucks as in MSISCHE. For the current policy, each hot skid has its own queue...reflect the official policy or position of the Department of Defense or the U.S. Government. IRB number ____N/A____. 12a. DISTRIBUTION / AVAILABILITY ...well as constraints imposed by limited military operating area (MOA) availability . Given the complexity of this scheduling problem, attempting to

  12. Optimal Routing and Scheduling of Charge for Electric Vehicles: Case Study

    OpenAIRE

    Barco, John; Guerra, Andres; Muñoz, Luis; Quijano, Nicanor

    2013-01-01

    In Colombia, there is an increasing interest about improving public transportation. One of the proposed strategies in that way is the use battery electric vehicles (BEVs). One of the new challenges is the BEVs routing problem, which is subjected to the traditional issues of the routing problems, and must also consider the particularities of autonomy, charge and battery degradation of the BEVs. In this work, a scheme that coordinates the routing, scheduling of charge and operating costs of BEV...

  13. Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

    Science.gov (United States)

    2016-01-01

    PANAMA CITY DIVISION PANAMA CITY, FLORIDA 32407-7001 5(3257...task. This paper is outlined as follows: in Section 2, we discuss the general setup of the the MCM scheduling problem, including the definition of the...Suite 1425 Arlington, VA 22203-1995 Naval Surface Warfare Center, Panama City Division 1 ATTN: Technical Library 110 Vernon Avenue Panama City, FL 32407 27

  14. Decoupling algorithms from schedules for easy optimization of image processing pipelines

    OpenAIRE

    Adams, Andrew; Paris, Sylvain; Levoy, Marc; Ragan-Kelley, Jonathan Millar; Amarasinghe, Saman P.; Durand, Fredo

    2012-01-01

    Using existing programming tools, writing high-performance image processing code requires sacrificing readability, portability, and modularity. We argue that this is a consequence of conflating what computations define the algorithm, with decisions about storage and the order of computation. We refer to these latter two concerns as the schedule, including choices of tiling, fusion, recomputation vs. storage, vectorization, and parallelism. We propose a representation for feed-forward imagi...

  15. Optimal Power Transmission of Offshore Wind Power Using a VSC-HVdc Interconnection

    Directory of Open Access Journals (Sweden)

    Miguel E. Montilla-DJesus

    2017-07-01

    Full Text Available High-voltage dc transmission based on voltage-source converter (VSC-HVdc is quickly increasing its power rating, and it can be the most appropriate link for the connection of offshore wind farms (OWFs to the grid in many locations. This paper presents a steady-state operation model to calculate the optimal power transmission of an OWF connected to the grid through a VSC-HVdc link. The wind turbines are based on doubly fed induction generators (DFIGs, and a detailed model of the internal OWF grid is considered in the model. The objective of the optimization problem is to maximize the active power output of the OWF, i.e., the reduction of losses, by considering the optimal reactive power allocation while taking into account the restrictions imposed by the available wind power, the reactive power capability of the DFIG, the DC link model, and the operating conditions. Realistic simulations are performed to evaluate the proposed model and to execute optimal operation analyses. The results show the effectiveness of the proposed method and demonstrate the advantages of using the reactive control performed by DFIG to achieve the optimal operation of the VSC-HVdc.

  16. Optimization of a neutron transmission beamline applied to materials science for the CAB linear accelerator

    International Nuclear Information System (INIS)

    Ramirez, S; Santisteban, J.R

    2009-01-01

    The Neutrons and Reactors Laboratory (NYR) of CAB (Centro Atomico Bariloche) is equipped with a linear electron accelerator (LINAC - Linear particle accelerator). This LINAC is used as a neutron source from which two beams are extracted to perform neutron transmission and dispersion experiments. Through these experiments, structural and dynamic properties of materials can be studied. The neutron transmission experiments consist in a collimated neutron beam which interacts with a sample and a detector behind the sample. Important information about the microstructural characteristics of the material can be obtained from the comparison between neutron spectra before and after the interaction with the sample. In the NYR Laboratory, cylindrical samples of one inch of diameter have been traditionally studied. Nonetheless, there is a great motivation for doing systematic research on smaller and with different geometries samples; particularly sheets and samples for tensile tests. Hence, in the NYR Laboratory it has been considered the possibility of incorporating a neutron guide into the existent transmission line. According to all mentioned above, the main objective of this work consisted in the optimization of the flight transmission tube optics of neutrons. This optimization not only improved the existent line but also contributed to an election criterion for the neutron guide acquisition. [es

  17. Optimization of the scheduled maintenance on the power units of the nuclear power plants with WWER

    International Nuclear Information System (INIS)

    Skalozubov, V.I.; Kovrizhkin, Yu.L.; Kolykhanov, V.N.; Kochneva, V.Yu.; Urbanskij, V.V.

    2008-01-01

    The advanced international and domestic experience in the field of the maintenance optimization of the power units of NPPs, as well, as on the base of the planning optimization, the maintenance organization and carrying out, the technical maintenance and repair control system automatization, the testing and monitoring optimization during the service process, the modernization of the technology and technical tools of the maintenance service and control is represented

  18. A Biobjective Optimization Model for Deadline Satisfaction in Line-of-Balance Scheduling with Work Interruptions Consideration

    Directory of Open Access Journals (Sweden)

    Xin Zou

    2018-01-01

    Full Text Available The line-of-balance (LOB technique has demonstrated many advantages in scheduling repetitive projects, one of which is that it allows more than one crew to be hired by an activity concurrently. The deadline satisfaction problem in LOB scheduling (DSPLOB aims to find an LOB schedule such that the project is completed within a given deadline and the total number of crews is minimized. Previous studies required a strict application of crew work continuity, which may lead to a decline in the competitiveness of solutions. This paper introduces work interruptions into the DSPLOB and presents a biobjective optimization model that can balance the two conflicting objectives of minimizing the total number of crews and maximizing work continuity. An efficient version of the ϵ-constraint method is customized to find all feasible tradeoff solutions. Then, these solutions are further improved by an automated procedure to reduce the number of interruptions for each activity without deteriorating the performance in both the objectives. The effectiveness and practicability of the proposed model are verified using a considerable number of instances. The results show that introducing work interruptions provides more flexibility in reducing the total number of crews under the LOB framework, especially for serial projects with a tight deadline constraint.

  19. Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem

    International Nuclear Information System (INIS)

    Shayeghi, H.; Mahdavi, M.; Bagheri, A.

    2010-01-01

    Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e. expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using discrete particle swarm optimization (DPSO) algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. The proposed idea has been tested on the Garvers network and an actual transmission network of the Azerbaijan regional electric company, Iran, and the results are compared with the decimal codification genetic algorithm (DCGA) technique. The results evaluation shows that the network will possess maximum efficiency economically. Also, it is shown that precision and convergence speed of the proposed DPSO based method for the solution of the STNEP problem is superior to DCGA approach.

  20. SimWIND: A geospatial infrastructure model for optimizing wind power generation and transmission

    International Nuclear Information System (INIS)

    Phillips, Benjamin R.; Middleton, Richard S.

    2012-01-01

    Wind is a clean, enduring energy resource with the capacity to satisfy 20% or more of U.S. electricity demand. Presently, wind potential is limited by a paucity of electrical transmission lines and/or capacity between promising wind resources and primary load centers. We present the model SimWIND to address this shortfall. SimWIND is an integrated optimization model for the geospatial arrangement and cost minimization of wind-power generation–transmission–delivery infrastructure. Given a set of possible wind-farm sites, the model simultaneously determines (1) where and how much power to generate and (2) where to build new transmission infrastructure and with what capacity in order to minimize the cost for delivering a targeted amount of power to load. Costs and routing of transmission lines consider geographic and social constraints as well as electricity losses. We apply our model to the Electric Reliability Council of Texas (ERCOT) Interconnection, considering scenarios that deliver up to 20 GW of new wind power. We show that SimWIND could potentially reduce ERCOT's projected ∼$5B transmission network upgrade line length and associated costs by 50%. These results suggest that SimWIND's coupled generation–transmission–delivery modeling approach could play a critical role in enhancing planning efforts and reducing costs for wind energy integration. - Highlights: ► Wind power is limited by transmission capacity between resources and demands. ► SimWIND is a coupled generation-transmission-delivery model for wind infrastructure. ► The model minimizes costs considering realistic transmission routing and networking. ► We show that SimWIND could save 50% of $5B costs for expanding the Texas grid. ► Results suggest SimWIND may play a critical role in enhancings wind planning efforts.

  1. Optimization of the heat treatment schedule for next european dipole (NED) powder in tube $Nb_{3}Sn$ strand

    CERN Document Server

    Boutboul, T; den Ouden, A; Pedrini, D; Volpini, G

    2009-01-01

    A Nb3Sn strand was successfully developed by the company SMI for Next European Dipole (NED) activity and on the basis of Powder-In-Tube (PIT) method. This strand, after the standard reaction recommended by the firm (84 h @ 675 oC), presents attractive performances as a critical current density in the non-copper part of ~ 2500 A/mm2 for 4.2 K and 12 T applied field, an effective filament diameter of ~ 50 μm and limited flux jumps at low magnetic fields. Heat treatment optimization studies are currently performed at CERN to try to optimize the strand electric abilities. For this purpose, various heat treatment schedules were already investigated with a plateau temperature as low as 625 oC. The preliminary results of these studies are summarized here.

  2. Optimal Scheduling of a Battery-Based Energy Storage System for a Microgrid with High Penetration of Renewable Sources

    DEFF Research Database (Denmark)

    Dulout, Jeremy; Hernández, Adriana Carolina Luna; Anvari-Moghaddam, Amjad

    2017-01-01

    A new scheduling method is proposed to manage efficiently the integration of renewable sources in microgrids (MGs) with energy storage systems (ESSs). The purpose of this work is to take into account the main stress factors influencing the ageing mechanisms of a battery energy storage system (BESS......) in order to make an optimal dispatch of resources in the microgrid and enhance the storage system lifetime while minimizing the cost of electric consumption. The load demand and generation profiles are derived from the analysis of consumption and renewable production (solar photovoltaic sources and wind...... turbines) of the Western Denmark electric grid. Thus, the proposed microgrid is mainly fed by renewable sources and few electricity is coming from the main grid (which helps operating costs minimization). In this respect, a cost analysis is performed to find the optimal hourly power output of the BESS...

  3. Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

    Science.gov (United States)

    He, Yaoyao; Yang, Shanlin; Xu, Qifa

    2013-07-01

    In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

  4. The optimal scheduling of decentralised co-generation plants in microgrids; Optimale Einsatzplanung von Kraft-Waerme-Kopplungsanlagen in Microgrids

    Energy Technology Data Exchange (ETDEWEB)

    Gunkel, David [TU Dresden (Germany). Lehrstuhl fuer Energiewirtschaft; Hess, Tobias; Schegner, Peter [TU Dresden (Germany). Inst. fuer Elektrische Energieversorgung und Hochspannungstechnik

    2011-07-01

    The daily operational scheduling of decentralised unit is an important optimization task of power systems. This proceeding deals with planning of small scaled co-generation power units for district heating in a microgrid. This power system can be mathematically formulated and solved by an optimization algorithm. The solution process consists of a unit commitment and dispatch. The starting unit commitment is characterised by a mixed integer nonlinear problem defining the on-off-state of all units. Subsequently, the dispatch distributes the generation requirements to every committed unit considering thermal demand. The dispatching is based on a mixed integer linear problem. Additionally, it presents a way for flexible reducing the outage reserve related to the operational condition. The given microgrid operates in an islanding mode. The method can also be applied in a grid connected model considering the possible requirements of a grid operator. (orig.)

  5. An Optimization Scheduling Model for Wind Power and Thermal Power with Energy Storage System considering Carbon Emission Trading

    Directory of Open Access Journals (Sweden)

    Huan-huan Li

    2015-01-01

    Full Text Available Wind power has the characteristics of randomness and intermittence, which influences power system safety and stable operation. To alleviate the effect of wind power grid connection and improve power system’s wind power consumptive capability, this paper took emission trading and energy storage system into consideration and built an optimization model for thermal-wind power system and energy storage systems collaborative scheduling. A simulation based on 10 thermal units and wind farms with 2800 MW installed capacity verified the correctness of the models put forward by this paper. According to the simulation results, the introduction of carbon emission trading can improve wind power consumptive capability and cut down the average coal consumption per unit of power. The introduction of energy storage system can smooth wind power output curve and suppress power fluctuations. The optimization effects achieve the best when both of carbon emission trading and energy storage system work at the same time.

  6. Radiation therapy for painful bone metastases. Aiming at optimal treatment schedules

    International Nuclear Information System (INIS)

    Murakami, Ryuji; Saito, Ryuichi; Miyazaki, Toshiyuki; Takahashi, Mutsumasa

    2001-01-01

    The purpose of this study is to evaluate the pain relief obtained by radiation therapy for painful bone metastases, with a special regard to general condition. Between June 1998 and May 2000, 54 patients with 86 painful bone metastases were treated with radiation therapy whose effects could be evaluated for a minimum period of 6 months or until death. Treatment schedules were 3 Gy/fraction/day (30-36 Gy/10-12 fractions) in usual cases (61 lesions), 4-8 Gy/fraction/day (8-20 Gy/1-5 fractions) in patients with a poor general condition (9 lesions), and 2 Gy/fraction/day (40-50 Gy/20-25 fractions) in lesions with a large radiation field (16 lesions). Complete pain relief without medication (CR) was achieved in 40 lesions (47%). Significant predictors for CR were primary site (p=0.0003), performance status (p=0.0060), pain score (p=00190), narcotic score (p<0.0001), and prognosis (p<0.0001), but no difference was found in CR among treatment schedules. No evidence of severe radiation-induced complication was seen. General condition (performance status and prognosis) has an influence on pain relief. Compared with the daily 2 Gy protocol, the daily 3 Gy protocol has the advantage of shorter treatment time. The treatment schedule should be assessed in patients with a large radiation field and/or poor general condition. Especially for the patients with poor general condition, combined pain medication should be considered. (author)

  7. Optimizing the dosing schedule of l-asparaginase improves its anti-tumor activity in breast tumor-bearing mice

    Directory of Open Access Journals (Sweden)

    Shoya Shiromizu

    2018-04-01

    Full Text Available Proliferation of acute lymphoblastic leukemic cells is nutritionally dependent on the external supply of asparagine. l-asparaginase, an enzyme hydrolyzing l-asparagine in blood, is used for treatment of acute lymphoblastic leukemic and other related blood cancers. Although previous studies demonstrated that l-asparaginase suppresses the proliferation of cultured solid tumor cells, it remains unclear whether this enzyme prevents the growth of solid tumors in vivo. In this study, we demonstrated the importance of optimizing dosing schedules for the anti-tumor activity of l-asparaginase in 4T1 breast tumor-bearing mice. Cultures of several types of murine solid tumor cells were dependent on the external supply of asparagine. Among them, we selected murine 4T1 breast cancer cells and implanted them into BALB/c female mice kept under standardized light/dark cycle conditions. The growth of 4T1 tumor cells implanted in mice was significantly suppressed by intravenous administration of l-asparaginase during the light phase, whereas its administration during the dark phase failed to show significant anti-tumor activity. Decreases in plasma asparagine levels due to the administration of l-asparaginase were closely related to the dosing time-dependency of its anti-tumor effects. These results suggest that the anti-tumor efficacy of l-asparaginase in breast tumor-bearing mice is improved by optimizing the dosing schedule. Keywords: l-asparaginase, Asparagine, Solid tumor, Chrono-pharmacotherapy

  8. Depositional features of the Middle Jurassic formation of Field N and their influence on optimal drilling schedule

    International Nuclear Information System (INIS)

    Mishina, D; Rukavishnikov, V; Belozerov, B; Bochkov, A

    2015-01-01

    The Middle Jurassic formation of Field N represented by 4 hydrodynamically connected layers (J5-6, J4, J3 and J2) contains 42% of the field STOIIP. The J2-6 formation is characterized as a gas-oil-condensate massive lithologically and tectonically screened accumulation with a gas cap (J2, J3 layers) and bottom water (J5-6 layer). Oil is predominantly in the J3 and J4 layers. There is a high risk of early gas coning from gas-bearing layers to oil producing wells determined on the basis of production test results, which can significantly decrease the life of the well. To select a more optimal drilling schedule, it is necessary to take the risk of early gas coning into account and determine distinctive features within the gas- saturated zone that can reduce it. The presence of a thick shale barrier between the J2 and J3 layers with thicknesses varying from 0 to 30 m is recognized as the beginning of a transgression cycle, and if the gas cap is only in the J2 layer, this barrier with the thickness of more than 5 m can extensively prevent early gas coning into oil producing wells. The integration of geological information represented by the probability map constructed and petrophysical information represented by the kh map provide the more precise determination of an optimal drilling schedule

  9. Optimizing the dosing schedule of l-asparaginase improves its anti-tumor activity in breast tumor-bearing mice.

    Science.gov (United States)

    Shiromizu, Shoya; Kusunose, Naoki; Matsunaga, Naoya; Koyanagi, Satoru; Ohdo, Shigehiro

    2018-04-01

    Proliferation of acute lymphoblastic leukemic cells is nutritionally dependent on the external supply of asparagine. l-asparaginase, an enzyme hydrolyzing l-asparagine in blood, is used for treatment of acute lymphoblastic leukemic and other related blood cancers. Although previous studies demonstrated that l-asparaginase suppresses the proliferation of cultured solid tumor cells, it remains unclear whether this enzyme prevents the growth of solid tumors in vivo. In this study, we demonstrated the importance of optimizing dosing schedules for the anti-tumor activity of l-asparaginase in 4T1 breast tumor-bearing mice. Cultures of several types of murine solid tumor cells were dependent on the external supply of asparagine. Among them, we selected murine 4T1 breast cancer cells and implanted them into BALB/c female mice kept under standardized light/dark cycle conditions. The growth of 4T1 tumor cells implanted in mice was significantly suppressed by intravenous administration of l-asparaginase during the light phase, whereas its administration during the dark phase failed to show significant anti-tumor activity. Decreases in plasma asparagine levels due to the administration of l-asparaginase were closely related to the dosing time-dependency of its anti-tumor effects. These results suggest that the anti-tumor efficacy of l-asparaginase in breast tumor-bearing mice is improved by optimizing the dosing schedule. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

  10. An Optimization of Manufacturing Systems using a Feedback Control Scheduling Model

    Science.gov (United States)

    Ikome, John M.; Kanakana, Grace M.

