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.

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.

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...

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.

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.

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....

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.

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.

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.

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.

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.

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.

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.

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 ...

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

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

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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

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.

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.

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...

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.

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.

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.

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.

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

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.

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...

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

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.

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

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

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.

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)

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.

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.

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...

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

Slowly progressive fluent aphasia

International Nuclear Information System (INIS)

Sakurai, Yasuhisa; Momose, Toshimitsu; Watanabe, Toshiaki; Ishikawa, Takashi; Iwata, Makoto; Bando, Mitsuaki.

1991-01-01

Three patients with slowly progressive fluent aphasia are reported. One of the patients presented with memory disturbance. They were characterized clinically by having selective deficits in vocabulary, which resulted in impairment of confrontation naming, and auditory comprehension. MRI showed an atrophy not only in the left temporal lobe (including the superior, middle and inferior temporal gyri), hippocampus, parahippocampual gyrus, and fusiform gyrus, but also in the left parietal lobe. I-123 IMP SPECT and F-18 FDG PET were used to determine regional cerebral blood flow and regional cerebral metabolic rate, respectively. In addition to the decreased tracer uptake in the left temporal and/or parietal lobe, a decreased uptake was seen in the bilateral basal ganglia, the inner side of the temporal lobe (including the bilateral hippocampus), the right anterior temporal lobe, and the left thalamus. These findings may deny the previous thought that lesions are localized in slowly progressive fluent aphasia. Furthermore, noticeable difficulty in naming, i.e., patients unable to recognize the right answer, are considered attributable to widespread lesions from the whole left temporal lobe, including the hippocampus, to the right temporal lobe. (N.K.)

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.

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.

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....

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.

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

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...

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.

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.

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.

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.

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....

Fluent Aphasia in Telugu: A Case Comparison Study of Semantic Dementia and Stroke Aphasia

Science.gov (United States)

Alladi, Suvarna; Mridula, Rukmini; Mekala, Shailaja; Rupela, Vani; Kaul, Subhash

2010-01-01

This study presents two cases with fluent aphasia in Telugu with semantic dementia and post-stroke fluent aphasia. Comparable scores were obtained on the conventional neuropsychological and language tests that were administered on the two cases. Both cases demonstrated fluent, grammatical and well-articulated speech with little content, impaired…

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....

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

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

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%.

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.

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.

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

FLUENT Test and Verification Document

International Nuclear Information System (INIS)

LEE, SI

2006-01-01

The FLUENT 6 CFD code has been benchmarked for a wide range of simple, classical, and complex physical problems associated with turbulent gas flow, natural convection, and turbulent mixing phenomena. The results validate the application of previous scoping calculations for the Tank 50/Tank 48 vapor space mixing. The benchmarked problems consisted of three groups. The first group was well-defined and classical problems for which analytical solutions exist. The other groups are complex and physical problems for which analytical solutions are difficult to obtain. For these test problems, CFD results were compared and verified through comparisons with experimental results. The benchmarking of the FLUENT 6 code showed that the code predictions are in good agreement with the analytical solutions or experimental test data. The code was shown to be sufficiently accurate to make reliable decisions based on calculated results for those applications that fall within the scope of the benchmarking test cases. For applications that fall outside the range of the benchmarking results, particularly for significantly higher benzene concentrations or for flow geometries not adequately represented by the κ-(var e psilon) turbulence model, further benchmarking work would be required

How Do Fluent and Poor Readers' Endurance Differ in Reading?

Science.gov (United States)

Bastug, Muhammet; Keskin, Hasan Kagan; Akyol, Mustafa

2017-01-01

It was observed in this research how endurance status of fluent readers and poor readers changed as the text became longer. 40 students of the primary school 4th-grade, 20 were fluent readers and other 20 were poor readers, participated in the research. A narrative text was utilised in the data collection process. Students' oral readings were…

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.

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.

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.

DEVELOPMENT OF PHONOLOGICAL AWARENESS OF STUTTERING CHILDREN AND CHILDREN WITH FLUENT SPEECH

Directory of Open Access Journals (Sweden)

Leila BEGIC

2014-03-01

Full Text Available The aim of this study was to determine characteristics of phonological awareness of stuttering children and children with fluent speech. The sample consisted of 64 children, between 56 and 83 months old (4 years and 8 months to 6 years and 11 months. Examinees were divided in two groups. The first group consisted of 32 stuttering children, 19 males, and 13 females. The control group consisted of 32 children with fluent speech, whose age and sex were equal to the age and sex of the children in the experimental group. The research was conducted in preschools and primary schools in Tuzla and Una-Sana Canton in Bosnia and Herzegovina. The subjects were examined with 7 subtests (syllable and phoneme blending abilities, ability to rhyme, phoneme segmentation, phoneme deletion, phoneme transposition and spoonerisms. Each of the subtest scores, which index a variety of phonological awareness abilities, was examined separately. Phonological awareness score is the total score which relates to a common result that the subjects achieved on these 7 individual subtests. The results showed that there were no statistically significant differences between stuttering children and their peers with fluent speech in relation to Phonological awareness score. The examination of differences between stuttering and non-stuttering children in individual variables, which describe phonological awareness, showed that there was statistically significant difference in the ability to rhyme between these two subjects groups. T-test was used for examination of the differences between the male stuttering children and their fluent peers, and also female stuttering children and their fluent peers for the phonological awareness variables. The results exhibited statistically significant differences in the variable Rhyme between the male stuttering children and their fluent peers. In addition, we examined the ability of phonemic analysis of children who stutter and children with fluent

An investigation of the use of co-verbal gestures in oral discourse among Chinese speakers with fluent versus non-fluent aphasia and healthy adults

Directory of Open Access Journals (Sweden)

Anthony Pak Hin Kong

2015-04-01

Full Text Available Introduction Co-verbal gestures can facilitate word production among persons with aphasia (PWA (Rose, Douglas, & Matyas, 2002 and play a communicative role for PWA to convey ideas (Sekine & Rose, 2013. Kong, Law, Kwan, Lai, and Lam (2015 recently reported a systematic approach to independently analyze gesture forms and functions in spontaneous oral discourse produced. When this annotation framework was used to compare speech-accompanying gestures used by PWA and unimpaired speakers, Kong, Law, Wat, and Lai (2013 found a significantly higher gesture-to-word ratio among PWAs. Speakers who were more severe in aphasia or produced a lower percentage of complete sentences or simple sentences in their narratives tended to use more gestures. Moreover, verbal-semantic processing impairment, but not the degree of hemiplegia, was found to affect PWAs’ employment of gestures. The current study aims to (1 investigate whether the frequency of gestural employment varied across speakers with non-fluent aphasia, fluent aphasia, and their controls, (2 examine how the distribution of gesture forms and functions differed across the three speaker groups, and (3 determine how well factors of complexity of linguistic output, aphasia severity, semantic processing integrity, and hemiplegia would predict the frequency of gesture use among PWAs. Method The participants included 23 Cantonese-speaking individuals with fluent aphasia, 21 with non-fluent aphasia, and 23 age- and education-matched controls. Three sets of language samples and video files were collected through the narrative tasks of recounting a personally important event, sequential description, and story-telling, using the Cantonese AphasiaBank protocol (Kong, Law, & Lee, 2009. While the language samples were linguistically quantified to reflect word- and sentential-level performance as well as discourse-level characteristics, the videos were annotated on the form and function of each gesture. All PWAs were

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.

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.

Conversion software for ANSYS APDL 2 FLUENT MHD magnetic file

International Nuclear Information System (INIS)

Ghita, G.; Ionescu, S.; Prisecaru, I.

2016-01-01

The present paper describes the improvements made to the conversion software for ANSYS APDL 2 FLUENT MHD Magnetic File which is able to extract the data from ANSYS APDL file and write down a file containing the magnetic field data in FLUENT magneto hydro dynamics (MHD) format. The MHD module has some features for the uniform and non uniform magnetic field but it is limited for sinusoidal or pulsed, square wave, having a fixed duty cycle of 50%. The present software, ANSYS APDL 2 FLUENT MHD Magnetic File, suffered major modifications in comparison with the last one. The most important improvement consists in a new graphical interface, which has 3D graphical interface for the input file but also for the output file. Another improvement has been made for processing time, the new version is two times faster comparing with the old one. (authors)

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.

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...

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.

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.

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

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...

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%.

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...

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.

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.

Lexical retrieval : An aspect of fluent second language production that can be enhanced

NARCIS (Netherlands)

Snellings, P; de Glopper, Kees; van Gelderen, A.

2002-01-01

The efficiency of lexical retrieval, an essential subprocess of productive language skills, is crucial in fluent writing and speaking. We examine the feasibility of an experimental computerized training for fluent lexical retrieval in the second language in a classroom setting, applying techniques

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 ...

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.

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.

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.

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)

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

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.

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.

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.

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

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.

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 ...

Non-fluent speech following stroke is caused by impaired efference copy.

Science.gov (United States)

Feenaughty, Lynda; Basilakos, Alexandra; Bonilha, Leonardo; den Ouden, Dirk-Bart; Rorden, Chris; Stark, Brielle; Fridriksson, Julius

2017-09-01

Efference copy is a cognitive mechanism argued to be critical for initiating and monitoring speech: however, the extent to which breakdown of efference copy mechanisms impact speech production is unclear. This study examined the best mechanistic predictors of non-fluent speech among 88 stroke survivors. Objective speech fluency measures were subjected to a principal component analysis (PCA). The primary PCA factor was then entered into a multiple stepwise linear regression analysis as the dependent variable, with a set of independent mechanistic variables. Participants' ability to mimic audio-visual speech ("speech entrainment response") was the best independent predictor of non-fluent speech. We suggest that this "speech entrainment" factor reflects integrity of internal monitoring (i.e., efference copy) of speech production, which affects speech initiation and maintenance. Results support models of normal speech production and suggest that therapy focused on speech initiation and maintenance may improve speech fluency for individuals with chronic non-fluent aphasia post stroke.

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

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

Speech Errors in Progressive Non-Fluent Aphasia

Science.gov (United States)

Ash, Sharon; McMillan, Corey; Gunawardena, Delani; Avants, Brian; Morgan, Brianna; Khan, Alea; Moore, Peachie; Gee, James; Grossman, Murray

2010-01-01

The nature and frequency of speech production errors in neurodegenerative disease have not previously been precisely quantified. In the present study, 16 patients with a progressive form of non-fluent aphasia (PNFA) were asked to tell a story from a wordless children's picture book. Errors in production were classified as either phonemic,…

The retrieval and inflection of verbs in the spontaneous speech of fluent aphasic speakers

NARCIS (Netherlands)

Bastiaanse, Y.R.M.

Fluent aphasia of the anomic and Wernicke's type is characterized by word retrieval difficulties. However, in fluent aphasic speech, grammatical deviations have been observed as well. There is debate as to whether these grammatical problems are caused by the word retrieval deficit, by an additional

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

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.

CFD RANS Simulations on a Generic Conventional Scale Model Submarine: Comparison between Fluent and OpenFOAM

Science.gov (United States)

2015-09-01

UNCLASSIFIED UNCLASSIFIED CFD RANS Simulations on a Generic Conventional Scale Model Submarine: Comparison between Fluent and OpenFOAM ... OpenFOAM to replace some of the Fluent simulations. The fidelity of the Fluent code has been carefully validated, but the accuracy of parts of the... OpenFOAM code have not been so extensively tested. To test the accuracy of the OpenFOAM software, CFD simulations have been performed on the DSTO

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.

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.

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)

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.

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...

Validation study of FLUENT for the application of dry-storage system thermal analysis

International Nuclear Information System (INIS)

Tseng Yungshin; Wang Jongrong; Cheng Yihsiang; Shih Chunkuan

2009-01-01

In this study, the commercial CFD code, FLUENT, has been selected to evaluate the accuracy of the temperature prediction for nuclear fuel dry storage system (DSS). The experimental results of VSC-17 DSS were employed for such purposes. Through a minimal assumptions and necessary simplifications (e.g., the lumped model of fuel), a high integrity geometry model with about 4.0 million meshes was employed to solve the conjugate heat transfer coupled with thermal radiation problem. First, the general scheme validation indicates that FLUENT can provide a result which agrees well with analytical solution. It means that the numerical scheme included in FLUENT is reliable and accurate. The comparisons with VSC-17 further points out that the maximum temperature difference between simulation and experimental solution is less than 10degC and the maximum deviation for whole DSS is less than 5%. Those findings mentioned above imply that FLUENT can be a highly accurate thermal analysis tool for d DSS design. Through a series modeling and scheme selecting, this code can be an efficient tool for the new DSS development in the future. (author)

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

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....

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.

Conditional and future tense impairment in non-fluent aphasia

DEFF Research Database (Denmark)

Rofes, Adrià; Bastiaanse, Roelien; Martínez-Ferreiro, Silvia

2014-01-01

Background:Morphological errors of tense and agreement are salient in agrammatic aphasia. The PADILIH predicts impairments in discourse linking that translate to greater difficulties in referring to a past event time than to a future or a present event time. In Catalan, the Periphrastic conditional...... between verb forms that refer to an irrealis future event (Simple conditional and Future). We also assessed whether problems in time reference extend to individuals with non-fluent aphasia that are not typical agrammatic Broca aphasia. Methods & Procedures:A sentence completion task that included 60...... sentences (20 per type) of equal length in a Conditional structure (if-sentences) was designed. We tested three sentence types: Periphrastic conditional, Simple conditional and Future. The task was administered to nine participants with non-fluent aphasia and nine age-matched non-brain-damaged participants...

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.

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....

Poetry: A Feast to Form Fluent Readers.

Science.gov (United States)

Seitz, Sheila

In this lesson plan, grade 3 to 5 students examine elements of fluent reading through oral poetry performance. They use the Internet to identify a poem to prepare and perform for the class. The main objective of this lesson concerns improving fluency. During four hours over a span of five days, students will: be able to identify text written in…

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.

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.

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.

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.

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...

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.

Damage to the anterior arcuate fasciculus predicts non-fluent speech production in aphasia

OpenAIRE

Fridriksson, Julius; Guo, Dazhou; Fillmore, Paul; Holland, Audrey; Rorden, Chris

2013-01-01

Non-fluent aphasia implies a relatively straightforward neurological condition characterized by limited speech output. However, it is an umbrella term for different underlying impairments affecting speech production. Several studies have sought the critical lesion location that gives rise to non-fluent aphasia. The results have been mixed but typically implicate anterior cortical regions such as Broca’s area, the left anterior insula, and deep white matter regions. To provide a clearer pictur...

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.

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.

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.

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.

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.

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.

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.

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

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.

Damage to the anterior arcuate fasciculus predicts non-fluent speech production in aphasia.