    2018-03-01

    In complex production system that involves multiple process, unplanned disruption often turn to make the entire production system vulnerable to a number of problems which leads to customer’s dissatisfaction. However, this problem has been an ongoing problem that requires a research and methods to streamline the entire process or develop a model that will address it, in contrast to this, we have developed a feedback scheduling model that can minimize some of this problem and after a number of experiment, it shows that some of this problems can be eliminated if the correct remedial actions are implemented on time.

  11. An Optimal Scheduling Algorithm with a Competitive Factor for Real-Time Systems

    Science.gov (United States)

    1991-07-29

    real - time systems in which the value of a task is proportional to its computation time. The system obtains the value of a given task if the task completes by its deadline. Otherwise, the system obtains no value for the task. When such a system is underloaded (i.e. there exists a schedule for which all tasks meet their deadlines), Dertouzos [6] showed that the earliest deadline first algorithm will achieve 100% of the possible value. We consider the case of a possibly overloaded system and present an algorithm which: 1. behaves like the earliest deadline first

  12. Uplink Packet-Data Scheduling in DS-CDMA Systems

    Science.gov (United States)

    Choi, Young Woo; Kim, Seong-Lyun

    In this letter, we consider the uplink packet scheduling for non-real-time data users in a DS-CDMA system. As an effort to jointly optimize throughput and fairness, we formulate a time-span minimization problem incorporating the time-multiplexing of different simultaneous transmission schemes. Based on simple rules, we propose efficient scheduling algorithms and compare them with the optimal solution obtained by linear programming.

  13. Optimal Allocation of Wind Turbines by Considering Transmission Security Constraints and Power System Stability

    Directory of Open Access Journals (Sweden)

    Rodrigo Palma-Behnke

    2013-01-01

    Full Text Available A novel optimization methodology consisting of finding the near optimal location of wind turbines (WTs on a planned transmission network in a secure and cost-effective way is presented on this paper. While minimizing the investment costs of WTs, the algorithm allocates the turbines so that a desired wind power energy-penetration level is reached. The optimization considers both transmission security and power system stability constraints. The results of the optimization provide regulators with a support instrument to give proper signals to WT investors, in order to achieve secure and cost effective wind power network integration. The proposal is especially aimed at countries in the initial stage of wind power development, where the WT network integration process can still be influenced by policy-makers. The proposed methodology is validated with a real power system. Obtained results are compared with those generated from a business-as-usual (BAU scenario, in which the WT network allocation is made according to existing WT projects. The proposed WT network allocation scheme not only reduces the total investment costs associated with a determined wind power energy target, but also improves power system stability.

  14. Active Power Flow Optimization of Industrial Power Supply with Regard to the Transmission Line Conductor Heating

    Directory of Open Access Journals (Sweden)

    Leyzgold D.Yu.

    2015-04-01

    Full Text Available This article studies the problem of the transmission line conductor heating effect on the active power flows optimization in the local segment of industrial power supply. The purpose is to determine the optimal generation rating of the distributed power sources, in which the power flow values will correspond to the minimum active power losses in the power supply. The timeliness is the need to define the most appropriate rated power values of distributed sources which will be connected to current industrial power supply. Basing on the model of active power flow optimization, authors formulate the description of the nonlinear transportation problem considering the active power losses depending on the transmission line conductor heating. Authors proposed a new approach to the heating model parameters definition based on allowable current loads and nominal parameters of conductors as part of the optimization problem. Analysis of study results showed that, despite the relatively small active power losses reduction to the tune 0,45% due to accounting of the conductors heating effect for the present configuration of power supply, there are significant fluctuations in the required generation rating in nodes of the network to 9,32% within seasonal changes in the outer air temperature. This fact should be taken into account when selecting the optimum power of distributed generation systems, as exemplified by an arbitrary network configuration.

  15. Ant colony optimization and event-based dynamic task scheduling and staffing for software projects

    Science.gov (United States)

    Ellappan, Vijayan; Ashwini, J.

    2017-11-01

    In programming change organizations from medium to inconceivable scale broadens, the issue of wander orchestrating is amazingly unusual and testing undertaking despite considering it a manual system. Programming wander-organizing requirements to deal with the issue of undertaking arranging and in addition the issue of human resource portion (also called staffing) in light of the way that most of the advantages in programming ventures are individuals. We propose a machine learning approach with finds respond in due order regarding booking by taking in the present arranging courses of action and an event based scheduler revives the endeavour arranging system moulded by the learning computation in perspective of the conformity in event like the begin with the Ander, the instant at what time possessions be free starting to ended errands, and the time when delegates stick together otherwise depart the wander inside the item change plan. The route toward invigorating the timetable structure by the even based scheduler makes the arranging method dynamic. It uses structure components to exhibit the interrelated surges of endeavours, slip-ups and singular all through different progression organizes and is adjusted to mechanical data. It increases past programming wander movement ask about by taking a gander at a survey based process with a one of a kind model, organizing it with the data based system for peril assessment and cost estimation, and using a choice showing stage.

  16. Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization

    Directory of Open Access Journals (Sweden)

    Qi Xu

    2012-01-01

    Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.

  17. Securing Communication via Transmission of Artificial Noise by Both Sides: Bipolar-Beamforming Optimization

    Directory of Open Access Journals (Sweden)

    Yongkai Zhou

    2013-01-01

    Full Text Available The paper considers the secure transmission in a wireless environment in which both the transmitter (Alice and the legitimate receiver (Bob send artificial noise (AN to interfere with the eavesdropper (Eve. Optimal design is analyzed in detail for this AN-by-both-side model to deal with Eve’s stochastic channel condition and random spatial distribution. Bipolar-beamforming is first proposed to jointly design Alice and Bob’s transmitting signals. By optimally assigning the transmitting antenna for Bob and allocating the power ratio between Alice’s information and the AN signal, maximum secrecy capacity can be achieved. Simulation is done to illustrate the process of bipolar-beamforming optimization. Results show that the AN-by-both-side model has good secrecy performance on both average and extreme conditions as Eve approaches Alice or Bob.

  18. Optimally Scheduling Distribution of the MH-60S Helicopter and Pilots to Combat Support (HC) Squadrons

    National Research Council Canada - National Science Library

    Culver, Cory

    2002-01-01

    ... (Optimal Transition HC Allocation Model) that minimizes lost flying days. OTHCAM takes into account variable training durations, travel times and tour lengths as well as manpower and aircraft constraints...

  19. Design and Optimization of Ultrasonic Wireless Power Transmission Links for Millimeter-Sized Biomedical Implants.

    Science.gov (United States)

    Meng, Miao; Kiani, Mehdi

    2017-02-01

    Ultrasound has been recently proposed as an alternative modality for efficient wireless power transmission (WPT) to biomedical implants with millimeter (mm) dimensions. This paper presents the theory and design methodology of ultrasonic WPT links that involve mm-sized receivers (Rx). For given load (R L ) and powering distance (d), the optimal geometries of transmitter (Tx) and Rx ultrasonic transducers, including their diameter and thickness, as well as the optimal operation frequency (f c ) are found through a recursive design procedure to maximize the power transmission efficiency (PTE). First, a range of realistic f c s is found based on the Rx thickness constrain. For a chosen f c within the range, the diameter and thickness of the Rx transducer are then swept together to maximize PTE. Then, the diameter and thickness of the Tx transducer are optimized to maximize PTE. Finally, this procedure is repeated for different f c s to find the optimal f c and its corresponding transducer geometries that maximize PTE. A design example of ultrasonic link has been presented and optimized for WPT to a 1 mm 3 implant, including a disk-shaped piezoelectric transducer on a silicon die. In simulations, a PTE of 2.11% at f c of 1.8 MHz was achieved for R L of 2.5 [Formula: see text] at [Formula: see text]. In order to validate our simulations, an ultrasonic link was optimized for a 1 mm 3 piezoelectric transducer mounted on a printed circuit board (PCB), which led to simulated and measured PTEs of 0.65% and 0.66% at f c of 1.1 MHz for R L of 2.5 [Formula: see text] at [Formula: see text], respectively.

  20. Anatomy-based transmission factors for technique optimization in portable chest x-ray

    Science.gov (United States)

    Liptak, Christopher L.; Tovey, Deborah; Segars, William P.; Dong, Frank D.; Li, Xiang

    2015-03-01

    Portable x-ray examinations often account for a large percentage of all radiographic examinations. Currently, portable examinations do not employ automatic exposure control (AEC). To aid in the design of a size-specific technique chart, acrylic slabs of various thicknesses are often used to estimate x-ray transmission for patients of various body thicknesses. This approach, while simple, does not account for patient anatomy, tissue heterogeneity, and the attenuation properties of the human body. To better account for these factors, in this work, we determined x-ray transmission factors using computational patient models that are anatomically realistic. A Monte Carlo program was developed to model a portable x-ray system. Detailed modeling was done of the x-ray spectrum, detector positioning, collimation, and source-to-detector distance. Simulations were performed using 18 computational patient models from the extended cardiac-torso (XCAT) family (9 males, 9 females; age range: 2-58 years; weight range: 12-117 kg). The ratio of air kerma at the detector with and without a patient model was calculated as the transmission factor. Our study showed that the transmission factor decreased exponentially with increasing patient thickness. For the range of patient thicknesses examined (12-28 cm), the transmission factor ranged from approximately 21% to 1.9% when the air kerma used in the calculation represented an average over the entire imaging field of view. The transmission factor ranged from approximately 21% to 3.6% when the air kerma used in the calculation represented the average signals from two discrete AEC cells behind the lung fields. These exponential relationships may be used to optimize imaging techniques for patients of various body thicknesses to aid in the design of clinical technique charts.

  1. An optimal control strategies using vaccination and fogging in dengue fever transmission model

    Science.gov (United States)

    Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan

    2017-08-01

    This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging optimally, the number of vector can be minimized. In this case, we considered the giving of vaccination as a control variable because it is one of the efforts that are being developed to reduce the spreading of dengue fever. We used Pontryagin Minimum Principle to solve the optimal control problem. Furthermore, the numerical simulation results are given to show the effect of the optimal control strategies in order to minimize the epidemic of dengue fever.

  2. GTZ: a fast compression and cloud transmission tool optimized for FASTQ files.

    Science.gov (United States)

    Xing, Yuting; Li, Gen; Wang, Zhenguo; Feng, Bolun; Song, Zhuo; Wu, Chengkun

    2017-12-28

    The dramatic development of DNA sequencing technology is generating real big data, craving for more storage and bandwidth. To speed up data sharing and bring data to computing resource faster and cheaper, it is necessary to develop a compression tool than can support efficient compression and transmission of sequencing data onto the cloud storage. This paper presents GTZ, a compression and transmission tool, optimized for FASTQ files. As a reference-free lossless FASTQ compressor, GTZ treats different lines of FASTQ separately, utilizes adaptive context modelling to estimate their characteristic probabilities, and compresses data blocks with arithmetic coding. GTZ can also be used to compress multiple files or directories at once. Furthermore, as a tool to be used in the cloud computing era, it is capable of saving compressed data locally or transmitting data directly into cloud by choice. We evaluated the performance of GTZ on some diverse FASTQ benchmarks. Results show that in most cases, it outperforms many other tools in terms of the compression ratio, speed and stability. GTZ is a tool that enables efficient lossless FASTQ data compression and simultaneous data transmission onto to cloud. It emerges as a useful tool for NGS data storage and transmission in the cloud environment. GTZ is freely available online at: https://github.com/Genetalks/gtz .

  3. Capacity optimization and scheduling of a multiproduct manufacturing facility for biotech products.

    Science.gov (United States)

    Shaik, Munawar A; Dhakre, Ankita; Rathore, Anurag S; Patil, Nitin

    2014-01-01

    A general mathematical framework has been proposed in this work for scheduling of a multiproduct and multipurpose facility involving manufacturing of biotech products. The specific problem involves several batch operations occurring in multiple units involving fixed processing time, unlimited storage policy, transition times, shared units, and deterministic and fixed data in the given time horizon. The different batch operations are modeled using state-task network representation. Two different mathematical formulations are proposed based on discrete- and continuous-time representations leading to a mixed-integer linear programming model which is solved using General Algebraic Modeling System software. A case study based on a real facility is presented to illustrate the potential and applicability of the proposed models. The continuous-time model required less number of events and has a smaller problem size compared to the discrete-time model. © 2014 American Institute of Chemical Engineers.

  4. Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S.; Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2010-08-15

    This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We propose a novel mixed-integer nonlinear programming (MINLP) approach, considering hydroelectric power generation as a nonlinear function of water discharge and of the head. As a new contribution to earlier studies, we model the on-off behavior of the hydro plants using integer variables, in order to avoid water discharges at forbidden areas. Thus, an enhanced STHS is provided due to the more realistic modeling presented in this paper. Our approach has been applied successfully to solve a test case based on one of the Portuguese cascaded hydro systems with a negligible computational time requirement. (author)

  5. Research on hybrid transmission mode for HVDC with optimal thermal power and renewable energy combination

    Science.gov (United States)

    Zhang, Jinfang; Yan, Xiaoqing; Wang, Hongfu

    2018-02-01

    With the rapid development of renewable energy in Northwest China, curtailment phenomena is becoming more and more serve owing to lack of adjustment ability and enough transmission capacity. Based on the existing HVDC projects, exploring the hybrid transmission mode associated with thermal power and renewable power will be necessary and important. This paper has proposed a method on optimal thermal power and renewable energy combination for HVDC lines, based on multi-scheme comparison. Having established the mathematic model for electric power balance in time series mode, ten different schemes have been picked for figuring out the suitable one by test simulation. By the proposed related discriminated principle, including generation device utilization hours, renewable energy electricity proportion and curtailment level, the recommendation scheme has been found. The result has also validated the efficiency of the method.

  6. Optimized Signaling Method for High-Speed Transmission Channels with Higher Order Transfer Function

    Science.gov (United States)

    Ševčík, Břetislav; Brančík, Lubomír; Kubíček, Michal

    2017-08-01

    In this paper, the selected results from testing of optimized CMOS friendly signaling method for high-speed communications over cables and printed circuit boards (PCBs) are presented and discussed. The proposed signaling scheme uses modified concept of pulse width modulated (PWM) signal which enables to better equalize significant channel losses during data high-speed transmission. Thus, the very effective signaling method to overcome losses in transmission channels with higher order transfer function, typical for long cables and multilayer PCBs, is clearly analyzed in the time and frequency domain. Experimental results of the measurements include the performance comparison of conventional PWM scheme and clearly show the great potential of the modified signaling method for use in low power CMOS friendly equalization circuits, commonly considered in modern communication standards as PCI-Express, SATA or in Multi-gigabit SerDes interconnects.