Science.gov (United States)

Fridriksson, Julius; Guo, Dazhou; Fillmore, Paul; Holland, Audrey; Rorden, Chris

2013-11-01

Non-fluent aphasia implies a relatively straightforward neurological condition characterized by limited speech output. However, it is an umbrella term for different underlying impairments affecting speech production. Several studies have sought the critical lesion location that gives rise to non-fluent aphasia. The results have been mixed but typically implicate anterior cortical regions such as Broca's area, the left anterior insula, and deep white matter regions. To provide a clearer picture of cortical damage in non-fluent aphasia, the current study examined brain damage that negatively influences speech fluency in patients with aphasia. It controlled for some basic speech and language comprehension factors in order to better isolate the contribution of different mechanisms to fluency, or its lack. Cortical damage was related to overall speech fluency, as estimated by clinical judgements using the Western Aphasia Battery speech fluency scale, diadochokinetic rate, rudimentary auditory language comprehension, and executive functioning (scores on a matrix reasoning test) in 64 patients with chronic left hemisphere stroke. A region of interest analysis that included brain regions typically implicated in speech and language processing revealed that non-fluency in aphasia is primarily predicted by damage to the anterior segment of the left arcuate fasciculus. An improved prediction model also included the left uncinate fasciculus, a white matter tract connecting the middle and anterior temporal lobe with frontal lobe regions, including the pars triangularis. Models that controlled for diadochokinetic rate, picture-word recognition, or executive functioning also revealed a strong relationship between anterior segment involvement and speech fluency. Whole brain analyses corroborated the findings from the region of interest analyses. An additional exploratory analysis revealed that involvement of the uncinate fasciculus adjudicated between Broca's and global aphasia

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)

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.

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.

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.

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...

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)

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.

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.

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

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)

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.

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.

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.

Epidural cortical stimulation as adjunctive treatment for non-fluent aphasia: preliminary findings.

Science.gov (United States)

Cherney, Leora R; Erickson, Robert K; Small, Steven L

2010-09-01

This study evaluated the safety and feasibility of targeted epidural cortical stimulation delivered concurrently with intensive speech-language therapy for treatment of chronic non-fluent aphasia. Eight stroke survivors with non-fluent aphasia received intensive behavioural therapy for 3 h daily for 6 weeks using a combination of articulation drills, oral reading and conversational practice. Four of these participants (investigational participants) also underwent functional MRI guided surgical implantation of an epidural stimulation device which was activated only during therapy sessions. Behavioural data were collected before treatment, immediately after treatment and at 6 and 12 weeks following termination of therapy. Imaging data were collected before and after treatment. Investigational participants showed a mean Aphasia Quotient change of 8.0 points immediately post-therapy and at the 6 week follow-up, and 12.3 points at 12 weeks. The control group had changes of 4.6, 5.5 and 3.6 points, respectively. Similar changes were noted on subjective caregiver ratings. Functional imaging suggested increased consolidation of activity in interventional participants. Behavioural speech-language therapy improves non-fluent aphasia, independent of cortical stimulation. However, epidural stimulation of the ipsilesional premotor cortex may augment this effect, with the largest effects after completion of therapy. The neural mechanisms underlying these effects are manifested in the brain by decreases in the volume of activity globally and in particular regions. Although the number of participants enrolled in this trial precludes definitive conclusions, targeted epidural cortical stimulation appears safe and may be a feasible adjunctive treatment for non-fluent aphasia, particularly when the aphasia is more severe.

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

Science.gov (United States)

Gao, Qian

For both the conventional radio frequency and the comparably recent optical wireless communication systems, extensive effort from the academia had been made in improving the network spectrum efficiency and/or reducing the error rate. To achieve these goals, many fundamental challenges such as power efficient constellation design, nonlinear distortion mitigation, channel training design, network scheduling and etc. need to be properly addressed. In this dissertation, novel schemes are proposed accordingly to deal with specific problems falling in category of these challenges. Rigorous proofs and analyses are provided for each of our work to make a fair comparison with the corresponding peer works to clearly demonstrate the advantages. The first part of this dissertation considers a multi-carrier optical wireless system employing intensity modulation (IM) and direct detection (DD). A block-wise constellation design is presented, which treats the DC-bias that conventionally used solely for biasing purpose as an information basis. Our scheme, we term it MSM-JDCM, takes advantage of the compactness of sphere packing in a higher dimensional space, and in turn power efficient constellations are obtained by solving an advanced convex optimization problem. Besides the significant power gains, the MSM-JDCM has many other merits such as being capable of mitigating nonlinear distortion by including a peak-to-power ratio (PAPR) constraint, minimizing inter-symbol-interference (ISI) caused by frequency-selective fading with a novel precoder designed and embedded, and further reducing the bit-error-rate (BER) by combining with an optimized labeling scheme. The second part addresses several optimization problems in a multi-color visible light communication system, including power efficient constellation design, joint pre-equalizer and constellation design, and modeling of different structured channels with cross-talks. Our novel constellation design scheme, termed CSK-Advanced, is

Sensitivity analysis of CFD code FLUENT-12 for supercritical water in vertical bare tubes

Energy Technology Data Exchange (ETDEWEB)

Farah, A.; Haines, P.; Harvel, G.; Pioro, I., E-mail: amjad.farah@yahoo.com, E-mail: patrickjhaines@gmail.com, E-mail: glenn.harvel@uoit.ca, E-mail: igor.pioro@uoit.ca [Univ. of Ontario Inst. of Technology, Faculty of Energy Systems and Nuclear Science,Oshawa, Ontario (Canada)

2012-07-01

The ability to use FLUENT 12 or other CFD software to accurately model supercritical water flow through various geometries in diabatic conditions is integral to research involving coal-fired power plants as well as Supercritical Water-cooled Reactors (SCWR). The cost and risk associated with constructing supercritical water test loops are far too great to use in a university setting. Previous work has shown that FLUENT 12, specifically realizable k-ε model, can reasonably predict the bulk and wall temperature distributions of externally heated vertical bare tubes for cases with relatively low heat and mass fluxes. However, sizeable errors were observed for other cases, often those which involved large heat fluxes that produce deteriorated heat transfer (DHT) regimes. The goal of this research is to gain a more complete understanding of how FLUENT 12 models supercritical water cases and where errors can be expected to occur. One control case is selected where expected changes in bulk and wall temperatures occur and they match empirical correlations' predictions, and the operating parameters are varied individually to gauge their effect on FLUENT's solution. The model used is the realizable k-ε, and the parameters altered are inlet pressure, mass flux, heat flux, and inlet temperature. (author)

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....

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.

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.

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.

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

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.

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.

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...

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...

Residence Time Distribution (RTD) Prediction in Mixing Vessel via CFD Simulation Using Fluent: An Overview

International Nuclear Information System (INIS)

Mohd Rizal Mamat; Azraf Azman; Noraishah Othman

2011-01-01

A prototype mixing vessel has been developed by Industrial Technology Division and installed at a multiphase testing facility in MINT Tech Park. A task to investigate the mixing vessel performance using Computational Fluid Dynamics (CFD) has been undertaken using the available FLUENT software in Malaysian Nuclear Agency. This paper discusses the modeling and simulation done in obtaining the Residence Time Distribution (RTD) for the mixing vessel using FLUENT. (author)

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.

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.

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.

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.

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.

Mechanisms for scheduling games with selfish players

NARCIS (Netherlands)

Hoeksma, R.P.

2015-01-01

Many challenges in operations research involve optimization. In particular, scheduling treats the optimal planning of tasks. This thesis focuses on machine scheduling models, where a number of tasks, called jobs, need to be scheduled on one or more machines. The outcome is determined by which job is

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

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

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

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.

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...

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

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.

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.

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.