  7. A new global particle swarm optimization for the economic emission dispatch with or without transmission losses

    International Nuclear Information System (INIS)

    Zou, Dexuan; Li, Steven; Li, Zongyan; Kong, Xiangyong

    2017-01-01

    Highlights: • A new global particle swarm optimization (NGPSO) is proposed. • NGPSO has strong convergence and desirable accuracy. • NGPSO is used to handle the economic emission dispatch with or without transmission losses. • The equality constraint can be satisfied by solving a quadratic equation. • The inequality constraints can be satisfied by using penalty function method. - Abstract: A new global particle swarm optimization (NGPSO) algorithm is proposed to solve the economic emission dispatch (EED) problems in this paper. NGPSO is different from the traditional particle swarm optimization (PSO) algorithm in two aspects. First, NGPSO uses a new position updating equation which relies on the global best particle to guide the searching activities of all particles. Second, it uses the randomization based on the uniform distribution to slightly disturb the flight trajectories of particles during the late evolutionary process. The two steps enable NGPSO to effectively execute a number of global searches, and thus they increase the chance of exploring promising solution space, and reduce the probabilities of getting trapped into local optima for all particles. On the other hand, the two objective functions of EED are normalized separately according to all candidate solutions, and then they are incorporated into one single objective function. The transformation steps are very helpful in eliminating the difference caused by the different dimensions of the two functions, and thus they strike a balance between the fuel cost and emission. In addition, a simple and common penalty function method is employed to facilitate the satisfactions of EED’s constraints. Based on these improvements in PSO, objective functions and constraints handling, high-quality solutions can be obtained for EED problems. Five examples are chosen to testify the performance of three improved PSOs on solving EED problems with or without transmission losses. Experimental results show that

  8. Optimization scheduling in intelligent Energy Management System for the DC residential distribution system

    DEFF Research Database (Denmark)

    Yue, Jingpeng; Hu, Zhijian; Li, Chendan

    2017-01-01

    Smart DC residential distribution system(RDS) consisted by DC living homes will be a significant integral part in the future green transmission with demand flexibility. Meanwhile, the distributed generations will play an important role in the active demand response (DR). Energy Management System...... (EMS) with aid of the wireless communication and the smart meter is imperative in achieving ADR for DC residential community. This paper presents a framework of centralized management system integration and the key process of ADR in DC residential distribution system. The propose framework and methods...

  9. Portfolio optimization using Mixture Design of Experiments. Scheduling trades within electricity markets

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury [Federal Univ. of Itajuba, Minas Gerais (Brazil)

    2011-01-15

    Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)

  10. Optimal resource allocation and load scheduling for a multi-commodity smart energy system

    NARCIS (Netherlands)

    Blaauwbroek, N.; Nguyen, H.P.; Shi, H.; Kamphuis, I.G.; Kling, W.L.; Konsman, M.J.

    2015-01-01

    The increasing introduction of district heating systems together with hybrid energy appliances as heat pumps and micro-combined heat and power installations, results in new opportunities for optimizing the available resources in multi-commodity smart energy systems, including electricity, heat and

  11. Portfolio optimization using Mixture Design of Experiments. Scheduling trades within electricity markets

    International Nuclear Information System (INIS)

    Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury

    2011-01-01

    Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)

  12. Optimal Power Scheduling for a Grid-Connected Hybrid PV-Wind-Battery Microgrid System

    DEFF Research Database (Denmark)

    Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi

    2016-01-01

    mathematical model, wherein the cost of energy purchased from the main grid is minimized and profits for selling energy generated by photovoltaic arrays are maximized by considering both physical constraints and requirements for a feasible deployment in the real system. The optimization model is tested...

  13. Design optimization of the transmission system for electric vehicles considering the dynamic efficiency of the regenerative brake

    NARCIS (Netherlands)

    Zhao, Bolin; Lv, Chen; Hofman, Theo; Steinbuch, Maarten; Zhang, Junzhi; Cao, Dongpu

    2018-01-01

    In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the

  14. Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm

    Directory of Open Access Journals (Sweden)

    P. Balachennaiah

    2016-06-01

    Full Text Available This paper proposes a Firefly algorithm based technique to optimize the control variables for simultaneous optimization of real power loss and voltage stability limit of the transmission system. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained optimization problem with an objective function integrating both real power loss and voltage stability limit. Transformers taps, unified power flow controller and its parameters have been included as control variables in the problem formulation. The effectiveness of the proposed algorithm has been tested on New England 39-bus system. Simulation results obtained with the proposed algorithm are compared with the real coded genetic algorithm for single objective of real power loss minimization and multi-objective of real power loss minimization and voltage stability limit maximization. Also, a classical optimization method known as interior point successive linear programming technique is considered here to compare the results of firefly algorithm for single objective of real power loss minimization. Simulation results confirm the potentiality of the proposed algorithm in solving optimization problems.

  15. Optimal estimation and scheduling in aquifer management using the rapid feedback control method

    Science.gov (United States)

    Ghorbanidehno, Hojat; Kokkinaki, Amalia; Kitanidis, Peter K.; Darve, Eric

    2017-12-01

    Management of water resources systems often involves a large number of parameters, as in the case of large, spatially heterogeneous aquifers, and a large number of "noisy" observations, as in the case of pressure observation in wells. Optimizing the operation of such systems requires both searching among many possible solutions and utilizing new information as it becomes available. However, the computational cost of this task increases rapidly with the size of the problem to the extent that textbook optimization methods are practically impossible to apply. In this paper, we present a new computationally efficient technique as a practical alternative for optimally operating large-scale dynamical systems. The proposed method, which we term Rapid Feedback Controller (RFC), provides a practical approach for combined monitoring, parameter estimation, uncertainty quantification, and optimal control for linear and nonlinear systems with a quadratic cost function. For illustration, we consider the case of a weakly nonlinear uncertain dynamical system with a quadratic objective function, specifically a two-dimensional heterogeneous aquifer management problem. To validate our method, we compare our results with the linear quadratic Gaussian (LQG) method, which is the basic approach for feedback control. We show that the computational cost of the RFC scales only linearly with the number of unknowns, a great improvement compared to the basic LQG control with a computational cost that scales quadratically. We demonstrate that the RFC method can obtain the optimal control values at a greatly reduced computational cost compared to the conventional LQG algorithm with small and controllable losses in the accuracy of the state and parameter estimation.

  16. Optimal estimation of spatially variable recharge and transmissivity fields under steady-state groundwater flow. Part 2. Case study

    Science.gov (United States)

    Graham, Wendy D.; Neff, Christina R.

    1994-05-01

    The first-order analytical solution of the inverse problem for estimating spatially variable recharge and transmissivity under steady-state groundwater flow, developed in Part 1 is applied to the Upper Floridan Aquifer in NE Florida. Parameters characterizing the statistical structure of the log-transmissivity and head fields are estimated from 152 measurements of transmissivity and 146 measurements of hydraulic head available in the study region. Optimal estimates of the recharge, transmissivity and head fields are produced throughout the study region by conditioning on the nearest 10 available transmissivity measurements and the nearest 10 available head measurements. Head observations are shown to provide valuable information for estimating both the transmissivity and the recharge fields. Accurate numerical groundwater model predictions of the aquifer flow system are obtained using the optimal transmissivity and recharge fields as input parameters, and the optimal head field to define boundary conditions. For this case study, both the transmissivity field and the uncertainty of the transmissivity field prediction are poorly estimated, when the effects of random recharge are neglected.

  17. Optimization of Training Signal Transmission for Estimating MIMO Channel under Antenna Mutual Coupling Conditions

    Directory of Open Access Journals (Sweden)

    Xia Liu

    2010-01-01

    Full Text Available This paper reports investigations on the effect of antenna mutual coupling on performance of training-based Multiple-Input Multiple-Output (MIMO channel estimation. The influence of mutual coupling is assessed for two training-based channel estimation methods, Scaled Least Square (SLS and Minimum Mean Square Error (MMSE. It is shown that the accuracy of MIMO channel estimation is governed by the sum of eigenvalues of channel correlation matrix which in turn is influenced by the mutual coupling in transmitting and receiving array antennas. A water-filling-based procedure is proposed to optimize the training signal transmission to minimize the MIMO channel estimation errors.

  18. An Integrated and Optimal Joint Scheduling of Energy Resources to Feed Electrical, Thermal and Potable Water Demands in Remote Area

    Directory of Open Access Journals (Sweden)

    R. Ghaffarpour

    2016-12-01

    Full Text Available The continuous spread of distributed energy resources (DERs such as combined heating and power (CHP, diesel generators, boilers and renewable energy sources provide an effective solution to energy related problems to serve the power and heat demands with minimum cost. Moreover, the DERs may play a significant role for supplying power and heat in rural areas, where grid electricity is not available. Also, some dry areas may face water scarcity and salinity problems. So, one important solution is the use of DERs to drive desalination units in order to solve water scarcity and salinity problems. In this study, the optimal scheduling of DERs and reverse osmosis (RO desalination unit that feed the required electric, thermal and potable water demands are determined. The present paper describes the operation constraints and cost function of components of the system in detail. Operation constraints of generation units as well as feasible region of operation CHP in dual dependency characteristic are taken into account. To confirm the performance of the proposed model the approach is tested on a realistic remote area over a 24-h period. The results show that the economical scheduling of DERs and desalination units can be obtained using proposed methodology by implementing the proposed formulation.

  19. A Scheduling Model for the Re-entrant Manufacturing System and Its Optimization by NSGA-II

    Directory of Open Access Journals (Sweden)

    Masoud Rabbani

    2016-11-01

    Full Text Available In this study, a two-objective mixed-integer linear programming model (MILP for multi-product re-entrant flow shop scheduling problem has been designed. As a result, two objectives are considered. One of them is maximization of the production rate and the other is the minimization of processing time. The system has m stations and can process several products in a moment. The re-entrant flow shop scheduling problem is well known as NP-hard problem and its complexity has been discussed by several researchers. Given that NSGA-II algorithm is one of the strongest and most applicable algorithm in solving multi-objective optimization problems, it is used to solve this problem. To increase algorithm performance, Taguchi technique is used to design experiments for algorithm’s parameters. Numerical experiments are proposed to show the efficiency and effectiveness of the model. Finally, the results of NSGA-II are compared with SPEA2 algorithm (Strength Pareto Evolutionary Algorithm 2. The experimental results show that the proposed algorithm performs significantly better than the SPEA2.

  20. A Flexible Job Shop Scheduling Problem with Controllable Processing Times to Optimize Total Cost of Delay and Processing

    Directory of Open Access Journals (Sweden)

    Hadi Mokhtari

    2015-11-01

    Full Text Available In this paper, the flexible job shop scheduling problem with machine flexibility and controllable process times is studied. The main idea is that the processing times of operations may be controlled by consumptions of additional resources. The purpose of this paper to find the best trade-off between processing cost and delay cost in order to minimize the total costs. The proposed model, flexible job shop scheduling with controllable processing times (FJCPT, is formulated as an integer non-linear programming (INLP model and then it is converted into an integer linear programming (ILP model. Due to NP-hardness of FJCPT, conventional analytic optimization methods are not efficient. Hence, in order to solve the problem, a Scatter Search (SS, as an efficient metaheuristic method, is developed. To show the effectiveness of the proposed method, numerical experiments are conducted. The efficiency of the proposed algorithm is compared with that of a genetic algorithm (GA available in the literature for solving FJSP problem. The results showed that the proposed SS provide better solutions than the existing GA.

  1. Trade-off between learning and exploitation: the Pareto-optimal versus evolutionarily stable learning schedule in cumulative cultural evolution.

    Science.gov (United States)

    Wakano, Joe Yuichiro; Miura, Chiaki

    2014-02-01

    Inheritance of culture is achieved by social learning and improvement is achieved by individual learning. To realize cumulative cultural evolution, social and individual learning should be performed in this order in one's life. However, it is not clear whether such a learning schedule can evolve by the maximization of individual fitness. Here we study optimal allocation of lifetime to learning and exploitation in a two-stage life history model under a constant environment. We show that the learning schedule by which high cultural level is achieved through cumulative cultural evolution is unlikely to evolve as a result of the maximization of individual fitness, if there exists a trade-off between the time spent in learning and the time spent in exploiting the knowledge that has been learned in earlier stages of one's life. Collapse of a fully developed culture is predicted by a game-theoretical analysis where individuals behave selfishly, e.g., less learning and more exploiting. The present study suggests that such factors as group selection, the ability of learning-while-working ("on the job training"), or environmental fluctuation might be important in the realization of rapid and cumulative cultural evolution that is observed in humans. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. An Improved Version of Discrete Particle Swarm Optimization for Flexible Job Shop Scheduling Problem with Fuzzy Processing Time

    Directory of Open Access Journals (Sweden)

    Song Huang

    2016-01-01

    Full Text Available The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF. In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.

  3. A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

    Science.gov (United States)

    Moghaddam, Kamran S.; Usher, John S.

    2011-07-01

    In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

  4. An Adaptive Procedure for Task Scheduling Optimization in Mobile Cloud Computing

    Directory of Open Access Journals (Sweden)

    Pham Phuoc Hung

    2015-01-01

    Full Text Available Nowadays, mobile cloud computing (MCC has emerged as a new paradigm which enables offloading computation-intensive, resource-consuming tasks up to a powerful computing platform in cloud, leaving only simple jobs to the capacity-limited thin client devices such as smartphones, tablets, Apple’s iWatch, and Google Glass. However, it still faces many challenges due to inherent problems of thin clients, especially the slow processing and low network connectivity. So far, a number of research studies have been carried out, trying to eliminate these problems, yet few have been found efficient. In this paper, we present an enhanced architecture, taking advantage of collaboration of thin clients and conventional desktop or laptop computers, known as thick clients, particularly aiming at improving cloud access. Additionally, we introduce an innovative genetic approach for task scheduling such that the processing time is minimized, while considering network contention and cloud cost. Our simulation shows that the proposed approach is more cost-effective and achieves better performance compared with others.

  5. Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization.

    Science.gov (United States)

    Chung, Sai Ho; Ma, Hoi Lam; Chan, Hing Kai

    2017-08-01

    This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era. © 2016 Society for Risk Analysis.

  6. Modeling Optimal Scheduling for Pumping System to Minimize Operation Cost and Enhance Operation Reliability

    Directory of Open Access Journals (Sweden)

    Yin Luo

    2012-01-01

    Full Text Available Traditional pump scheduling models neglect the operation reliability which directly relates with the unscheduled maintenance cost and the wear cost during the operation. Just for this, based on the assumption that the vibration directly relates with the operation reliability and the degree of wear, it could express the operation reliability as the normalization of the vibration level. The characteristic of the vibration with the operation point was studied, it could be concluded that idealized flow versus vibration plot should be a distinct bathtub shape. There is a narrow sweet spot (80 to 100 percent BEP to obtain low vibration levels in this shape, and the vibration also follows similar law with the square of the rotation speed without resonance phenomena. Then, the operation reliability could be modeled as the function of the capacity and rotation speed of the pump and add this function to the traditional model to form the new. And contrast with the tradition method, the result shown that the new model could fix the result produced by the traditional, make the pump operate in low vibration, then the operation reliability could increase and the maintenance cost could decrease.

  7. A short-term scheduling for the optimal operation of biorefineries

    International Nuclear Information System (INIS)

    Grisi, E.F.; Yusta, J.M.; Khodr, H.M.

    2011-01-01

    This work presents an analysis of the inherent potentialities and characteristics of the sugarcane industries that produce sugar, bioethanol, biogas and bioelectricity and that are being described as ''Biorefineries''. These Biorefineries are capable of producing bio-energy under diverse forms, intended for their own internal consumption and for external sales and marketing. A complex model and simulation are carried out of the processes of a sugarcane industry, with the data characteristic as well as the production costs, prices of products and considerations on the energy demand by basic processes. A Mixed-Integer Linear Programming (MILP) optimization problem formulation and an analysis of optimal solutions in short-term operation are described, taking into account the production cost functions of each commodity and the incomes obtained from selling electricity and other products. The objective is to maximize the hourly plant economic profit in the different scenarios considered in a real case study. (author)

  8. Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

    Science.gov (United States)

    2013-12-01

    with 214 turbines [22]. In July 2011, the DoD declared that a complete study of 217 wind farm projects proposed in 35 states and Puerto Rico found...14. SUBJECT TERMS Hybrid electric grid , Microgrid , Hybrid renewable energy system , energy management center, optimization, Day...electric grid. In the case of a hybrid electric grid (HEG), or hybrid renewable energy system (HRES) where the microgrid can be connected to the commercial

  9. Optimal LNG (liquefied natural gas) regasification scheduling for import terminals with storage

    International Nuclear Information System (INIS)

    Trotter, Ian M.; Gomes, Marília Fernandes Maciel; Braga, Marcelo José; Brochmann, Bjørn; Lie, Ole Nikolai

    2016-01-01

    We describe a stochastic dynamic programming model for maximising the revenue generated by regasification of LNG (liquefied natural gas) from storage tanks at importation terminals in relation to a natural gas spot market. We present three numerical resolution strategies: a posterior optimal strategy, a rolling intrinsic strategy and a full option strategy based on a least-squares Monte Carlo algorithm. We then compare model simulation results to the observed behaviour of three LNG importation terminals in the UK for the period April 2011 to April 2012, and find that there was low correlation between the observed regasification decisions of the operators and those suggested by the three simulated strategies. However, the actions suggested by the model simulations would have generated significantly higher revenues, suggesting that the facilities might have been operated sub-optimally. A further numerical experiment shows that increasing the storage and regasification capacities of a facility can significantly increase the achievable revenue, even without altering the amount of LNG received, by allowing operators more flexibility to defer regasification. - Highlights: • We present a revenue maximisation model for LNG (liquefied natural gas) storage tanks at import terminals. • Three resolution strategies: posterior optimal, rolling intrinsic and full option. • The full option strategy is based on a least-squares Monte Carlo algorithm. • Model simulations show potential for higher revenue in three UK LNG terminals. • Numerical experiments show how storage and regasification capacities affect revenue.