Fluent Aphasia From Herpes Simplex Encephalitis

Directory of Open Access Journals (Sweden)

Fariba Yadegari

2006-09-01

Full Text Available The present case report introduces a patient with fluent aphasia, anterograde amnesia and anosmia due to herpes simplex encephalitis after her first delivery. The left medial temporal lobe was one of the main areas involved. On aphasia testing she showed severe anomia on both confrontation and free recall, agraphia, alexia, repetition disorder and some auditory comprehension impairments. Therapy was focused on the following issues: phonological output lexicon , using graphemes as a source of reestablishing phonological representation; describing pictures to reinforce free recall and self-cuing word retrieval strategies; sequencing the events for language memory reinforcement, etc. Results showed improvement in confrontational naming, free recall, correct responses without priming, writing on dictation, spontaneous writing and reading comprehension.

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.

Development of Watch Schedule Using Rules Approach

Science.gov (United States)

Jurkevicius, Darius; Vasilecas, Olegas

The software for schedule creation and optimization solves a difficult, important and practical problem. The proposed solution is an online employee portal where administrator users can create and manage watch schedules and employee requests. Each employee can login with his/her own account and see his/her assignments, manage requests, etc. Employees set as administrators can perform the employee scheduling online, manage requests, etc. This scheduling software allows users not only to see the initial and optimized watch schedule in a simple and understandable form, but also to create special rules and criteria and input their business. The system using rules automatically will generate watch schedule.

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. 

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.

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.

MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING

Directory of Open Access Journals (Sweden)

Ladislav Rosocha

2015-07-01

Full Text Available Purpose: The efficiency of medical staff is a fundamental feature of healthcare facilities quality. Therefore the better implementation of their preferences into the scheduling problem might not only rise the work-life balance of doctors and nurses, but also may result into better patient care. This paper focuses on optimization of medical staff preferences considering the scheduling problem.Methodology/Approach: We propose a medical staff scheduling algorithm based on simulated annealing, a well-known method from statistical thermodynamics. We define hard constraints, which are linked to legal and working regulations, and minimize the violations of soft constraints, which are related to the quality of work, psychic, and work-life balance of staff.Findings: On a sample of 60 physicians and nurses from gynecology department we generated monthly schedules and optimized their preferences in terms of soft constraints. Our results indicate that the final value of objective function optimized by proposed algorithm is more than 18-times better in violations of soft constraints than initially generated random schedule that satisfied hard constraints.Research Limitation/implication: Even though the global optimality of final outcome is not guaranteed, desirable solutionwas obtained in reasonable time. Originality/Value of paper: We show that designed algorithm is able to successfully generate schedules regarding hard and soft constraints. Moreover, presented method is significantly faster than standard schedule generation and is able to effectively reschedule due to the local neighborhood search characteristics of simulated annealing.

Diversity in the lexical and syntactic abilities of fluent aphasic speakers

NARCIS (Netherlands)

Bastiaanse, Y.R.M.; Edwards, S.

In an earlier study by the authors, it was suggested that some fluent aphasic speakers exhibit subtle grammatical deficits. In this paper, how far lexical accessing problems might account for these deficits is considered. For this study, spontaneous speech data collected from two groups of aphasic

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.

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.

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.

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

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.

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

Car painting process scheduling with harmony search algorithm

Science.gov (United States)

Syahputra, M. F.; Maiyasya, A.; Purnamawati, S.; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Automotive painting program in the process of painting the car body by using robot power, making efficiency in the production system. Production system will be more efficient if pay attention to scheduling of car order which will be done by considering painting body shape of car. Flow shop scheduling is a scheduling model in which the job-job to be processed entirely flows in the same product direction / path. Scheduling problems often arise if there are n jobs to be processed on the machine, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. Harmony Search Algorithm is a metaheuristic optimization algorithm based on music. The algorithm is inspired by observations that lead to music in search of perfect harmony. This musical harmony is in line to find optimal in the optimization process. Based on the tests that have been done, obtained the optimal car sequence with minimum makespan value.

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...

Modelling of plasma processes based on the commercial computational fluid dynamics (CFD) package FLUENT

International Nuclear Information System (INIS)

Sazhin, S.S.

1995-01-01

Traditional applications of the commercial CFD package FLUENT include modelling of gas and liquid flows, combustion processes, thermal radiation exchange, particle dynamics and related processes of industrial interest. Recently, however, the area of applications of this package has been extended to modelling of new processes such as CO 2 laser discharges and the solution of the Boltzmann equation. Results of this modelling were reported at XXI International Conference on Phenomena in Ionized gases in Bochum and were later published in a number of research papers. The aim of this report is to summarize some further latest developments of the FLUENT package aimed to adjust it to the needs of modelling of plasma processes including those in ionized gases. The simplest way to modify this package is to include Amper force into Navier-Stokes equation and Ohm heating term into the enthalpy equation. In most cases, however, electric currents and electric and magnetic fields used in these equations cannot be assumed to be a priori known as they depend on plasma dynamics (distribution of velocities and pressures) and thermodynamics (distribution of temperatures) which implicitly enter into Maxwell or any equivalent electromagnetic equations. This makes it necessary to include these electromagnetic equations into the general iteration loop used in FLUENT

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.

Artificial grammar learning in vascular and progressive non-fluent aphasias.

Science.gov (United States)

Cope, Thomas E; Wilson, Benjamin; Robson, Holly; Drinkall, Rebecca; Dean, Lauren; Grube, Manon; Jones, P Simon; Patterson, Karalyn; Griffiths, Timothy D; Rowe, James B; Petkov, Christopher I

2017-09-01

Patients with non-fluent aphasias display impairments of expressive and receptive grammar. This has been attributed to deficits in processing configurational and hierarchical sequencing relationships. This hypothesis had not been formally tested. It was also controversial whether impairments are specific to language, or reflect domain general deficits in processing structured auditory sequences. Here we used an artificial grammar learning paradigm to compare the abilities of controls to participants with agrammatic aphasia of two different aetiologies: stroke and frontotemporal dementia. Ten patients with non-fluent variant primary progressive aphasia (nfvPPA), 12 with non-fluent aphasia due to stroke, and 11 controls implicitly learned a novel mixed-complexity artificial grammar designed to assess processing of increasingly complex sequencing relationships. We compared response profiles for otherwise identical sequences of speech tokens (nonsense words) and tone sweeps. In all three groups the ability to detect grammatical violations varied with sequence complexity, with performance improving over time and being better for adjacent than non-adjacent relationships. Patients performed less well than controls overall, and this was related more strongly to aphasia severity than to aetiology. All groups improved with practice and performed well at a control task of detecting oddball nonwords. Crucially, group differences did not interact with sequence complexity, demonstrating that aphasic patients were not disproportionately impaired on complex structures. Hierarchical cluster analysis revealed that response patterns were very similar across all three groups, but very different between the nonsense word and tone tasks, despite identical artificial grammar structures. Overall, we demonstrate that agrammatic aphasics of two different aetiologies are not disproportionately impaired on complex sequencing relationships, and that the learning of phonological and non

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.

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.

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...

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...

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.

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.

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...

Construction schedules slack time minimizing

Science.gov (United States)

Krzemiński, Michał

2017-07-01

The article presents two copyright models for minimizing downtime working brigades. Models have been developed for construction schedules performed using the method of work uniform. Application of flow shop models is possible and useful for the implementation of large objects, which can be divided into plots. The article also presents a condition describing gives which model should be used, as well as a brief example of optimization schedule. The optimization results confirm the legitimacy of the work on the newly-developed models.

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

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.

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

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)

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.

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.

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.

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.

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.

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.

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....

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.