  10. Efficiency optimization of wireless power transmission systems for active capsule endoscopes.

    Science.gov (United States)

    Zhiwei, Jia; Guozheng, Yan; Jiangpingping; Zhiwu, Wang; Hua, Liu

    2011-10-01

    Multipurpose active capsule endoscopes have drawn considerable attention in recent years, but these devices continue to suffer from energy limitations. A wireless power supply system is regarded as a practical way to overcome the power shortage problem in such devices. This paper focuses on the efficiency optimization of a wireless energy supply system with size and safety constraints. A mathematical programming model in which these constraints are considered is proposed for transmission efficiency, optimal frequency and current, and overall system effectiveness. To verify the feasibility of the proposed method, we use a wireless active capsule endoscope as an illustrative example. The achieved efficiency can be regarded as an index for evaluating the system, and the proposed approach can be used to direct the design of transmitting and receiving coils.

  11. Efficiency optimization of wireless power transmission systems for active capsule endoscopes

    International Nuclear Information System (INIS)

    Zhiwei, Jia; Guozheng, Yan; Jiangpingping; Zhiwu, Wang; Hua, Liu

    2011-01-01

    Multipurpose active capsule endoscopes have drawn considerable attention in recent years, but these devices continue to suffer from energy limitations. A wireless power supply system is regarded as a practical way to overcome the power shortage problem in such devices. This paper focuses on the efficiency optimization of a wireless energy supply system with size and safety constraints. A mathematical programming model in which these constraints are considered is proposed for transmission efficiency, optimal frequency and current, and overall system effectiveness. To verify the feasibility of the proposed method, we use a wireless active capsule endoscope as an illustrative example. The achieved efficiency can be regarded as an index for evaluating the system, and the proposed approach can be used to direct the design of transmitting and receiving coils

  12. Design and Optimization of a 3-Coil Inductive Link for Efficient Wireless Power Transmission.

    Science.gov (United States)

    Kiani, Mehdi; Jow, Uei-Ming; Ghovanloo, Maysam

    2011-07-14

    Inductive power transmission is widely used to energize implantable microelectronic devices (IMDs), recharge batteries, and energy harvesters. Power transfer efficiency (PTE) and power delivered to the load (PDL) are two key parameters in wireless links, which affect the energy source specifications, heat dissipation, power transmission range, and interference with other devices. To improve the PTE, a 4-coil inductive link has been recently proposed. Through a comprehensive circuit based analysis that can guide a design and optimization scheme, we have shown that despite achieving high PTE at larger coil separations, the 4-coil inductive links fail to achieve a high PDL. Instead, we have proposed a 3-coil inductive power transfer link with comparable PTE over its 4-coil counterpart at large coupling distances, which can also achieve high PDL. We have also devised an iterative design methodology that provides the optimal coil geometries in a 3-coil inductive power transfer link. Design examples of 2-, 3-, and 4-coil inductive links have been presented, and optimized for 13.56 MHz carrier frequency and 12 cm coupling distance, showing PTEs of 15%, 37%, and 35%, respectively. At this distance, the PDL of the proposed 3-coil inductive link is 1.5 and 59 times higher than its equivalent 2- and 4-coil links, respectively. For short coupling distances, however, 2-coil links remain the optimal choice when a high PDL is required, while 4-coil links are preferred when the driver has large output resistance or small power is needed. These results have been verified through simulations and measurements.

  13. Does Operation Scheduling Make a Difference: Tapping the Potential of Optimized Design for Skipping-Stop Strategy in Reducing Bus Emissions

    Directory of Open Access Journals (Sweden)

    Xumei Chen

    2017-09-01

    Full Text Available The idea of corporate social responsibility has promoted bus operation agencies to rethink how to provide not only efficient but also environmentally friendly services for residents. A study on the potential of using an optimized design of skip-stop services, one of the essential operational strategies in practice, to reduce emissions is conducted in this paper. The underlying scheduling problem is formulated as a nonlinear programming problem with the primary objective of optimizing the total costs for both passengers and operating agencies, as well as with the secondary objective of minimizing bus emissions. A solution method is developed to solve the problem. A real-world case of Route 16 in Beijing is studied, in which the optimal scheduling strategy that maximizes the cost savings and environmental benefits is determined. The costs and emissions of the proposed scheduling strategy are compared with the optimal scheduling with skip-stop services without considering bus emissions. The results show that the proposed scheduling strategy outperforms the other operating strategy with respect to operational costs and bus emissions. A sensitivity study is then conducted to investigate the impact of the fleet size in operations and passenger demand on the effectiveness of the proposed stop-skipping strategy considering bus emissions.

  14. Load Sharing Multiobjective Optimization Design of a Split Torque Helicopter Transmission

    Directory of Open Access Journals (Sweden)

    Chenxi Fu

    2015-01-01

    Full Text Available Split torque designs can offer significant advantages over the traditional planetary designs for helicopter transmissions. However, it has two unique properties, gap and phase differences, which result in the risk of unequal load sharing. Various methods have been proposed to eliminate the effect of gap and promote load sharing to a certain extent. In this paper, system design parameters will be optimized to change the phase difference, thereby further improving load sharing. A nonlinear dynamic model is established to measure the load sharing with dynamic mesh forces quantitatively. Afterwards, a multiobjective optimization of a reference split torque design is conducted with the promoting of load sharing property, lightweight, and safety considered as the objectives. The load sharing property, which is measured by load sharing coefficient, is evaluated under multiple operating conditions with dynamic analysis method. To solve the multiobjective model with NSGA-II, an improvement is done to overcome the problem of time consuming. Finally, a satisfied optimal solution is picked up as the final design from the Pareto optimal front, which achieves improvements in all the three objectives compared with the reference design.

  15. Optimization of a pressure control valve for high power automatic transmission considering stability

    Science.gov (United States)

    Jian, Hongchao; Wei, Wei; Li, Hongcai; Yan, Qingdong

    2018-02-01

    The pilot-operated electrohydraulic clutch-actuator system is widely utilized by high power automatic transmission because of the demand of large flowrate and the excellent pressure regulating capability. However, a self-excited vibration induced by the inherent non-linear characteristics of valve spool motion coupled with the fluid dynamics can be generated during the working state of hydraulic systems due to inappropriate system parameters, which causes sustaining instability in the system and leads to unexpected performance deterioration and hardware damage. To ensure a stable and fast response performance of the clutch actuator system, an optimal design method for the pressure control valve considering stability is proposed in this paper. A non-linear dynamic model of the clutch actuator system is established based on the motion of the valve spool and coupling fluid dynamics in the system. The stability boundary in the parameter space is obtained by numerical stability analysis. Sensitivity of the stability boundary and output pressure response time corresponding to the valve parameters are identified using design of experiment (DOE) approach. The pressure control valve is optimized using particle swarm optimization (PSO) algorithm with the stability boundary as constraint. The simulation and experimental results reveal that the optimization method proposed in this paper helps in improving the response characteristics while ensuring the stability of the clutch actuator system during the entire gear shift process.

  16. Energy Optimization for Distributed Energy Resources Scheduling with Enhancements in Voltage Stability Margin

    DEFF Research Database (Denmark)

    Morais, Hugo; Sousa, Tiago; Perez, Angel

    2016-01-01

    to evaluate the resulting multiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programming methodology. With this new methodology, the system operators can consider both the costs and voltage stability. Priority can be assigned to one objective function according...... to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks. One detailed case study considering a distribution network with high penetration of distributed energy...

  17. Optimal control of the gear shifting process for shift smoothness in dual-clutch transmissions

    Science.gov (United States)

    Li, Guoqiang; Görges, Daniel

    2018-03-01

    The control of the transmission system in vehicles is significant for the driving comfort. In order to design a controller for smooth shifting and comfortable driving, a dynamic model of a dual-clutch transmission is presented in this paper. A finite-time linear quadratic regulator is proposed for the optimal control of the two friction clutches in the torque phase for the upshift process. An integral linear quadratic regulator is introduced to regulate the relative speed difference between the engine and the slipping clutch under the optimization of the input torque during the inertia phase. The control objective focuses on smoothing the upshift process so as to improve the driving comfort. Considering the available sensors in vehicles for feedback control, an observer design is presented to track the immeasurable variables. Simulation results show that the jerk can be reduced both in the torque phase and inertia phase, indicating good shift performance. Furthermore, compared with conventional controllers for the upshift process, the proposed control method can reduce shift jerk and improve shift quality.

  18. Optimized scheduling technique of null subcarriers for peak power control in 3GPP LTE downlink.

    Science.gov (United States)

    Cho, Soobum; Park, Sang Kyu

    2014-01-01

    Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system.

  19. Optimal Scheduling of an Regional Integrated Energy System with Energy Storage Systems for Service Regulation

    Directory of Open Access Journals (Sweden)

    Hengrui Ma

    2018-01-01

    Full Text Available Ancillary services are critical to maintaining the safe and stable operation of power systems that contain a high penetration level of renewable energy resources. As a high-quality regulation resource, the regional integrated energy system (RIES with energy storage system (ESS can effectively adjust the non-negligible frequency offset caused by the renewable energy integration into the power system, and help solve the problem of power system frequency stability. In this paper, the optimization model aiming at regional integrated energy system as a participant in the regulation market based on pay-for-performance is established. Meanwhile YALMIP + CPLEX is used to simulate and analyze the total operating cost under different dispatch modes. This paper uses the actual operation model of the PJM regulation market to guide the optimal allocation of regulation resource in the regional integrated energy system, and provides a balance between the power trading revenue and regulation market revenue in order to achieve the maximum profit.

  20. Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

    Science.gov (United States)

    Tang, Haijing; Wang, Siye; Zhang, Yanjun

    2013-01-01

    Clustering has become a common trend in very long instruction words (VLIW) architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC) VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC) VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file. PMID:23970841

  1. Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

    Directory of Open Access Journals (Sweden)

    Haijing Tang

    2013-01-01

    Full Text Available Clustering has become a common trend in very long instruction words (VLIW architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file.

  2. A transmission power optimization with a minimum node degree for energy-efficient wireless sensor networks with full-reachability.

    Science.gov (United States)

    Chen, Yi-Ting; Horng, Mong-Fong; Lo, Chih-Cheng; Chu, Shu-Chuan; Pan, Jeng-Shyang; Liao, Bin-Yih

    2013-03-20

    Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments.

  3. A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability

    Science.gov (United States)

    Chen, Yi-Ting; Horng, Mong-Fong; Lo, Chih-Cheng; Chu, Shu-Chuan; Pan, Jeng-Shyang; Liao, Bin-Yih

    2013-01-01

    Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments. PMID:23519351

  4. Two-Stage Optimal Scheduling of Electric Vehicle Charging based on Transactive Control

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Ma, Kang

    2018-01-01

    In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time EV charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day......-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modelled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model...... predictive control (MPC) based method is proposed for the aggregator to clear the transactive market. The realtime charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the realtime requests and preferences of the EV owners. As such, the aggregators...

  5. Optimization Scheduling Model for Wind-thermal Power System Considering the Dynamic penalty factor

    Science.gov (United States)

    PENG, Siyu; LUO, Jianchun; WANG, Yunyu; YANG, Jun; RAN, Hong; PENG, Xiaodong; HUANG, Ming; LIU, Wanyu

    2018-03-01

    In this paper, a new dynamic economic dispatch model for power system is presented.Objective function of the proposed model presents a major novelty in the dynamic economic dispatch including wind farm: introduced the “Dynamic penalty factor”, This factor could be computed by using fuzzy logic considering both the variable nature of active wind power and power demand, and it could change the wind curtailment cost according to the different state of the power system. Case studies were carried out on the IEEE30 system. Results show that the proposed optimization model could mitigate the wind curtailment and the total cost effectively, demonstrate the validity and effectiveness of the proposed model.

  6. An improved DPSO with mutation based on similarity algorithm for optimization of transmission lines loading

    International Nuclear Information System (INIS)

    Shayeghi, H.; Mahdavi, M.; Bagheri, A.

    2010-01-01

    Static transmission network expansion planning (STNEP) problem acquires a principal role in power system planning and should be evaluated carefully. Up till now, various methods have been presented to solve the STNEP problem. But only in one of them, lines adequacy rate has been considered at the end of planning horizon and the problem has been optimized by discrete particle swarm optimization (DPSO). DPSO is a new population-based intelligence algorithm and exhibits good performance on solution of the large-scale, discrete and non-linear optimization problems like STNEP. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In order to overcome these drawbacks and considering lines adequacy rate, in this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using an improved DPSO algorithm. The proposed improved DPSO is a new conception, collectivity, which is based on similarity between the particle and the current global best particle in the swarm that can prevent the premature convergence of DPSO around the local solution. The proposed method has been tested on the Garver's network and a real transmission network in Iran, and compared with the DPSO based method for solution of the TNEP problem. The results show that the proposed improved DPSO based method by preventing the premature convergence is caused that with almost the same expansion costs, the network adequacy is increased considerably. Also, regarding the convergence curves of both methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than DPSO approach.

  7. An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems

    International Nuclear Information System (INIS)

    Guerra, Omar J.; Tejada, Diego A.; Reklaitis, Gintaras V.

    2016-01-01

    Highlights: • A novel optimization framework for the design and planning of interconnected power systems is proposed. • The framework integrates generation and transmission capacity expansion planning. • Reserve and emission constraints are included. • Business as usual and CO_2 mitigation policy scenarios are evaluated. • Reconfiguration of existing power generation technologies is the most cost-effective option for CO_2 emissions mitigation. - Abstract: Energy, and particularly electricity, has played and will continue to play a very important role in the development of human society. Electricity, which is the most flexible and manageable energy form, is currently used in a variety of activities and applications. For instance, electricity is used for heating, cooling, lighting, and for operating electronic appliances and electric vehicles. Nowadays, given the rapid development and commercialization of technologies and devices that rely on electricity, electricity demand is increasing faster than overall primary energy supply. Consequently, the design and planning of power systems is becoming a progressively more important issue in order to provide affordable, reliable and sustainable energy in timely fashion, not only in developed countries but particularly in developing economies where electricity demand is increasing even faster. Power systems are networks of electrical devices, such as power plants, transformers, and transmission lines, used to produce, transmit, and supply electricity. The design and planning of such systems require the selection of generation technologies, along with the capacity, location, and timing of generation and transmission capacity expansions to meet electricity demand over a long-term horizon. This manuscript presents a comprehensive optimization framework for the design and planning of interconnected power systems, including the integration of generation and transmission capacity expansion planning. The proposed

  8. Optimal allocation of multi-state retransmitters in acyclic transmission networks

    International Nuclear Information System (INIS)

    Levitin, Gregory

    2002-01-01

    In this paper, an algorithm for optimal allocation of multi-state elements (MEs) in acyclic transmission networks (ATNs) is suggested. The ATNs consist of a number of positions (nodes) in which MEs capable of receiving and sending a signal are allocated. Each network has a root position where the signal source is located, a number of leaf positions that can only receive a signal, and a number of intermediate positions containing MEs capable of transmitting the received signal to some other nodes. Each ME that is located in a nonleaf node can have different states determined by a set of nodes receiving the signal directly from this ME. The probability of each state is assumed to be known for each ME. The ATN reliability is defined as the probability that a signal from the root node is transmitted to each leaf node. The optimal distribution of MEs with different characteristics among ATN positions provides the greatest possible ATN reliability. The suggested algorithm is based on using a universal generating function technique for network reliability evaluation. A genetic algorithm is used as the optimization tool. Illustrative examples are presented

  9. Scheduling optimization of a real-world multi product pipeline network; Otimizacao das operacoes de transporte de derivados de petroleo em redes de dutos

    Energy Technology Data Exchange (ETDEWEB)

    Boschetto, Suelen N.; Felizari, Luiz C.; Magatao, Leandro; Stebel, Sergio L.; Neves Junior, Flavio; Lueders, Ricardo; Arruda, Lucia V.R. de [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil); Ribas, Paulo Cesar; Bernardo, Luiz F.J. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2008-07-01

    This work develops an optimization structure to aid the operational decision-making of scheduling activities in a real world pipeline network. The proposed approach is based on a decomposition method to address complex problems with high computational burden. The Pre-analysis makes a previous evaluation of a batch sequencing, getting information to be entered into optimization block. The continuous time Mixed Integer Linear Program (MILP) model gets such information and calculates the scheduling. The models are applied to a pipeline network that connects different areas including refineries, terminals, and final clients. Many oil derivatives (e.g. gasoline, liquefied petroleum gas, naphtha) can be sent or received in this network. The computational burden to determine a short-term scheduling within the considered scenario is a relevant issue. Many insights have been derived from the obtained solutions, which are given in a reduced computational time for oil industrial-size scenarios. (author)

  10. On Multiple Users Scheduling Using Superposition Coding over Rayleigh Fading Channels

    KAUST Repository

    Zafar, Ammar

    2013-02-20

    In this letter, numerical results are provided to analyze the gains of multiple users scheduling via superposition coding with successive interference cancellation in comparison with the conventional single user scheduling in Rayleigh blockfading broadcast channels. The information-theoretic optimal power, rate and decoding order allocation for the superposition coding scheme are considered and the corresponding histogram for the optimal number of scheduled users is evaluated. Results show that at optimality there is a high probability that only two or three users are scheduled per channel transmission block. Numerical results for the gains of multiple users scheduling in terms of the long term throughput under hard and proportional fairness as well as for fixed merit weights for the users are also provided. These results show that the performance gain of multiple users scheduling over single user scheduling increases when the total number of users in the network increases, and it can exceed 10% for high number of users

  11. Erratum to ''Johnson's algorithm : A key to solve optimally or approximately flowshop scheduling problems with unavailability periods'' [International Journal of Production Economics 121 (2009) 81-87

    OpenAIRE

    Rapine , Christophe

    2013-01-01

    International audience; In Allaoui H., Artiba A, ''Johnson's algorithm : A key to solve optimally or approximately flowshop scheduling problems with unavailability periods'' [International Journal of Production Economics 121 (2009)] the authors propose optimality conditions for the Johnson sequence in presence of one unavailability period on the first machine and pretend for a performance guarantee of 2 when several unavailability periods may occur. We establish in this note that these condit...

  12. Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems

    International Nuclear Information System (INIS)

    Luo, Yugong; Zhu, Tao; Wan, Shuang; Zhang, Shuwei; Li, Keqiang

    2016-01-01

    The widespread use of electric vehicles (EVs) is becoming an imminent trend. Research has been done on the scheduling of EVs from the perspective of the charging characteristic, improvement in the safety and economy of the power grid, or the traffic jams in the transport system caused by a large number of EVs driven to charging stations. There is a lack of systematic studies considering EVs, the power grid, and the transport system all together. In this paper, a novel optimal charging scheduling strategy for different types of EVs is proposed based on not only transport system information, such as road length, vehicle velocity and waiting time, but also grid system information, such as load deviation and node voltage. In addition, a charging scheduling simulation platform suitable for large-scale EV deployment is developed based on actual charging scenarios. The simulation results show that the improvements in both the transport system efficiency and the grid system operation can be obtained by using the optimal strategy, such as the node voltage drop is decreased, the power loss is reduced, and the load curve is optimized. - Highlights: • A novel optimal charging scheduling strategy is proposed for different electric vehicles (EVs). • A simulation platform suitable for large-scale EV deployment is established. • The traffic congestion near the charging and battery-switch stations is relieved. • The safety and economy problems of the distribution network are solved. • The peak-to-valley load of the distribution system is reduced.

  13. Optimized transmission-line impedance transformers for petawatt-class pulsed-power accelerators

    Directory of Open Access Journals (Sweden)

    D. R. Welch

    2008-03-01

    Full Text Available We have developed 1D analytic and 2D fully electromagnetic models of radial transmission-line impedance transformers. The models have been used to quantify the power-transport efficiency and pulse sharpening of such transformers as a function of voltage pulse width and impedance profile. For the cases considered, we find that in the limit as Γ→0 (where Γ is the ratio of the pulse width to the one-way transit time of the transformer, the transport efficiency is maximized when the impedance profile is exponential. As Γ increases from zero, the optimum profile gradually deviates from an exponential. A numerical procedure is presented that determines the optimum profile for a given pulse shape and width. The procedure can be applied to optimize the design of impedance transformers used in petawatt-class pulsed-power accelerators.

  14. Modeling and optimization of nonreciprocal transmission through 2D magnetophotonic crystal

    Energy Technology Data Exchange (ETDEWEB)

    Vanwolleghem, M; Halagacka, L; Magdenko, L; Beavillain, P; Dagens, B [Institut d' Electronique Fondamentale, UMR CNRS 8622, Universite Paris-Sud, Orsay (France); Postava, K, E-mail: mathias.vanwolleghem@u-psud.fr, E-mail: kamil.postava@vsb.cz [Department of Physics, Technical University of Ostrava, 708 33 Ostrava (Czech Republic)

    2011-07-06

    A combination of unique magneto-optic (MO) non-reciprocity and photonic band gap in periodic structures is promising for efficient enhancement of optical isolation and integrated isolator applications [M. Vanwolleghem et al, Phys. Rev. B 80 (2009) 121102(R)]. In this paper we model and optimize a novel magneto-photonic crystal structure consisting air holes in transparent magneto-optic material in transverse geometry (Bismuth iron garnet ({epsilon}{sub xx} = 6.25 and {epsilon}{sub yz} = 0.1 i) at wavelength {lambda} = 1300 nm). Such a system with reduced symmetry shows an unidirectional bandgap. The model is based on plane wave Fourier expansion of the field inside the periodic system using RCWA. While in the forward direction the structure transmit the light in the backward direction it shows a band gap and transmission is almost forbidden.

  15. Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

    Directory of Open Access Journals (Sweden)

    Santanu Biswas

    Full Text Available Visceral leishmaniasis (VL is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

  16. Optimal resonance configuration for ultrasonic wireless power transmission to millimeter-sized biomedical implants.

    Science.gov (United States)

    Miao Meng; Kiani, Mehdi

    2016-08-01

    In order to achieve efficient wireless power transmission (WPT) to biomedical implants with millimeter (mm) dimensions, ultrasonic WPT links have recently been proposed. Operating both transmitter (Tx) and receiver (Rx) ultrasonic transducers at their resonance frequency (fr) is key in improving power transmission efficiency (PTE). In this paper, different resonance configurations for Tx and Rx transducers, including series and parallel resonance, have been studied to help the designers of ultrasonic WPT links to choose the optimal resonance configuration for Tx and Rx that maximizes PTE. The geometries for disk-shaped transducers of four different sets of links, operating at series-series, series-parallel, parallel-series, and parallel-parallel resonance configurations in Tx and Rx, have been found through finite-element method (FEM) simulation tools for operation at fr of 1.4 MHz. Our simulation results suggest that operating the Tx transducer with parallel resonance increases PTE, while the resonance configuration of the mm-sized Rx transducer highly depends on the load resistance, Rl. For applications that involve large Rl in the order of tens of kΩ, a parallel resonance for a mm-sized Rx leads to higher PTE, while series resonance is preferred for Rl in the order of several kΩ and below.

  17. Smart house-based optimal operation of thermal unit commitment for a smart grid considering transmission constraints

    Science.gov (United States)

    Howlader, Harun Or Rashid; Matayoshi, Hidehito; Noorzad, Ahmad Samim; Muarapaz, Cirio Celestino; Senjyu, Tomonobu

    2018-05-01

    This paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.

  18. Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid

    International Nuclear Information System (INIS)

    Petrollese, Mario; Valverde, Luis; Cocco, Daniele; Cau, Giorgio; Guerra, José

    2016-01-01

    Highlights: • Energy management strategy for a renewable hydrogen-based microgrid. • Integration of optimal generation scheduling with a model predictive control. • Experimental tests are carried out simulating typical summer and winter days. • Effective improvement in performance and reduction in microgrid operating cost are achieved. - Abstract: This paper presents a novel control strategy for the optimal management of microgrids with high penetration of renewable energy sources and different energy storage systems. The control strategy is based on the integration of optimal generation scheduling with a model predictive control in order to achieve both long and short-term optimal planning. In particular, long-term optimization of the various microgrid components is obtained by the adoption of an optimal generation scheduling, in which a statistical approach is used to take into account weather and load forecasting uncertainties. The real-time management of the microgrid is instead entrusted to a model predictive controller, which has the important feature of using the results obtained by the optimal generation scheduling. The proposed control strategy was tested in a laboratory-scale microgrid present at the University of Seville, which is composed of an electronic power source that emulates a photovoltaic system, a battery bank and a hydrogen production and storage system. Two different experimental tests that simulate a summer and a winter day were carried out over a 24-h period to verify the reliability and performance enhancement of the control system. Results show an effective improvement in performance in terms of reduction of the microgrid operating cost and greater involvement of the hydrogen storage system for the maintenance of a spinning reserve in batteries.

  19. Efficient Load Scheduling Method For Power Management

    Directory of Open Access Journals (Sweden)

    Vijo M Joy

    2015-08-01

    Full Text Available An efficient load scheduling method to meet varying power supply needs is presented in this paper. At peak load times the power generation system fails due to its instability. Traditionally we use load shedding process. In load shedding process disconnect the unnecessary and extra loads. The proposed method overcomes this problem by scheduling the load based on the requirement. Artificial neural networks are used for this optimal load scheduling process. For generate economic scheduling artificial neural network has been used because generation of power from each source is economically different. In this the total load required is the inputs of this network and the power generation from each source and power losses at the time of transmission are the output of the neural network. Training and programming of the artificial neural networks are done using MATLAB.

  20. Optimal wireless receiver structure for omnidirectional inductive power transmission to biomedical implants.

    Science.gov (United States)

    Gougheri, Hesam Sadeghi; Kiani, Mehdi

    2016-08-01

    In order to achieve omnidirectional inductive power transmission to biomedical implants, the use of several orthogonal coils in the receiver side (Rx) has been proposed in the past. In this paper, the optimal Rx structure for connecting three orthogonal Rx coils and the power management is found to achieve the maximum power delivered to the load (PDL) in the presence of any Rx coil tilting. Unlike previous works, in which a separate power management has been used for each coil to deliver power to the load, different resonant Rx structures for connecting three Rx coils to a single power management are studied. In simulations, connecting three Rx coils with the diameters of 3 mm, 3.3 mm, and 3.6 mm in series and resonating them with a single capacitor at the operation frequency of 100 MHz led to the maximum PDL for large loads when the implant was tilted for 45o. This optimal Rx structure achieves higher PDL in worst-case scenarios as well as reduces the number of power managements to only one.

  1. Joint opportunistic scheduling and network coding for bidirectional relay channel

    KAUST Repository

    Shaqfeh, Mohammad

    2013-07-01

    In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users\\' transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited. © 2013 IEEE.

  2. Does high optimism protect against the inter-generational transmission of high BMI? The Cardiovascular Risk in Young Finns Study.

    Science.gov (United States)

    Serlachius, Anna; Pulkki-Råback, Laura; Juonala, Markus; Sabin, Matthew; Lehtimäki, Terho; Raitakari, Olli; Elovainio, Marko

    2017-09-01

    The transmission of overweight from one generation to the next is well established, however little is known about what psychosocial factors may protect against this familial risk. The aim of this study was to examine whether optimism plays a role in the intergenerational transmission of obesity. Our sample included 1043 participants from the prospective Cardiovascular Risk in Young FINNS Study. Optimism was measured in early adulthood (2001) when the cohort was aged 24-39years. BMI was measured in 2001 (baseline) and 2012 when they were aged 35-50years. Parental BMI was measured in 1980. Hierarchical linear regression and logistic regression were used to examine the association between optimism and future BMI/obesity, and whether an interaction existed between optimism and parental BMI when predicting BMI/obesity 11years later. High optimism in young adulthood demonstrated a negative relationship with high BMI in mid-adulthood, but only in women (β=-0.127, p=0.001). The optimism×maternal BMI interaction term was a significant predictor of future BMI in women (β=-0.588, p=0.036). The logistic regression results confirmed that high optimism predicted reduced obesity in women (OR=0.68, 95% CI, 0.55-0.86), however the optimism × maternal obesity interaction term was not a significant predictor (OR=0.50, 95% CI, 0.10-2.48). Our findings supported our hypothesis that high optimism mitigated the intergenerational transmission of high BMI, but only in women. These findings also provided evidence that positive psychosocial factors such as optimism are associated with long-term protective effects on BMI in women. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Optimal planning of the Nordic transmission system with 100% electric vehicle penetration of passenger cars by 2050

    International Nuclear Information System (INIS)

    Graabak, Ingeborg; Wu, Qiuwei; Warland, Leif; Liu, Zhaoxi

    2016-01-01

    This paper presents the optimal planning of the Nordic backbone transmission system with 100% electric vehicle penetration of passenger cars by 2050. Electric vehicles will play an important role in the future energy systems and can reduce the greenhouse gas emission from the transport sector. However, the electric vehicles will increase the electricity consumption and might induce congestions in the transmission systems. In order to deal with the electricity consumption increase from the electric vehicle integration into the power system and maximize the social welfare, the optimal investments of the Nordic transmission system are studied. Case studies were conducted using the market simulation model EMPS (Efi's multi-area power market simulator) and two electric vehicle charging scenarios: a spot price based scenario and a dumb charging scenario. The electric vehicle charging power is assumed to be 3.68 kW with 1 phase 16 A. The complete electrification of the private passenger fleet increases the yearly power demand in the Nordic region with ca 7.5%. The profitable increases in transmission capacities are highest for dumb charging, but are very low for both dumb and spot price based charging compared to a Reference case. - Highlights: • The electric vehicle distribution is done using population and car statistics. • The 100% penetration electric vehicle demand is obtained for Nordic countries. • The optimal investments in the Nordic transmission system with electric vehicles are studied.

  4. A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response

    International Nuclear Information System (INIS)

    Ju, Liwei; Tan, Zhongfu; Yuan, Jinyun; Tan, Qingkun; Li, Huanhuan; Dong, Fugui

    2016-01-01

    Highlights: • Our research focuses on Virtual Power Plant (VPP). • Virtual Power Plant consists of WPP, PV, CGT, ESSs and DRPs. • Robust optimization theory is introduced to analyze uncertainties. • A bi-level stochastic scheduling optimization model is proposed for VPP. • Models are built to measure the impacts of ESSs and DERPs on VPP operation. - Abstract: To reduce the uncertain influence of wind power and solar photovoltaic power on virtual power plant (VPP) operation, robust optimization theory (ROT) is introduced to build a stochastic scheduling model for VPP considering the uncertainty, price-based demand response (PBDR) and incentive-based demand response (IBDR). First, the VPP components are described including the wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs) and demand resource providers (DRPs). Then, a scenario generation and reduction frame is proposed for analyzing and simulating output stochastics based on the interval method and the Kantorovich distance. Second, a bi-level robust scheduling model is proposed with a double robust coefficient for WPP and PV. In the upper layer model, the maximum VPP operation income is taken as the optimization objective for building the scheduling model with the day-ahead prediction output of WPP and PV. In the lower layer model, the day-ahead scheduling scheme is revised with the actual output of the WPP and PV under the objectives of the minimum system net load and the minimum system operation cost. Finally, the independent micro-grid in a coastal island in eastern China is used for the simulation analysis. The results illustrate that the model can overcome the influence of uncertainty on VPP operations and reduce the system power shortage cost by connecting the day-ahead scheduling with the real-time scheduling. ROT could provide a flexible decision tool for decision makers, effectively addressing system uncertainties. ESSs could

  5. Transmission characteristics and optimal diagnostic samples to detect an FMDV infection in vaccinated and non-vaccinated sheep

    NARCIS (Netherlands)

    Eble, P.L.; Orsel, K.; Kluitenberg-van Hemert, F.; Dekker, A.

    2015-01-01

    We wanted to quantify transmission of FMDV Asia-1 in sheep and to evaluate which samples would be optimal for detection of an FMDV infection in sheep. For this, we used 6 groups of 4 non-vaccinated and 6 groups of 4 vaccinated sheep. In each group 2 sheep were inoculated and contact exposed to 2

  6. Optimal Planning of the Nordic Transmission System with 100% Electric Vehicle Penetration of passenger cars by 2050

    DEFF Research Database (Denmark)

    Graabak, Ingeborg; Wu, Qiuwei; Warland, Leif

    2016-01-01

    This paper presents the optimal planning of the Nordic backbone transmission system with 100% electric vehicle penetration of passenger cars by 2050. Electric vehicles will play an important role in the future energy systems and can reduce the greenhouse gas emission from the transport sector....... However, the electric vehicles will increase the electricity consumption and might induce congestions in the transmission systems. In order to deal with the electricity consumption increase from the electric vehicle integration into the power system and maximize the social welfare, the optimal investments...... of the Nordic transmission system are studied. Case studies were conducted using the market simulation model EMPS and two electric vehicle charging scenarios: a spot price based scenario and a dumb charging scenario. The electric vehicle charging power is assumed to be 3.68 kW with 1 phase 16A. The complete...

  7. A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

    Directory of Open Access Journals (Sweden)

    Fanrong Kong

    2017-09-01

    Full Text Available To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

  8. Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

    International Nuclear Information System (INIS)

    Mohanta, Dusmanta Kumar; Sadhu, Pradip Kumar; Chakrabarti, R.