Gain-Scheduled Control of a Fossil-Fired Power Plant Boiler

DEFF Research Database (Denmark)

Hangstrup, M.; Stoustrup, Jakob; Andersen, Palle

1999-01-01

-scheduling which interpolates between unstable controllers is not allowed using traditional schemes. The results show that a considerable optimization of the conventional controlled system is obtainable. Also the gain-scheduled optimizing controller is seen to have a superior performance compared to the fixed LTI......In this paper the objective is to optimize the control of a coal fired 250 MW power plant boiler. The conventional control system is supplemented with a multivariable optimizing controller operating in parallel with the conventional control system. Due to the strong dependence of the gains...... and dynamics upon the load, it is beneficial to consider a gain-scheduling control approach. Optimization using complex mu synthesis results in unstable LTI controllers in some operating points of the boiler. A recent gain-scheduling approach allowing for unstable fixed LTI controllers is applied. Gain...

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

Directory of Open Access Journals (Sweden)

Chandramouli Anandaraman

2011-10-01

Full Text Available Job Shop Scheduling Problem (JSSP and Flow Shop Scheduling Problem (FSSP are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.

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

Endogenous scheduling preferences and congestion

DEFF Research Database (Denmark)

Fosgerau, Mogens; Small, Kenneth

2010-01-01

and leisure, but agglomeration economies at home and at work lead to scheduling preferences forming endogenously. Using bottleneck congestion technology, we obtain an equilibrium queuing pattern consistent with a general version of the Vickrey bottleneck model. However, the policy implications are different....... Compared to the predictions of an analyst observing untolled equilibrium and taking scheduling preferences as exogenous, we find that both the optimal capacity and the marginal external cost of congestion have changed. The benefits of tolling are greater, and the optimal time varying toll is different....

Multi-Satellite Observation Scheduling for Large Area Disaster Emergency Response

Science.gov (United States)

Niu, X. N.; Tang, H.; Wu, L. X.

2018-04-01

an optimal imaging plan, plays a key role in coordinating multiple satellites to monitor the disaster area. In the paper, to generate imaging plan dynamically according to the disaster relief, we propose a dynamic satellite task scheduling method for large area disaster response. First, an initial robust scheduling scheme is generated by a robust satellite scheduling model in which both the profit and the robustness of the schedule are simultaneously maximized. Then, we use a multi-objective optimization model to obtain a series of decomposing schemes. Based on the initial imaging plan, we propose a mixed optimizing algorithm named HA_NSGA-II to allocate the decomposing results thus to obtain an adjusted imaging schedule. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of rapid response using satellite resources and used to evaluate the performance of the proposed method with state-of-the-art approaches. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

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.

The robust schedule - A link to improved workflow

DEFF Research Database (Denmark)

Lindhard, Søren; Wandahl, Søren

2012-01-01

-down the contractors, and force them to rigorously adhere to the initial schedule. If delayed the work-pace or manpower has to be increased to observe the schedule. In attempt to improve productivity three independent site-mangers have been interviewed about time-scheduling. Their experiences and opinions have been...... analyzed and weaknesses in existing time scheduling have been found. The findings showed a negative side effect of keeping the schedule to tight. A too tight schedule is inflexible and cannot absorb variability in production. Flexibility is necessary because of the contractors interacting and dependable....... The result is a chaotic, complex and uncontrolled construction site. Furthermore, strict time limits entail the workflow to be optimized under non-optimal conditions. Even though productivity seems to be increasing, productivity per man-hour is decreasing resulting in increased cost. To increase productivity...

Application of coupled symbolic and numeric processing to an advanced scheduling system for plant construction

International Nuclear Information System (INIS)

Kobayashi, Yasuhiro; Takamoto, Masanori; Nonaka, Hisanori; Yamada, Naoyuki

1994-01-01

A scheduling system has been developed by integrating symbolic processing functions for constraint handling and modification guidance, with numeric processing functions for schedule optimization and evaluation. The system is composed of an automatic schedule generation module, interactive schedule revision module and schedule evaluation module. The goal of the problem solving is the flattening of the daily resources requirement throughout the scheduling period. The automatic schedule generation module optimizes the initial schedule according to the formulatable portion of requirement description specified in a predicate-like language. A planning engineer refines the near-goal schedule through a knowledge-based interactive optimization process to obtain the goal schedule which fully covers the requirement description, with the interactive schedule revision module and schedule evaluation module. A scheduling system has been implemented on the basis of the proposed problem solving framework and experimentally applied to real-world sized scheduling problems for plant construction. With a result of the overall plant construction scheduling, a section schedule optimization process is described with the emphasis on the symbolic processing functions. (author)

Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia.

Science.gov (United States)

Mandelli, Maria Luisa; Vilaplana, Eduard; Brown, Jesse A; Hubbard, H Isabel; Binney, Richard J; Attygalle, Suneth; Santos-Santos, Miguel A; Miller, Zachary A; Pakvasa, Mikhail; Henry, Maya L; Rosen, Howard J; Henry, Roland G; Rabinovici, Gil D; Miller, Bruce L; Seeley, William W; Gorno-Tempini, Maria Luisa

2016-10-01

Neurodegeneration has been hypothesized to follow predetermined large-scale networks through the trans-synaptic spread of toxic proteins from a syndrome-specific epicentre. To date, no longitudinal neuroimaging study has tested this hypothesis in vivo in frontotemporal dementia spectrum disorders. The aim of this study was to demonstrate that longitudinal progression of atrophy in non-fluent/agrammatic variant primary progressive aphasia spreads over time from a syndrome-specific epicentre to additional regions, based on their connectivity to the epicentre in healthy control subjects. The syndrome-specific epicentre of the non-fluent/agrammatic variant of primary progressive aphasia was derived in a group of 10 mildly affected patients (clinical dementia rating equal to 0) using voxel-based morphometry. From this region, the inferior frontal gyrus (pars opercularis), we derived functional and structural connectivity maps in healthy controls (n = 30) using functional magnetic resonance imaging at rest and diffusion-weighted imaging tractography. Graph theory analysis was applied to derive functional network features. Atrophy progression was calculated using voxel-based morphometry longitudinal analysis on 34 non-fluent/agrammatic patients. Correlation analyses were performed to compare volume changes in patients with connectivity measures of the healthy functional and structural speech/language network. The default mode network was used as a control network. From the epicentre, the healthy functional connectivity network included the left supplementary motor area and the prefrontal, inferior parietal and temporal regions, which were connected through the aslant, superior longitudinal and arcuate fasciculi. Longitudinal grey and white matter changes were found in the left language-related regions and in the right inferior frontal gyrus. Functional connectivity strength in the healthy speech/language network, but not in the default network, correlated with

Schedule Matters: Understanding the Relationship between Schedule Delays and Costs on Overruns

Science.gov (United States)

Majerowicz, Walt; Shinn, Stephen A.

2016-01-01

This paper examines the relationship between schedule delays and cost overruns on complex projects. It is generally accepted by many project practitioners that cost overruns are directly related to schedule delays. But what does "directly related to" actually mean? Some reasons or root causes for schedule delays and associated cost overruns are obvious, if only in hindsight. For example, unrealistic estimates, supply chain difficulties, insufficient schedule margin, technical problems, scope changes, or the occurrence of risk events can negatively impact schedule performance. Other factors driving schedule delays and cost overruns may be less obvious and more difficult to quantify. Examples of these less obvious factors include project complexity, flawed estimating assumptions, over-optimism, political factors, "black swan" events, or even poor leadership and communication. Indeed, is it even possible the schedule itself could be a source of delay and subsequent cost overrun? Through literature review, surveys of project practitioners, and the authors' own experience on NASA programs and projects, the authors will categorize and examine the various factors affecting the relationship between project schedule delays and cost growth. The authors will also propose some ideas for organizations to consider to help create an awareness of the factors which could cause or influence schedule delays and associated cost growth on complex projects.