    2007-01-01

    This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power

  9. 设备维修最优化调度策略方法研究%Equipment Repair Optimization Scheduling Strategy Approach

    Institute of Scientific and Technical Information of China (English)

    张宏铭

    2014-01-01

    Under the condition of information technology, wartime equipment repair optimization scheduling problem is a key problem in the process of repair security. In this paper, according to the PSO algorithm based model is proposed for the wartime equipment repair security scheduling strategy, maximize the wartime repair support system effectiveness. Im-proved the PSO algorithm, solved the local optimization algorithm, compared with the FCFS repair security scheduling strat-egy, The simulation experiments show that the POS algorithm to scheduling performance is improved obviously.%信息化条件下,战时装备维修优化调度问题是装备维修保障过程中的关键问题。本文根据PSO算法建立模型提出了战时装备维修保障调度策略,最大限度的提高战时维修保障系统的效能,同时对PSO算法进行改进,解决算法中的局部最优化问题,最后与基于FCFS算法的维修保障调度策略进行对比,通过仿真实验证明PSO算法对调度性能有明显改善。

  10. Optimal estimation of spatially variable recharge and transmissivity fields under steady-state groundwater flow. Part 1. Theory

    Science.gov (United States)

    Graham, Wendy D.; Tankersley, Claude D.

    1994-05-01

    Stochastic methods are used to analyze two-dimensional steady groundwater flow subject to spatially variable recharge and transmissivity. Approximate partial differential equations are developed for the covariances and cross-covariances between the random head, transmissivity and recharge fields. Closed-form solutions of these equations are obtained using Fourier transform techniques. The resulting covariances and cross-covariances can be incorporated into a Bayesian conditioning procedure which provides optimal estimates of the recharge, transmissivity and head fields given available measurements of any or all of these random fields. Results show that head measurements contain valuable information for estimating the random recharge field. However, when recharge is treated as a spatially variable random field, the value of head measurements for estimating the transmissivity field can be reduced considerably. In a companion paper, the method is applied to a case study of the Upper Floridan Aquifer in NE Florida.

  11. Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity.

    Science.gov (United States)

    Costa, Rui Ponte; Padamsey, Zahid; D'Amour, James A; Emptage, Nigel J; Froemke, Robert C; Vogels, Tim P

    2017-09-27

    Long-term modifications of neuronal connections are critical for reliable memory storage in the brain. However, their locus of expression-pre- or postsynaptic-is highly variable. Here we introduce a theoretical framework in which long-term plasticity performs an optimization of the postsynaptic response statistics toward a given mean with minimal variance. Consequently, the state of the synapse at the time of plasticity induction determines the ratio of pre- and postsynaptic modifications. Our theory explains the experimentally observed expression loci of the hippocampal and neocortical synaptic potentiation studies we examined. Moreover, the theory predicts presynaptic expression of long-term depression, consistent with experimental observations. At inhibitory synapses, the theory suggests a statistically efficient excitatory-inhibitory balance in which changes in inhibitory postsynaptic response statistics specifically target the mean excitation. Our results provide a unifying theory for understanding the expression mechanisms and functions of long-term synaptic transmission plasticity. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  12. Optimal Design of Wireless Power Transmission Links for Millimeter-Sized Biomedical Implants.

    Science.gov (United States)

    Ahn, Dukju; Ghovanloo, Maysam

    2016-02-01

    This paper presents a design methodology for RF power transmission to millimeter-sized implantable biomedical devices. The optimal operating frequency and coil geometries are found such that power transfer efficiency (PTE) and tissue-loss-constrained allowed power are maximized. We define receiver power reception susceptibility (Rx-PRS) and transmitter figure of merit (Tx-FoM) such that their multiplication yields the PTE. Rx-PRS and Tx-FoM define the roles of the Rx and Tx in the PTE, respectively. First, the optimal Rx coil geometry and operating frequency range are identified such that the Rx-PRS is maximized for given implant constraints. Since the Rx is very small and has lesser design freedom than the Tx, the overall operating frequency is restricted mainly by the Rx. Rx-PRS identifies such operating frequency constraint imposed by the Rx. Secondly, the Tx coil geometry is selected such that the Tx-FoM is maximized under the frequency constraint at which the Rx-PRS was saturated. This aligns the target frequency range of Tx optimization with the frequency range at which Rx performance is high, resulting in the maximum PTE. Finally, we have found that even in the frequency range at which the PTE is relatively flat, the tissue loss per unit delivered power can be significantly different for each frequency. The Rx-PRS can predict the frequency range at which the tissue loss per unit delivered power is minimized while PTE is maintained high. In this way, frequency adjustment for the PTE and tissue-loss-constrained allowed power is realized by characterizing the Rx-PRS. The design procedure was verified through full-wave electromagnetic field simulations and measurements using de-embedding method. A prototype implant, 1 mm in diameter, achieved PTE of 0.56% ( -22.5 dB) and power delivered to load (PDL) was 224 μW at 200 MHz with 12 mm Tx-to-Rx separation in the tissue environment.

  13. Multiuser Scheduling on the Downlink of an LTE Cellular System

    Directory of Open Access Journals (Sweden)

    Raymond Kwan

    2008-01-01

    Full Text Available The challenge of scheduling user transmissions on the downlink of a long-term evolution (LTE cellular communication system is addressed. In particular, a novel optimalmultiuser scheduler is proposed. Numerical results show that the system performance improves with increasing correlation among OFDMA subcarriers. It is found that only a limited amount of feedback information is needed to achieve relatively good performance. A suboptimal reduced-complexity scheduler is also proposed and shown to provide good performance. The suboptimal scheme is especially attractive when the number of users is large, in which case the complexity of the optimal scheme is high.

  14. Technical Report: Optimizing the Slab Yard Planning and Crane Scheduling Problem using a Two-Stage Approach

    DEFF Research Database (Denmark)

    Hansen, Anders Dohn; Clausen, Jens

    2008-01-01

    In this paper, we present The Slab Yard Planning and Crane Scheduling Problem. The problem has its origin in steel production facilities with a large throughput. A slab yard is used as a buffer for slabs that are needed in the upcoming production. Slabs are transported by cranes and the problem...... considered here, is concerned with the generation of schedules for these. The problem is decomposed and modeled in two parts, namely a planning problem and a scheduling problem. In the planning problem a set of crane operations is created to take the yard from its current state to a desired goal state...... schedule for the cranes is generated, where each operation is assigned to a crane and is given a specific time of initiation. For both models, a thorough description of the modeling details is given along with a specification of objective criteria. Variants of the models are presented as well. Preliminary...

  15. Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

    International Nuclear Information System (INIS)

    Zhou, Jianzhong; Lu, Peng; Li, Yuanzheng; Wang, Chao; Yuan, Liu; Mo, Li

    2016-01-01

    Highlights: • HTWCS system is established while considering uncertainty of wind power. • An enhanced multi-objective bee colony optimization algorithm is proposed. • Some heuristic repairing strategies are designed to handle various constraints. • HTWCS problem with economic/environment objectives is solved by EMOBCO. - Abstract: This paper presents a short-term economic/environmental hydro-thermal-wind complementary scheduling (HTWCS) system considering uncertainty of wind power, as well as various complicated non-linear constraints. HTWCS system is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., economic and environmental criteria. Then an enhanced multi-objective bee colony optimization algorithm (EMOBCO) is proposed to solve this problem, which adopts Elite archive set, adaptive mutation/selection mechanism and local searching strategy to improve global searching ability of standard bee colony optimization (BCO). Especially, a novel constraints-repairing strategy with compressing decision space and a violation-adjustment method are used to handle various hydraulic and electric constraints. Finally, a daily scheduling simulation case of hydro-thermal-wind system is conducted to verify feasibility and effectiveness of the proposed EMOBCO in solving HTWCS problem. The simulation results indicate that the proposed EMOBCO can provide lower economic cost and smaller pollutant emission than other method established recently while considering various complex constraints in HTWCS problem.

  16. Evaluation and Verification of Channel Transmission Characteristics of Human Body for Optimizing Data Transmission Rate in Electrostatic-Coupling Intra Body Communication System: A Comparative Analysis.

    Directory of Open Access Journals (Sweden)

    Yuhwai Tseng

    Full Text Available Intra-body communication is a new wireless scheme for transmitting signals through the human body. Understanding the transmission characteristics of the human body is therefore becoming increasingly important. Electrostatic-coupling intra-body communication system in a ground-free situation that integrate electronic products that are discretely located on individuals, such as mobile phones, PDAs, wearable computers, and biomedical sensors, are of particular interest.The human body is modeled as a simplified Resistor-Capacitor network. A virtual ground between the transmitter and receiver in the system is represented by a resister-capacitor network. Value of its resistance and capacitance are determined from a system perspective. The system is characterized by using a mathematical unit step function in digital baseband transmission scheme with and without Manchester code. As a result, the signal-to-noise and to-intersymbol-interference ratios are improved by manipulating the load resistor. The data transmission rate of the system is optimized. A battery-powered transmitter and receiver are developed to validate the proposal.A ground-free system fade signal energy especially for a low-frequency signal limited system transmission rate. The system transmission rate is maximized by simply manipulating the load resistor. Experimental results demonstrate that for a load resistance of 10k-50k Ω, the high-pass 3 dB frequency of the band-pass channel is 400kHz-2MHz in the worst-case scenario. The system allows a Manchester-coded baseband signal to be transmitted at speeds of up to 20M bit per second with signal-to-noise and signal-to-intersymbol-interference ratio of more than 10 dB.The human body can function as a high speed transmission medium with a data transmission rate of 20Mbps in an electrostatic-coupling intra-body communication system. Therefore, a wideband signal can be transmitted directly through the human body with a good signal

  17. Evaluation and Verification of Channel Transmission Characteristics of Human Body for Optimizing Data Transmission Rate in Electrostatic-Coupling Intra Body Communication System: A Comparative Analysis.

    Science.gov (United States)

    Tseng, Yuhwai; Su, Chauchin; Ho, Yingchieh

    2016-01-01

    Intra-body communication is a new wireless scheme for transmitting signals through the human body. Understanding the transmission characteristics of the human body is therefore becoming increasingly important. Electrostatic-coupling intra-body communication system in a ground-free situation that integrate electronic products that are discretely located on individuals, such as mobile phones, PDAs, wearable computers, and biomedical sensors, are of particular interest. The human body is modeled as a simplified Resistor-Capacitor network. A virtual ground between the transmitter and receiver in the system is represented by a resister-capacitor network. Value of its resistance and capacitance are determined from a system perspective. The system is characterized by using a mathematical unit step function in digital baseband transmission scheme with and without Manchester code. As a result, the signal-to-noise and to-intersymbol-interference ratios are improved by manipulating the load resistor. The data transmission rate of the system is optimized. A battery-powered transmitter and receiver are developed to validate the proposal. A ground-free system fade signal energy especially for a low-frequency signal limited system transmission rate. The system transmission rate is maximized by simply manipulating the load resistor. Experimental results demonstrate that for a load resistance of 10k-50k Ω, the high-pass 3 dB frequency of the band-pass channel is 400kHz-2MHz in the worst-case scenario. The system allows a Manchester-coded baseband signal to be transmitted at speeds of up to 20M bit per second with signal-to-noise and signal-to-intersymbol-interference ratio of more than 10 dB. The human body can function as a high speed transmission medium with a data transmission rate of 20Mbps in an electrostatic-coupling intra-body communication system. Therefore, a wideband signal can be transmitted directly through the human body with a good signal-to-noise quality of 10 dB if

  18. Flexible job-shop scheduling based on genetic algorithm and simulation validation

    Directory of Open Access Journals (Sweden)

    Zhou Erming

    2017-01-01

    Full Text Available This paper selects flexible job-shop scheduling problem as the research object, and Constructs mathematical model aimed at minimizing the maximum makespan. Taking the transmission reverse gear production line of a transmission corporation as an example, genetic algorithm is applied for flexible jobshop scheduling problem to get the specific optimal scheduling results with MATLAB. DELMIA/QUEST based on 3D discrete event simulation is applied to construct the physical model of the production workshop. On the basis of the optimal scheduling results, the logical link of the physical model for the production workshop is established, besides, importing the appropriate process parameters to make virtual simulation on the production workshop. Finally, through analyzing the simulated results, it shows that the scheduling results are effective and reasonable.

  19. Revisiting Symbiotic Job Scheduling

    OpenAIRE

    Eyerman , Stijn; Michaud , Pierre; Rogiest , Wouter

    2015-01-01

    International audience; —Symbiotic job scheduling exploits the fact that in a system with shared resources, the performance of jobs is impacted by the behavior of other co-running jobs. By coscheduling combinations of jobs that have low interference, the performance of a system can be increased. In this paper, we investigate the impact of using symbiotic job scheduling for increasing throughput. We find that even for a theoretically optimal scheduler, this impact is very low, despite the subs...

  20. [Design and optimization of wireless power and data transmission for visual prosthesis].

    Science.gov (United States)

    Lei, Xuping; Wu, Kaijie; Zhao, Lei; Chai, Xinyu

    2013-11-01

    Boosting spatial resolution of visual prostheses is an effective method to improve implant subjects' visual perception. However, power consumption of visual implants greatly rises with the increasing number of implanted electrodes. In respond to this trend, visual prostheses need to develop high-efficiency wireless power transmission and high-speed data transmission. This paper presents a review of current research progress on wireless power and data transmission for visual prostheses, analyzes relative principles and requirement, and introduces design methods of power and data transmission.

  1. Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response

    International Nuclear Information System (INIS)

    Ju, Liwei; Li, Huanhuan; Zhao, Junwei; Chen, Kangting; Tan, Qingkun; Tan, Zhongfu

    2016-01-01

    Highlights: • Our research focuses on virtual power plant. • Electric vehicle group and demand response are integrated into virtual power plant. • Stochastic chance constraint planning is applied to overcome uncertainties. • A multi-objective stochastic scheduling model is proposed for virtual power plant. • A three-stage hybrid intelligent solution algorithm is proposed for solving the model. - Abstract: A stochastic chance-constrained planning method is applied to build a multi-objective optimization model for virtual power plant scheduling. Firstly, the implementation cost of demand response is calculated using the system income difference. Secondly, a wind power plant, photovoltaic power, an electric vehicle group and a conventional power plant are aggregated into a virtual power plant. A stochastic scheduling model is proposed for the virtual power plant, considering uncertainties under three objective functions. Thirdly, a three-stage hybrid intelligent solution algorithm is proposed, featuring the particle swarm optimization algorithm, the entropy weight method and the fuzzy satisfaction theory. Finally, the Yunnan distributed power demonstration project in China is utilized for example analysis. Simulation results demonstrate that when considering uncertainties, the system will reduce the grid connection of the wind power plant and photovoltaic power to decrease the power shortage punishment cost. The average reduction of the system power shortage punishment cost and the operation revenue of virtual power plant are 61.5% and 1.76%, respectively, while the average increase of the system abandoned energy cost is 40.4%. The output of the virtual power plant exhibits a reverse distribution with the confidence degree of the uncertainty variable. The proposed algorithm rapidly calculates a global optimal set. The electric vehicle group could provide spinning reserve to ensure stability of the output of the virtual power plant. Demand response could

  2. Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

    Directory of Open Access Journals (Sweden)

    M. Christobel

    2015-01-01

    Full Text Available One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO algorithm based on the particle’s best position (pbDPSO and global best position (gbDPSO is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF and First Come First Serve (FCFS algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER and Dynamic Voltage Scaling (DVS were used in the proposed DPSO algorithm.

  3. Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

    Science.gov (United States)

    Christobel, M.; Tamil Selvi, S.; Benedict, Shajulin

    2015-01-01

    One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm. PMID:26075296

  4. A Multiobjective Robust Scheduling Optimization Mode for Multienergy Hybrid System Integrated by Wind Power, Solar Photovoltaic Power, and Pumped Storage Power

    Directory of Open Access Journals (Sweden)

    Lihui Zhang

    2017-01-01

    Full Text Available Wind power plant (WPP, photovoltaic generators (PV, cell-gas turbine (CGT, and pumped storage power station (PHSP are integrated into multienergy hybrid system (MEHS. Firstly, this paper presents MEHS structure and constructs a scheduling model with the objective functions of maximum economic benefit and minimum power output fluctuation. Secondly, in order to relieve the uncertainty influence of WPP and PV on system, robust stochastic theory is introduced to describe uncertainty and propose a multiobjective stochastic scheduling optimization mode by transforming constraint conditions with uncertain variables. Finally, a 9.6 MW WPP, a 6.5 MW PV, three CGT units, and an upper reservoir with 10 MW·h equivalent capacity are chosen as simulation system. The results show MEHS system can achieve the best operation result by using the multienergy hybrid generation characteristic. PHSP could shave peak and fill valley of load curve by optimizing pumping storage and inflowing generating behaviors based on the load supply and demand status and the available power of WPP and PV. Robust coefficients can relieve the uncertainty of WPP and PV and provide flexible scheduling decision tools for decision-makers with different risk attitudes by setting different robust coefficients, which could maximize economic benefits and minimize operation risks at the same time.

  5. A Model for Optimizing the Combination of Solar Electricity Generation, Supply Curtailment, Transmission and Storage

    Science.gov (United States)

    Perez, Marc J. R.

    With extraordinary recent growth of the solar photovoltaic industry, it is paramount to address the biggest barrier to its high-penetration across global electrical grids: the inherent variability of the solar resource. This resource variability arises from largely unpredictable meteorological phenomena and from the predictable rotation of the earth around the sun and about its own axis. To achieve very high photovoltaic penetration, the imbalance between the variable supply of sunlight and demand must be alleviated. The research detailed herein consists of the development of a computational model which seeks to optimize the combination of 3 supply-side solutions to solar variability that minimizes the aggregate cost of electricity generated therefrom: Storage (where excess solar generation is stored when it exceeds demand for utilization when it does not meet demand), interconnection (where solar generation is spread across a large geographic area and electrically interconnected to smooth overall regional output) and smart curtailment (where solar capacity is oversized and excess generation is curtailed at key times to minimize the need for storage.). This model leverages a database created in the context of this doctoral work of satellite-derived photovoltaic output spanning 10 years at a daily interval for 64,000 unique geographic points across the globe. Underpinning the model's design and results, the database was used to further the understanding of solar resource variability at timescales greater than 1-day. It is shown that--as at shorter timescales--cloud/weather-induced solar variability decreases with geographic extent and that the geographic extent at which variability is mitigated increases with timescale and is modulated by the prevailing speed of clouds/weather systems. Unpredictable solar variability up to the timescale of 30 days is shown to be mitigated across a geographic extent of only 1500km if that geographic extent is oriented in a north

  6. Pruning-Based, Energy-Optimal, Deterministic I/O Device Scheduling for Hard Real-Time Systems

    Science.gov (United States)

    2005-02-01

    However, DPM via I/O device scheduling for hard real - time systems has received relatively little attention. In this paper,we present an offline I/O...polynomial time. We present experimental results to show that EDS and MDO reduce the energy consumption of I/O devices significantly for hard real - time systems .

  7. A Lifetime Optimization Algorithm Limited by Data Transmission Delay and Hops for Mobile Sink-Based Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yourong Chen

    2017-01-01

    Full Text Available To improve the lifetime of mobile sink-based wireless sensor networks and considering that data transmission delay and hops are limited in actual system, a lifetime optimization algorithm limited by data transmission delay and hops (LOA_DH for mobile sink-based wireless sensor networks is proposed. In LOA_DH, some constraints are analyzed, and an optimization model is proposed. Maximum capacity path routing algorithm is used to calculate the energy consumption of communication. Improved genetic algorithm which modifies individuals to meet all constraints is used to solve the optimization model. The optimal solution of sink node’s sojourn grid centers and sojourn times which maximizes network lifetime is obtained. Simulation results show that, in three node distribution scenes, LOA_DH can find the movement solution of sink node which covers all sensor nodes. Compared with MCP_RAND, MCP_GMRE, and EASR, the solution improves network lifetime and reduces average amount of node discarded data and average energy consumption of nodes.

  8. Application of Grey Wolf Optimizer Algorithm for Optimal Power Flow of Two-Terminal HVDC Transmission System

    Directory of Open Access Journals (Sweden)

    Heba Ahmed Hassan

    2017-01-01

    Full Text Available This paper applies a relatively new optimization method, the Grey Wolf Optimizer (GWO algorithm for Optimal Power Flow (OPF of two-terminal High Voltage Direct Current (HVDC electrical power system. The OPF problem of pure AC power systems considers the minimization of total costs under equality and inequality constraints. Hence, the OPF problem of integrated AC-DC power systems is extended to incorporate HVDC links, while taking into consideration the power transfer control characteristics using a GWO algorithm. This algorithm is inspired by the hunting behavior and social leadership of grey wolves in nature. The proposed algorithm is applied to two different case-studies: the modified 5-bus and WSCC 9-bus test systems. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques. Analysis of the obtained results show that the proposed GWO algorithm is able to achieve shorter CPU time, as well as minimized total cost when compared with already existing optimization techniques. This conclusion proves the efficiency of the GWO algorithm.

  9. Optimal Scheduling of Material Handling Devices in a PCB Production Line: Problem Formulation and a Polynomial Algorithm

    Directory of Open Access Journals (Sweden)

    Ada Che

    2008-01-01

    Full Text Available Modern automated production lines usually use one or multiple computer-controlled robots or hoists for material handling between workstations. A typical application of such lines is an automated electroplating line for processing printed circuit boards (PCBs. In these systems, cyclic production policy is widely used due to large lot size and simplicity of implementation. This paper addresses cyclic scheduling of a multihoist electroplating line with constant processing times. The objective is to minimize the cycle time, or equivalently to maximize the production throughput, for a given number of hoists. We propose a mathematical model and a polynomial algorithm for this scheduling problem. Computational results on randomly generated instances are reported.

  10. Evolutionary Optimization for the Number and Capacity of Surge Tanks and Pipeline Diameters in a Transmission Line

    Directory of Open Access Journals (Sweden)

    Gholam Reza Talebzadeh Sarvestani

    2006-09-01

    Full Text Available Controlling the unsteady effects of fluid flow (water hammer is one of the most important monitoring factors for structural protection of transmission pipelines. These effects are controlled by surge tanks, air chambers, pressure relief valves, and check valves. Generally, the critical points are detected by simulating the unsteady flow of the fluid, and accordingly, optimum positioning of the control devices is decided. Among the search methods, Genetic Algorithm (GA is an effective and robust method to solve highly complex optimization problems. Here, for the first time, GA coupled with an unsteady flow simulator is used to optimize the number and capacity of surge tanks in a pipeline system. In addition, the pipeline diameters are optimized for their best performance.

  11. Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid

    International Nuclear Information System (INIS)

    Jian, Linni; Zheng, Yanchong; Xiao, Xinping; Chan, C.C.

    2015-01-01

    Highlights: • A novel event-triggered scheduling scheme for vehicle-to-grid (V2G) operation is proposed. • New scheme can handle the uncertainty arising from stochastic connection of electric vehicles. • New scheme aims at minimizing the overall load variance of power grid by V2G operation. • Method to evaluate the performance of proposed scheme is elaborated and demonstrated. - Abstract: Vehicle-to-grid (V2G) operation of plug-in electric vehicles (PEVs) is attracting increasing attention since it can assist to improve the efficiency and reliability of power grid, as well as reduce the operating cost and greenhouse gas emission of electric vehicles. Within the scheme of V2G operation, PEVs are expected to serve as a novel distributed energy storage system (ESS) to help achieve the balance between supply and demand of power grid. One of the key difficulties concerning its practical implementation lies in that the availability of PEVs as ESS for grid remains highly uncertain due to their mobility as transportation tools. To address this issue, a novel event-triggered scheduling scheme for V2G operation based on the scenario of stochastic PEV connection to smart grid is proposed in this paper. Firstly, the mathematical model is formulated. Secondly, the preparation of input data for systematic evaluation is introduced and the case study is conducted. Finally, statistic analysis results demonstrate that our proposed V2G scheduling scheme can dramatically smooth out the fluctuation in power load profiles

  12. Optimization of municipal waste collection scheduling and routing using vehicle assignment problem (case study of Surabaya city waste collection)

    Science.gov (United States)

    Ramdhani, M. N.; Baihaqi, I.; Siswanto, N.

    2018-04-01

    Waste collection and disposal become a major problem for many metropolitan cities. Growing population, limited vehicles, and increased road traffic make the waste transportation become more complex. Waste collection involves some key considerations, such as vehicle assignment, vehicle routes, and vehicle scheduling. In the scheduling process, each vehicle has a scheduled departure that serve each route. Therefore, vehicle’s assignments should consider the time required to finish one assigment on that route. The objective of this study is to minimize the number of vehicles needed to serve all routes by developing a mathematical model which uses assignment problem approach. The first step is to generated possible routes from the existing routes, followed by vehicle assignments for those certain routes. The result of the model shows fewer vehicles required to perform waste collection asa well as the the number of journeys that the vehicle to collect the waste to the landfill. The comparison of existing conditions with the model result indicates that the latter’s has better condition than the existing condition because each vehicle with certain route has an equal workload, all the result’s model has the maximum of two journeys for each route.

  13. Adaptable System Increasing the Transmission Speed and Reliability in Packet Network by Optimizing Delay

    Directory of Open Access Journals (Sweden)

    Zbynek Kocur

    2014-01-01

    Full Text Available There is a great diversity in the transmission technologies in current data networks. Individual technologies are in most cases incompatible at physical and partially also at the link layer of the reference ISO/OSI model. Network compatibility, as the ability to transmit data, is realizable through the third layer, which is able to guarantee the operation of the different devices across their technological differences. The proposed inverse packet multiplexer addresses increase of the speed and reliability of packet transmission to the third layer, and at the same time it increases the stability of the data communication by the regulation of the delay value during the transmission. This article presents implementation of a communication system and its verification in real conditions. The conclusion compares the strengths and weaknesses of the proposed control system.

  14. Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Qin Hui [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhou Jianzhong, E-mail: jz.zhou@hust.edu.c [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Youlin; Wang Ying; Zhang Yongchuan [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-04-15

    A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE's operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.

  15. Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

    Science.gov (United States)

    He, Hao; Wang, Jun; Zhu, Jiang; Li, Shaoqian

    2010-12-01

    In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP), which can be solved by standard linear programming (LP) method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

  16. Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Jiang Zhu

    2010-01-01

    Full Text Available In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP, which can be solved by standard linear programming (LP method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

  17. A Smart Grid Framework for Optimally Integrating Supply-Side, Demand-Side and Transmission Line Management Systems

    Directory of Open Access Journals (Sweden)

    Chukwuka Monyei

    2018-04-01

    Full Text Available A coordinated centralized energy management system (ConCEMS is presented in this paper that seeks to integrate for optimal grid operation—the supply side energy management system (SSEMS, home energy management system (HEMS and transmission line management system (TLMS. ConCEMS in ensuring the optimal operation of an IEEE 30-bus electricity network harmonizes the individual objective function of SSEMS, HEMS and TLMS to evolve an optimal dispatch of participating demand response (DR loads that does not violate transmission line ampacity limits (TLMS constraint while minimizing consumer cost (HEMS constraint and supply side operations cost (SSEMS constraint. An externally constrained genetic algorithm (ExC-GA that is influenced by feedback from TLMS is also presented that intelligently varies the dispatch time of participating DR loads to meet the individual objective functions. Hypothetical day ahead dynamic pricing schemes (Price1, Price2 and Price3 have also been adopted alongside an existing time of use (Price0 pricing scheme for comparison and discussion while a dynamic thermal line rating (DTLR algorithm has also been incorporated to dynamically compute power limits based on real time associated data.

  18. Optimal width of quasicrystalline slabs of dielectric cylinders to microwave radiation transmission contrast

    Energy Technology Data Exchange (ETDEWEB)

    Andueza, Ángel; Sevilla, Joaquín [Dpto. Ing. Eléctrica y Electrónica Universidad Pública de Navarra, 31006 Pamplona (Spain); Smart Cities Institute, Universidad Pública de Navarra, 31006 Pamplona (Spain); Wang, Kang [Laboratoire de Physique des Solides, UMR CNRS/Université Paris-Sud, Université Paris-Saclay, 91405 Orsay (France); Pérez-Conde, Jesús [Dpto. de Física Universidad Pública de Navarra, 31006 Pamplona (Spain)

    2016-08-28

    Light confinement induced by resonant states in aperiodic photonic structures is interesting for many applications. A particular case of these resonances can be found in 2D quasicrystalline arrangements of dielectric cylinders. These systems present a rather isotropic band gap as well as isolated in-gap photonic states (as a result of spatially localized resonances). These states are built by high symmetry polygonal clusters that can be regarded as photonic molecules. In this paper, we study the transmission properties of a slab of glass cylinders arranged in approximants of the decagonal quasicrystalline structure. In particular, we investigate the influence of the slab width in the transmission contrast between the states and the gap. The study is both experimental and numerical in the microwave regime. We find that the best transmission contrast is found for a width of around three times the radiation wavelength. The transmission in the band gap region is mediated by the resonances of the photonic molecules. If the samples are thin enough, they become transparent except around a resonance of the photonic molecule which reflects the incoming light.

  19. A New Method Based on Simulation-Optimization Approach to Find Optimal Solution in Dynamic Job-shop Scheduling Problem with Breakdown and Rework

    Directory of Open Access Journals (Sweden)

    Farzad Amirkhani

    2017-03-01

    The proposed method is implemented on classical job-shop problems with objective of makespan and results are compared with mixed integer programming model. Moreover, the appropriate dispatching priorities are achieved for dynamic job-shop problem minimizing a multi-objective criteria. The results show that simulation-based optimization are highly capable to capture the main characteristics of the shop and produce optimal/near-optimal solutions with highly credibility degree.

  20. Geometrical optimization of the transmission and dispersion properties of arrayed waveguide gratings using two stigmatic point mountings.

    Science.gov (United States)

    Muñoz, P; Pastor, D; Capmany, J; Martínez, A

    2003-09-22

    In this paper, the procedure to optimize flat-top Arrayed Waveguide Grating (AWG) devices in terms of transmission and dispersion properties is presented. The systematic procedure consists on the stigmatization and minimization of the Light Path Function (LPF) used in classic planar spectrograph theory. The resulting geometry arrangement for the Arrayed Waveguides (AW) and the Output Waveguides (OW) is not the classical Rowland mounting, but an arbitrary geometry arrangement. Simulation using previous published enhanced modeling show how this geometry reduces the passband ripple, asymmetry and dispersion, in a design example.

  1. Optimization of the exploitation system of a low enthalpy geothermal aquifer with zones of different transmissivities and temperatures

    International Nuclear Information System (INIS)

    Tselepidou, K.; Katsifarakis, K.L.

    2010-01-01

    Market penetration of renewable energy sources, such as geothermal energy, could be promoted even by small cost reductions, achieved through improved development design. This paper deals with optimization of the exploitation system of a low enthalpy geothermal aquifer, by means of the method of genetic algorithms, which has been successfully used in similar problems of groundwater resources management. With respect to water flow, the aquifer consists of two zones of different transmissivities, while from the thermal point of view it may bear any number of zones with different temperatures. The optimization process comprises the annual pumping cost of the required flow and the amortization cost of the pipe network, which carries the hot water from the wells to a central water tank, situated at the border of the geothermal field. Results show that application of the proposed methodology allows better planning of low enthalpy geothermal heating systems, which may be crucial in cases of marginal financial viability. (author)

  2. Optimization Technique With Sensitivity Analysis On Menu Scheduling For Boarding School Student Aged 13-18 Using “Sufahani-Ismail Algorithm”

    Science.gov (United States)

    Sudin, Azila M.; Sufahani, Suliadi

    2018-04-01

    Boarding school student aged 13-18 need to eat nutritious meals which contains proper calories, vitality and nutrients for appropriate development with a specific end goal to repair and upkeep the body tissues. Furthermore, it averts undesired diseases and contamination. Serving healthier food is a noteworthy stride towards accomplishing that goal. However, arranging a nutritious and balance menu manually is convoluted, wasteful and tedious. Therefore, the aim of this study is to develop a mathematical model with an optimization technique for menu scheduling that fulfill the whole supplement prerequisite for boarding school student, reduce processing time, minimize the budget and furthermore serve assortment type of food each day. It additionally gives the flexibility for the cook to choose any food to be considered in the beginning of the process and change any favored menu even after the ideal arrangement and optimal solution has been obtained. This is called sensitivity analysis. A recalculation procedure will be performed in light of the ideal arrangement and seven days menu was produced. The data was gathered from the Malaysian Ministry of Education and schools authorities. Menu arranging is a known optimization problem. Therefore Binary Programming alongside optimization technique and “Sufahani-Ismail Algorithm” were utilized to take care of this issue. In future, this model can be implemented to other menu problem, for example, for sports, endless disease patients, militaries, colleges, healing facilities and nursing homes.

  3. Power Allocation Optimization: Linear Precoding Adapted to NB-LDPC Coded MIMO Transmission

    Directory of Open Access Journals (Sweden)

    Tarek Chehade

    2015-01-01

    Full Text Available In multiple-input multiple-output (MIMO transmission systems, the channel state information (CSI at the transmitter can be used to add linear precoding to the transmitted signals in order to improve the performance and the reliability of the transmission system. This paper investigates how to properly join precoded closed-loop MIMO systems and nonbinary low density parity check (NB-LDPC. The q elements in the Galois field, GF(q, are directly mapped to q transmit symbol vectors. This allows NB-LDPC codes to perfectly fit with a MIMO precoding scheme, unlike binary LDPC codes. The new transmission model is detailed and studied for several linear precoders and various designed LDPC codes. We show that NB-LDPC codes are particularly well suited to be jointly used with precoding schemes based on the maximization of the minimum Euclidean distance (max-dmin criterion. These results are theoretically supported by extrinsic information transfer (EXIT analysis and are confirmed by numerical simulations.