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

School Competence and Fluent Academic Performance: Informing Assessment of Educational Outcomes in Survivors of Pediatric Medulloblastoma.

Science.gov (United States)

Holland, Alice Ann; Hughes, Carroll W; Stavinoha, Peter L

2015-01-01

Academic difficulties are widely acknowledged but not adequately studied in survivors of pediatric medulloblastoma. Although most survivors require special education services and are significantly less likely than healthy peers to finish high school, measured academic skills are typically average. This study sought to identify potential factors associated with academic difficulties in this population and focused on school competence and fluent academic performance. Thirty-six patients (ages 7-18 years old) were recruited through the Departments of Neurosurgery and Neuro-Oncology at Children's Medical Center Dallas and Cook Children's Medical Center in Fort Worth, TX. Participants completed a neuropsychological screening battery including selected Woodcock-Johnson III Tests of Achievement subtests. Parents completed the Child Behavior Checklist. School competence was significantly correlated with measured academic skills and fluency. Basic academic skill development was broadly average, in contrast to significantly worse fluent academic performance. School competence may have utility as a measure estimating levels of educational success in this population. Additionally, academic difficulties experienced by childhood medulloblastoma survivors may be better captured by measuring deficits in fluent academic performance rather than skills. Identification of these potential factors associated with educational outcomes of pediatric medulloblastoma survivors has significant implications for research, clinical assessment, and academic services/interventions.

Constraint-induced aphasia therapy versus intensive semantic treatment in fluent aphasia.

Science.gov (United States)

Wilssens, Ineke; Vandenborre, Dorien; van Dun, Kim; Verhoeven, Jo; Visch-Brink, Evy; Mariën, Peter

2015-05-01

The authors compared the effectiveness of 2 intensive therapy methods: Constraint-Induced Aphasia Therapy (CIAT; Pulvermüller et al., 2001) and semantic therapy (BOX; Visch-Brink & Bajema, 2001). Nine patients with chronic fluent aphasia participated in a therapy program to establish behavioral treatment outcomes. Participants were randomly assigned to one of two groups (CIAT or BOX). Intensive therapy significantly improved verbal communication. However, BOX treatment showed a more pronounced improvement on two communication-namely, a standardized assessment for verbal communication, the Amsterdam Nijmegen Everyday Language Test (Blomert, Koster, & Kean, 1995), and a subjective rating scale, the Communicative Effectiveness Index (Lomas et al., 1989). All participants significantly improved on one (or more) subtests of the Aachen Aphasia Test (Graetz, de Bleser, & Willmes, 1992), an impairment-focused assessment. There was a treatment-specific effect. BOX treatment had a significant effect on language comprehension and semantics, whereas CIAT treatment affected language production and phonology. The findings indicate that in patients with fluent aphasia, (a) intensive treatment has a significant effect on language and verbal communication, (b) intensive therapy results in selective treatment effects, and (c) an intensive semantic treatment shows a more striking mean improvement on verbal communication in comparison with communication-based CIAT treatment.

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)

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.

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.

Influência da extensão da palavra e local da ruptura na sílaba na fala de adolescentes e adultos gagos e fluentes The influence of word extension and the position of speech disruption within the syllable structure in the speech of stuttering and fluent adolescents and adults

Directory of Open Access Journals (Sweden)

Fabiola Staróbole Juste

2011-03-01

Full Text Available OBJETIVO: Verificar a influência da extensão da palavra e da posição dentro da sílaba na ocorrência de rupturas de fala em adolescentes e adultos gagos e fluentes. MÉTODOS: Participaram do estudo 120 indivíduos, de ambos os gêneros, com idades entre 12 e 49 anos e 11 meses, divididos em quatro grupos: Grupo de Pesquisa 1 (GP1 - 30 adolescentes gagos; Grupo de Pesquisa 2 (GP2 - 30 adultos gagos; Grupo Controle 1 (GC1 - 30 adolescentes fluentes; Grupo Controle 2 (GC2 - 30 adultos fluentes. Foram coletadas amostras de fala contendo 200 sílabas fluentes. Nas amostras de fala, as palavras rompidas foram analisadas quanto à sua extensão (monossílaba, dissílaba, trissílaba e polissílaba e quanto ao local da ruptura na sílaba (posição de ataque, núcleo ou coda. RESULTADOS: Quanto à extensão, os grupos de falantes gagos não apresentaram predominância de rupturas em nenhuma das possibilidades, já para os falantes fluentes, as rupturas foram mais frequentes nas palavras monossilábicas. Em relação ao local da ruptura na sílaba, os falantes gagos apresentaram ruptura principalmente na posição de ataque, enquanto os fluentes apresentaram mais rupturas em posição de núcleo. CONCLUSÃO: De maneira geral, esses resultados sugerem que as rupturas de fala ocorrem no início das palavras e sílabas, indicando uma dificuldade na temporalização entre as velocidades de seleção e ativação fonológica, que ocorre independentemente da extensão da palavra.PURPOSE: To verify the influence of word extension and the position of speech disruption within the syllable structure in the speech of stuttering and fluent adolescents and adults. METHODS: Participants were 120 individuals of both genders, with ages between 12 and 49 years and 11 months, who were divided into four groups: Research Group 1 (RG1 - 30 adolescents who stutter; Research Group 2 (RG2 - 30 adults who stutter; Control Group 1 (CG1 - 30 adolescents who do not stutter

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

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。

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...

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.

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.

Performance comparison of some evolutionary algorithms on job shop scheduling problems

Science.gov (United States)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Computer-aided resource planning and scheduling for radiological services

Science.gov (United States)

Garcia, Hong-Mei C.; Yun, David Y.; Ge, Yiqun; Khan, Javed I.

1996-05-01

There exists tremendous opportunity in hospital-wide resource optimization based on system integration. This paper defines the resource planning and scheduling requirements integral to PACS, RIS and HIS integration. An multi-site case study is conducted to define the requirements. A well-tested planning and scheduling methodology, called Constrained Resource Planning model, has been applied to the chosen problem of radiological service optimization. This investigation focuses on resource optimization issues for minimizing the turnaround time to increase clinical efficiency and customer satisfaction, particularly in cases where the scheduling of multiple exams are required for a patient. How best to combine the information system efficiency and human intelligence in improving radiological services is described. Finally, an architecture for interfacing a computer-aided resource planning and scheduling tool with the existing PACS, HIS and RIS implementation is presented.

Distributed Research Project Scheduling Based on Multi-Agent Methods

Directory of Open Access Journals (Sweden)

Constanta Nicoleta Bodea

2011-01-01

Full Text Available Different project planning and scheduling approaches have been developed. The Operational Research (OR provides two major planning techniques: CPM (Critical Path Method and PERT (Program Evaluation and Review Technique. Due to projects complexity and difficulty to use classical methods, new approaches were developed. Artificial Intelligence (AI initially promoted the automatic planner concept, but model-based planning and scheduling methods emerged later on. The paper adresses the project scheduling optimization problem, when projects are seen as Complex Adaptive Systems (CAS. Taken into consideration two different approaches for project scheduling optimization: TCPSP (Time- Constrained Project Scheduling and RCPSP (Resource-Constrained Project Scheduling, the paper focuses on a multiagent implementation in MATLAB for TCSP. Using the research project as a case study, the paper includes a comparison between two multi-agent methods: Genetic Algorithm (GA and Ant Colony Algorithm (ACO.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

Science.gov (United States)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

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...

Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching

Science.gov (United States)

Shen, Kaiming; Yu, Wei

2018-05-01

This two-part paper develops novel methodologies for using fractional programming (FP) techniques to design and optimize communication systems. Part I of this paper proposes a new quadratic transform for FP and treats its application for continuous optimization problems. In this Part II of the paper, we study discrete problems, such as those involving user scheduling, which are considerably more difficult to solve. Unlike the continuous problems, discrete or mixed discrete-continuous problems normally cannot be recast as convex problems. In contrast to the common heuristic of relaxing the discrete variables, this work reformulates the original problem in an FP form amenable to distributed combinatorial optimization. The paper illustrates this methodology by tackling the important and challenging problem of uplink coordinated multi-cell user scheduling in wireless cellular systems. Uplink scheduling is more challenging than downlink scheduling, because uplink user scheduling decisions significantly affect the interference pattern in nearby cells. Further, the discrete scheduling variable needs to be optimized jointly with continuous variables such as transmit power levels and beamformers. The main idea of the proposed FP approach is to decouple the interaction among the interfering links, thereby permitting a distributed and joint optimization of the discrete and continuous variables with provable convergence. The paper shows that the well-known weighted minimum mean-square-error (WMMSE) algorithm can also be derived from a particular use of FP; but our proposed FP-based method significantly outperforms WMMSE when discrete user scheduling variables are involved, both in term of run-time efficiency and optimizing results.

Realistic Scheduling Mechanism for Smart Homes

Directory of Open Access Journals (Sweden)

Danish Mahmood

2016-03-01

Full Text Available In this work, we propose a Realistic Scheduling Mechanism (RSM to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO, optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.

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...

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.

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

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...

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...

On program restructuring, scheduling, and communication for parallel processor systems

Energy Technology Data Exchange (ETDEWEB)

Polychronopoulos, Constantine D. [Univ. of Illinois, Urbana, IL (United States)

1986-08-01

This dissertation discusses several software and hardware aspects of program execution on large-scale, high-performance parallel processor systems. The issues covered are program restructuring, partitioning, scheduling and interprocessor communication, synchronization, and hardware design issues of specialized units. All this work was performed focusing on a single goal: to maximize program speedup, or equivalently, to minimize parallel execution time. Parafrase, a Fortran restructuring compiler was used to transform programs in a parallel form and conduct experiments. Two new program restructuring techniques are presented, loop coalescing and subscript blocking. Compile-time and run-time scheduling schemes are covered extensively. Depending on the program construct, these algorithms generate optimal or near-optimal schedules. For the case of arbitrarily nested hybrid loops, two optimal scheduling algorithms for dynamic and static scheduling are presented. Simulation results are given for a new dynamic scheduling algorithm. The performance of this algorithm is compared to that of self-scheduling. Techniques for program partitioning and minimization of interprocessor communication for idealized program models and for real Fortran programs are also discussed. The close relationship between scheduling, interprocessor communication, and synchronization becomes apparent at several points in this work. Finally, the impact of various types of overhead on program speedup and experimental results are presented.

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.

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

The Synthesis of a Hardware Scheduler for Non-Manifest Loops

NARCIS (Netherlands)

Mansour, O.; Molenkamp, Egbert; Krol, Th.

This paper addresses the hardware implementation of a dynamic scheduler for non-manifest data dependent periodic loops. Static scheduling techniques which are known to give near optimal scheduling-solutions for manifest loops, fail at scheduling non-manifest loops, since they lack the run time

Utilization Bound of Non-preemptive Fixed Priority Schedulers

Science.gov (United States)

Park, Moonju; Chae, Jinseok

It is known that the schedulability of a non-preemptive task set with fixed priority can be determined in pseudo-polynomial time. However, since Rate Monotonic scheduling is not optimal for non-preemptive scheduling, the applicability of existing polynomial time tests that provide sufficient schedulability conditions, such as Liu and Layland's bound, is limited. This letter proposes a new sufficient condition for non-preemptive fixed priority scheduling that can be used for any fixed priority assignment scheme. It is also shown that the proposed schedulability test has a tighter utilization bound than existing test methods.

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

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.

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%.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

Science.gov (United States)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

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

Nuclear power plant maintenance scheduling dilemma: a genetic algorithm approach

International Nuclear Information System (INIS)

Mahdavi, M.H.; Modarres, M.

2004-01-01

There are huge numbers of components scheduled for maintenance when a nuclear power plant is shut down. Among these components, a number of them are safety related which their operability as well as reliability when plant becomes up is main concerns. Not performing proper maintenance on this class of components/system would impose substantial risk on operating the NPP. In this paper a new approach based on genetic algorithms is presented to optimize the NPP maintenance schedule during shutdown. following this approach the cost incurred by maintenance activities for each schedule is balanced with the risk imposed by the maintenance scheduling plan to the plant operation status when it is up. The risk model implemented in the GA scheduler as its evaluation function is developed on the basis of the probabilistic risk assessment methodology. the Ga optimizers itself is shown to be superior compared to other optimization methods such as the monte carlo technique

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

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

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.

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.

Planning and Scheduling for Fleets of Earth Observing Satellites

Science.gov (United States)

Frank, Jeremy; Jonsson, Ari; Morris, Robert; Smith, David E.; Norvig, Peter (Technical Monitor)

2001-01-01

We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.

Robust and Flexible Scheduling with Evolutionary Computation

DEFF Research Database (Denmark)

Jensen, Mikkel T.

Over the last ten years, there have been numerous applications of evolutionary algorithms to a variety of scheduling problems. Like most other research on heuristic scheduling, the primary aim of the research has been on deterministic formulations of the problems. This is in contrast to real world...... scheduling problems which are usually not deterministic. Usually at the time the schedule is made some information about the problem and processing environment is available, but this information is uncertain and likely to change during schedule execution. Changes frequently encountered in scheduling...... environments include machine breakdowns, uncertain processing times, workers getting sick, materials being delayed and the appearance of new jobs. These possible environmental changes mean that a schedule which was optimal for the information available at the time of scheduling can end up being highly...

Integrated network design and scheduling problems :

Energy Technology Data Exchange (ETDEWEB)

Nurre, Sarah G.; Carlson, Jeffrey J.

2014-01-01

We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

A review of metaheuristic scheduling techniques in cloud computing

Directory of Open Access Journals (Sweden)

Mala Kalra

2015-11-01

Full Text Available Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO, Genetic Algorithm (GA and Particle Swarm Optimization (PSO, and two novel techniques: League Championship Algorithm (LCA and BAT algorithm.

Intelligent Scheduling of a Grid-Connected Heat Pump in a Danish Detached House

DEFF Research Database (Denmark)

Gianniou, Panagiota; Foteinaki, Kyriaki; Heller, Alfred

This study proposes a methodology for intelligent scheduling of a heat pump installed in a refurbished grid-connected detached house in Denmark. This scheduling is conducted through the coupling of a dynamic building simulation tool with an optimization tool. The optimization of the operation of ...... thermal comfort conditions. The proposed methodology bridges dynamic building modelling with optimization of real-time operation of HVAC systems offering a detailed model for building physics, especially regarding thermal mass and a stochastic price-based control....... of the system is based on a price-signal considering a three-day period for different weather cases. The results show that the optimal scheduling of the system is successful in terms of reducing the peak load during times when electricity prices are high, thus achieving cost savings as well as maintaining good......This study proposes a methodology for intelligent scheduling of a heat pump installed in a refurbished grid-connected detached house in Denmark. This scheduling is conducted through the coupling of a dynamic building simulation tool with an optimization tool. The optimization of the operation...