  4. Stage, tumor growth rate and optimal dose fractionation schedules in the treatment of squamous cell carcinoma of the head and neck

    International Nuclear Information System (INIS)

    Sugawara, Tadashi; Morita, Mamoru; Aihara, Toshinori; Tanaka, Osamu

    1983-01-01

    In 77 patients with cancer of the head and neck, 45 patients received radiotherapy alone, while 32 patients with T 1 or T 2 glottic cancer received combined therapy with laryngomicrosurgery performed prior to or during the course of irradiation. These T 1 and T 2 groups were separately analyzed from other T 1 and T 2 groups as T sub(LMS). Local recurrence rates were compared concerning overall time and fraction size in following three subgroups, i.e., T sub(LMS), Tsub(1+2), and Tsub(3+4). No significant correlation was detected between total dose converted to partial tolerance (PT) and local control in all subgroups except for Tsub(3+4), in which local recurrence rate was rather higher in high PT range. Local control was significantly dependent on overall treatment time and fraction size, differently by tumor stage. Favorable fractionation schedules considered as optimal for T sub(LMS) were those in which treatment was given 5 times weekly with a daily dose rate more than 196 rad in a shorter overall time of less than 42 days. In contrast to T sub(LMS), effective schedules for Tsub(3+4) consisted of longer overall time of more than 60 days and a lower daily dose rate of less than 153 rad. In Tsub(1+2), the optimal schedule was needed to be altered according to rapidness of progression of disease characterised by duration of the complaints, which was statistically proved to be significantly shorter in Tsub(1+2) than Tsub(3+4). Rapidity-adjusted schedule consisted of a shorter over-all time of less than 49 days with a daily dose rate of more than 175 rad 5 fractions a week for a case having a duration of complaints of less than 2.9 months, and a longer overall time of more than 50 days with a daily dose rate of less than 174 rad for a case having a duration of more than 3 months. (J.P.N.)

  5. Operation costs and pollutant emissions reduction by definition of new collection scheduling and optimization of MSW collection routes using GIS. The case study of Barreiro, Portugal.

    Science.gov (United States)

    Zsigraiova, Zdena; Semiao, Viriato; Beijoco, Filipa

    2013-04-01

    This work proposes an innovative methodology for the reduction of the operation costs and pollutant emissions involved in the waste collection and transportation. Its innovative feature lies in combining vehicle route optimization with that of waste collection scheduling. The latter uses historical data of the filling rate of each container individually to establish the daily circuits of collection points to be visited, which is more realistic than the usual assumption of a single average fill-up rate common to all the system containers. Moreover, this allows for the ahead planning of the collection scheduling, which permits a better system management. The optimization process of the routes to be travelled makes recourse to Geographical Information Systems (GISs) and uses interchangeably two optimization criteria: total spent time and travelled distance. Furthermore, rather than using average values, the relevant parameters influencing fuel consumption and pollutant emissions, such as vehicle speed in different roads and loading weight, are taken into consideration. The established methodology is applied to the glass-waste collection and transportation system of Amarsul S.A., in Barreiro. Moreover, to isolate the influence of the dynamic load on fuel consumption and pollutant emissions a sensitivity analysis of the vehicle loading process is performed. For that, two hypothetical scenarios are tested: one with the collected volume increasing exponentially along the collection path; the other assuming that the collected volume decreases exponentially along the same path. The results evidence unquestionable beneficial impacts of the optimization on both the operation costs (labor and vehicles maintenance and fuel consumption) and pollutant emissions, regardless the optimization criterion used. Nonetheless, such impact is particularly relevant when optimizing for time yielding substantial improvements to the existing system: potential reductions of 62% for the total

  6. Revisiting the NEH algorithm- the power of job insertion technique for optimizing the makespan in permutation flow shop scheduling

    Directory of Open Access Journals (Sweden)

    A. Baskar

    2016-04-01

    Full Text Available Permutation flow shop scheduling problems have been an interesting area of research for over six decades. Out of the several parameters, minimization of makespan has been studied much over the years. The problems are widely regarded as NP-Complete if the number of machines is more than three. As the computation time grows exponentially with respect to the problem size, heuristics and meta-heuristics have been proposed by many authors that give reasonably accurate and acceptable results. The NEH algorithm proposed in 1983 is still considered as one of the best simple, constructive heuristics for the minimization of makespan. This paper analyses the powerful job insertion technique used by NEH algorithm and proposes seven new variants, the complexity level remains same. 120 numbers of problem instances proposed by Taillard have been used for the purpose of validating the algorithms. Out of the seven, three produce better results than the original NEH algorithm.

  7. Looking for optimal number and placement of FACTS devices to manage the transmission congestion

    International Nuclear Information System (INIS)

    Rahimzadeh, Sajad; Tavakoli Bina, Mohammad

    2011-01-01

    Some applications of FACTS devices show that they are proper and effective tools to control the technical parameters of power systems. However determination of optimal number, location, size and type of these devices is a difficult problem. Moreover, applying a suitable objective function for optimal placement of FACTS devices plays a very important role in economic improvement of a power market. In this paper optimal placement of parallel and series FACTS devices is studied. The STATCOM is selected as a parallel FACTS device and SSSC as a series one. The optimization problem is formulated in regard to restructured environment and a new objective function is defined so that its minimization can alleviate the congestion and provide fairer conditions for power market participants. Moreover, an index based on objective function value is presented to determine the optimal number of each FACTS device in a specific designed algorithm. The power injection models for STATCOM and SSSC are adopted by applying neural models based on the averaging technique. This model takes the converter power losses into account and produces the required PQ-phasor that is suitable for power system steady state analysis. The proposed method is applied on modified IEEE 14-bus, 30-bus and 118-bus test systems and the results are analyzed.

  8. Refinery scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Magalhaes, Marcus V.; Fraga, Eder T. [PETROBRAS, Rio de Janeiro, RJ (Brazil); Shah, Nilay [Imperial College, London (United Kingdom)

    2004-07-01

    This work addresses the refinery scheduling problem using mathematical programming techniques. The solution adopted was to decompose the entire refinery model into a crude oil scheduling and a product scheduling problem. The envelope for the crude oil scheduling problem is composed of a terminal, a pipeline and the crude area of a refinery, including the crude distillation units. The solution method adopted includes a decomposition technique based on the topology of the system. The envelope for the product scheduling comprises all tanks, process units and products found in a refinery. Once crude scheduling decisions are Also available the product scheduling is solved using a rolling horizon algorithm. All models were tested with real data from PETROBRAS' REFAP refinery, located in Canoas, Southern Brazil. (author)

  9. Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

    Science.gov (United States)

    Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

    2018-03-01

    In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

  10. Joint Network Coding and Opportunistic Scheduling for the Bidirectional Relay Channel

    KAUST Repository

    Shaqfeh, Mohammad

    2013-05-27

    In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users’ transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

  11. Joint Network Coding and Opportunistic Scheduling for the Bidirectional Relay Channel

    KAUST Repository

    Shaqfeh, Mohammad; Alnuweiri, Hussein; Alouini, Mohamed-Slim; Zafar, Ammar

    2013-01-01

    In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users’ transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

  12. Optimization of Trade-offs in Error-free Image Transmission

    Science.gov (United States)

    Cox, Jerome R.; Moore, Stephen M.; Blaine, G. James; Zimmerman, John B.; Wallace, Gregory K.

    1989-05-01

    The availability of ubiquitous wide-area channels of both modest cost and higher transmission rate than voice-grade lines promises to allow the expansion of electronic radiology services to a larger community. The band-widths of the new services becoming available from the Integrated Services Digital Network (ISDN) are typically limited to 128 Kb/s, almost two orders of magnitude lower than popular LANs can support. Using Discrete Cosine Transform (DCT) techniques, a compressed approximation to an image may be rapidly transmitted. However, intensity or resampling transformations of the reconstructed image may reveal otherwise invisible artifacts of the approximate encoding. A progressive transmission scheme reported in ISO Working Paper N800 offers an attractive solution to this problem by rapidly reconstructing an apparently undistorted image from the DCT coefficients and then subse-quently transmitting the error image corresponding to the difference between the original and the reconstructed images. This approach achieves an error-free transmission without sacrificing the perception of rapid image delivery. Furthermore, subsequent intensity and resampling manipulations can be carried out with confidence. DCT coefficient precision affects the amount of error information that must be transmitted and, hence the delivery speed of error-free images. This study calculates the overall information coding rate for six radiographic images as a function of DCT coefficient precision. The results demonstrate that a minimum occurs for each of the six images at an average coefficient precision of between 0.5 and 1.0 bits per pixel (b/p). Apparently undistorted versions of these six images can be transmitted with a coding rate of between 0.25 and 0.75 b/p while error-free versions can be transmitted with an overall coding rate between 4.5 and 6.5 b/p.

  13. Design and optimization of the PBFA II vacuum interface and transmission lines for light ion fusion

    International Nuclear Information System (INIS)

    Mc Daniel, D.H.; Stinnett, R.W.; Gray, E.W.; Mattis, R.E.

    1985-01-01

    The PBFA II vacuum insulator was originally designed for optimum coupling to a proton ion diode with minimum inductance. In July 1983 it was decided that lithium ions at 30 MeV would be the baseline for PBFA II. This requires the use of Plasma Opening Switches (POS) and vacuum inductor to reach 30 MV. To achieve this, the vacuum magnetically insulated transmission lines had to be redesigned as an inductive energy store. To gain optimum coupling to this vacuum inductor, the output impedance of the water section was increased by the use of a water-dielectric transformer. The calculations leading to the final design are discussed

  14. Optimal control of transmission power management in wireless backbone mesh networks

    CSIR Research Space (South Africa)

    Olwal, TO

    2011-01-01

    Full Text Available , the TPM problems are modelled as a singular-perturbation of both energy and packet evolutions at the queue system as well as a weak-coupling problem, owing to the interference across adjacent multiple channels. Based on these models, an optimal control...

  15. Improvement of DC Optimal Power Flow Problem Based on Nodal Approximation of Transmission Losses

    Directory of Open Access Journals (Sweden)

    M. R. Baghayipour

    2012-03-01

    3-\tIts formulation is simple and easy to understand. Moreover, it can simply be realized in the form of Lagrange representation, makes it possible to be considered as some constraints in the body of any bi-level optimization problem, with its internal level including the OPF problem satisfaction.

  16. Optimal decoding and information transmission in Hodgkin–Huxley neurons under metabolic cost constraints

    Czech Academy of Sciences Publication Activity Database

    Košťál, Lubomír; Kobayashi, R.

    2015-01-01

    Roč. 136, Oct 2015 (2015), s. 3-10 ISSN 0303-2647 R&D Projects: GA ČR(CZ) GA15-08066S Institutional support: RVO:67985823 Keywords : neuronal coding * information transfer * optimal decoding Subject RIV: BD - Theory of Information Impact factor: 1.495, year: 2015

  17. Gradient decent based multi-objective cultural differential evolution for short-term hydrothermal optimal scheduling of economic emission with integrating wind power and photovoltaic power

    International Nuclear Information System (INIS)

    Zhang, Huifeng; Yue, Dong; Xie, Xiangpeng; Dou, Chunxia; Sun, Feng

    2017-01-01

    With the integration of wind power and photovoltaic power, optimal operation of hydrothermal power system becomes great challenge due to its non-convex, stochastic and complex-coupled constrained characteristics. This paper extends short-term hydrothermal system optimal model into short-term hydrothermal optimal scheduling of economic emission while considering integrated intermittent energy resources (SHOSEE-IIER). For properly solving SHOSEE-IIER problem, a gradient decent based multi-objective cultural differential evolution (GD-MOCDE) is proposed to improve the optimal efficiency of SHOSEE-IIER combined with three designed knowledge structures, which mainly enhances search ability of differential evolution in the shortest way. With considering those complex-coupled and stochastic constraints, a heuristic constraint-handling measurement is utilized to tackle with them both in coarse and fine tuning way, and probability constraint-handling procedures are taken to properly handle those stochastic constraints combined with their probability density functions. Ultimately, those approaches are implemented on five test systems, which testify the optimization efficiency of proposed GD-MOCDE and constraint-handling efficiency for system load balance, water balance and stochastic constraint-handling measurements, those obtained results reveal that the proposed GD-MOCDE can properly solve the SHOSEE-IIER problem combined with those constraint-handling approaches. - Highlights: • Gradient decent method is proposed to improve mutation operator. • Hydrothermal system is extended to hybrid energy system. • The uncertainty constraint is converted into deterministic constraint. • The results show the viability and efficiency of proposed algorithm.

  18. Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression

    Science.gov (United States)

    Panayi, Efstathios; Kyriakides, George

    2017-01-01

    Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields. PMID:28961254

  19. Short-term hydro generation scheduling of Xiluodu and Xiangjiaba cascade hydropower stations using improved binary-real coded bee colony optimization algorithm

    International Nuclear Information System (INIS)

    Lu, Peng; Zhou, Jianzhong; Wang, Chao; Qiao, Qi; Mo, Li

    2015-01-01

    Highlights: • STHGS problem is decomposed into two parallel sub-problems of UC and ELD. • Binary coded BCO is used to solve UC sub-problem with 0–1 discrete variables. • Real coded BCO is used to solve ELD sub-problem with continuous variables. • Some heuristic repairing strategies are designed to handle various constraints. • The STHGS of Xiluodu and Xiangjiaba cascade stations is solved by IB-RBCO. - Abstract: Short-term hydro generation scheduling (STHGS) of cascade hydropower stations is a typical nonlinear mixed integer optimization problem to minimize the total water consumption while simultaneously meeting the grid requirements and other hydraulic and electrical constraints. In this paper, STHGS problem is decomposed into two parallel sub-problems of unit commitment (UC) and economic load dispatch (ELD), and the methodology of improved binary-real coded bee colony optimization (IB-RBCO) algorithm is proposed to solve them. Firstly, the improved binary coded BCO is used to solve the UC sub-problem with 0–1 discrete variables, and the heuristic repairing strategy for unit state constrains is applied to generate the feasible unit commitment schedule. Then, the improved real coded BCO is used to solve the ELD sub-problem with continuous variables, and an effective method is introduced to handle various unit operation constraints. Especially, the new updating strategy of DE/best/2/bin method with dynamic parameter control mechanism is applied to real coded BCO to improve the search ability of IB-RBCO. Finally, to verify the feasibility and effectiveness of the proposed IB-RBCO method, it is applied to solve the STHGS problem of Xiluodu and Xiangjiaba cascaded hydropower stations, and the simulating results are compared with other intelligence algorithms. The simulation results demonstrate that the proposed IB-RBCO method can get higher-quality solutions with less water consumption and shorter calculating time when facing the complex STHGS problem

  20. TH-302, a hypoxia-activated prodrug with broad in vivo preclinical combination therapy efficacy: optimization of dosing regimens and schedules.

    Science.gov (United States)

    Liu, Qian; Sun, Jessica D; Wang, Jingli; Ahluwalia, Dharmendra; Baker, Amanda F; Cranmer, Lee D; Ferraro, Damien; Wang, Yan; Duan, Jian-Xin; Ammons, W Steve; Curd, John G; Matteucci, Mark D; Hart, Charles P

    2012-06-01

    Subregional hypoxia is a common feature of tumors and is recognized as a limiting factor for the success of radiotherapy and chemotherapy. TH-302, a hypoxia-activated prodrug selectively targeting hypoxic regions of solid tumors, delivers a cytotoxic warhead to the tumor, while maintaining relatively low systemic toxicity. The antitumor activity, different dosing sequences, and dosing regimens of TH-302 in combination with commonly used conventional chemotherapeutics were investigated in human tumor xenograft models. Seven chemotherapeutic drugs (docetaxel, cisplatin, pemetrexed, irinotecan, doxorubicin, gemcitabine, and temozolomide) were tested in combination with TH-302 in eleven human xenograft models, including non-small cell lung cancer (NSCLC), colon cancer, prostate cancer, fibrosarcoma, melanoma, and pancreatic cancer. The antitumor activity of docetaxel, cisplatin, pemetrexed, irinotecan, doxorubicin, gemcitabine, and temozolomide was increased when combined with TH-302 in nine out of eleven models tested. Administration of TH-302 2-8 h prior to the other chemotherapeutics yielded superior efficacy versus other sequences tested. Simultaneous administration of TH-302 and chemotherapeutics increased toxicity versus schedules with dosing separations. In a dosing optimization study, TH-302 administered daily at 50 mg/kg intraperitoneally for 5 days per week in the H460 NSCLC model showed the optimal response with minimal toxicity. TH-302 enhances the activity of a wide range of conventional anti-neoplastic agents in a broad panel of in vivo xenograft models. These data highlight in vivo effects of schedule and order of drug administration in regimen efficacy and toxicity and have relevance to the design of human regimens incorporating TH-302.