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.

Early-Stage Chunking of Finger Tapping Sequences by Persons Who Stutter and Fluent Speakers

Science.gov (United States)

Smits-Bandstra, Sarah; De Nil, Luc F.

2013-01-01

This research note explored the hypothesis that chunking differences underlie the slow finger-tap sequencing performance reported in the literature for persons who stutter (PWS) relative to fluent speakers (PNS). Early-stage chunking was defined as an immediate and spontaneous tendency to organize a long sequence into pauses, for motor planning,…

ENHANCED HYBRID PSO – ACO ALGORITHM FOR GRID SCHEDULING

Directory of Open Access Journals (Sweden)

P. Mathiyalagan

2010-07-01

Full Text Available Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, heuristic approaches based on particle swarm optimization and ant colony optimization algorithms are adopted for solving task scheduling problems in grid environment. Particle Swarm Optimization (PSO is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The ACO algorithm is improved by modifying the pheromone updating rule. ACO algorithm is hybridized with PSO algorithm for efficient result and better convergence in PSO algorithm.

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

Using Integer Programming for Airport Service Planning in Staff Scheduling

Directory of Open Access Journals (Sweden)

W.H. Ip

2010-09-01

Full Text Available Reliability and safety in flight is extremely necessary and that depend on the adoption of proper maintenance system. Therefore, it is essential for aircraft maintenance companies to perform the manpower scheduling efficiently. One of the objectives of this paper is to provide an Integer Programming approach to determine the optimal solutions to aircraft maintenance planning and scheduling and hence the planning and scheduling processes can become more efficient and effective. Another objective is to develop a set of computational schedules for maintenance manpower to cover all scheduled flights. In this paper, a sequential methodology consisting of 3 stages is proposed. They are initial maintenance demand schedule, the maintenance pairing and the maintenance group(s assignment. Since scheduling would split up into different stages, different mathematical techniques have been adopted to cater for their own problem characteristics. Microsoft Excel would be used. Results from the first stage and second stage would be inputted into integer programming model using Microsoft Excel Solver to find the optimal solution. Also, Microsoft Excel VBA is used for devising a scheduling system in order to reduce the manual process and provide a user friendly interface. For the results, all can be obtained optimal solution and the computation time is reasonable and acceptable. Besides, the comparison of the peak time and non-peak time is discussed.

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.

Clinicopathologic analysis of progressive non-fluent aphasia and corticobasal degeneration:Case report and review

Directory of Open Access Journals (Sweden)

Paulo Roberto de Brito-Marques

Full Text Available Abstract Objective: To investigate progressive non-fluent aphasia and histopathologically-proven corticobasal degeneration. Methods: We evaluated symptoms, signs, neuropsychological deficits, and radiology data longitudinally, in a patient with autopsy-proven corticobasal degeneration and correlated these observations directly to the neuroanatomic distribution of the disease. Results: At presentation, a specific pattern of cognitive impairment was evident with an extreme extrapyramidal motor abnormality. Follow-up examination revealed persistent impairment of praxis and executive functioning, progressive worsening of language performance, and moderately preserved memory. The motor disorder manifested and worsened as the condition progressed. Many of the residual nerve cells were ballooned and achromatic with eccentric nuclei. Tau-immunoreactive pathology was significantly more prominent in neurons in the frontal and parietal cortices and dentate nuclei than in temporal neocortex, hippocampi and brainstem. Conclusion: The clinical diagnosis of progressive non-fluent aphasia secondary to corticobasal degeneration hinged on a specific pattern of impaired cognition as well as an extrapyramidal motor disorder, reflecting the neuroanatomic distribution of the disease in frontal and anterior temporal cortices and the dentate nuclei.

Clinicopathologic analysis of progressive non-fluent aphasia and corticobasal degeneration: Case report and review.

Science.gov (United States)

de Brito-Marques, Paulo Roberto; Vieira-Mello, Roberto José; Montenegro, Luciano; Aragão, Maria de Fátima Vasco

2011-01-01

To investigate progressive non-fluent aphasia and histopathologically-proven corticobasal degeneration. We evaluated symptoms, signs, neuropsychological deficits, and radiology data longitudinally, in a patient with autopsy-proven corticobasal degeneration and correlated these observations directly to the neuroanatomic distribution of the disease. At presentation, a specific pattern of cognitive impairment was evident with an extreme extrapyramidal motor abnormality. Follow-up examination revealed persistent impairment of praxis and executive functioning, progressive worsening of language performance, and moderately preserved memory. The motor disorder manifested and worsened as the condition progressed. Many of the residual nerve cells were ballooned and achromatic with eccentric nuclei. Tau-immunoreactive pathology was significantly more prominent in neurons in the frontal and parietal cortices and dentate nuclei than in temporal neocortex, hippocampi and brainstem. The clinical diagnosis of progressive non-fluent aphasia secondary to corticobasal degeneration hinged on a specific pattern of impaired cognition as well as an extrapyramidal motor disorder, reflecting the neuroanatomic distribution of the disease in frontal and anterior temporal cortices and the dentate nuclei.

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.

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.

Nurse Scheduling by Cooperative GA with Effective Mutation Operator

Science.gov (United States)

Ohki, Makoto

In this paper, we propose an effective mutation operators for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). The nurse scheduling is a very difficult task, because NSP is a complex combinatorial optimizing problem for which many requirements must be considered. In real hospitals, the schedule changes frequently. The changes of the shift schedule yields various problems, for example, a fall in the nursing level. We describe a technique of the reoptimization of the nurse schedule in response to a change. The conventional CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the unfavorable situation because it is inferior to ability for global search. When the optimization stagnates for long generation cycle, a searching point, population in this case, would be caught in a wide local minimum area. To escape such local minimum area, small change in a population should be required. Based on such consideration, we propose a mutation operator activated depending on the optimization speed. When the optimization stagnates, in other words, when the optimization speed decreases, the mutation yields small changes in the population. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator requires two well-defined parameters. This means that user have to consider the value of these parameters carefully. To solve this problem, we propose a periodic mutation operator which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.

Harmonious personnel scheduling

NARCIS (Netherlands)

Fijn van Draat, Laurens; Post, Gerhard F.; Veltman, Bart; Winkelhuijzen, Wessel

2006-01-01

The area of personnel scheduling is very broad. Here we focus on the ‘shift assignment problem’. Our aim is to discuss how ORTEC HARMONY handles this planning problem. In particular we go into the structure of the optimization engine in ORTEC HARMONY, which uses techniques from genetic algorithms,

SPANR planning and scheduling

Science.gov (United States)

Freund, Richard F.; Braun, Tracy D.; Kussow, Matthew; Godfrey, Michael; Koyama, Terry

2001-07-01

SPANR (Schedule, Plan, Assess Networked Resources) is (i) a pre-run, off-line planning and (ii) a runtime, just-in-time scheduling mechanism. It is designed to support primarily commercial applications in that it optimizes throughput rather than individual jobs (unless they have highest priority). Thus it is a tool for a commercial production manager to maximize total work. First the SPANR Planner is presented showing the ability to do predictive 'what-if' planning. It can answer such questions as, (i) what is the overall effect of acquiring new hardware or (ii) what would be the effect of a different scheduler. The ability of the SPANR Planner to formulate in advance tree-trimming strategies is useful in several commercial applications, such as electronic design or pharmaceutical simulations. The SPANR Planner is demonstrated using a variety of benchmarks. The SPANR Runtime Scheduler (RS) is briefly presented. The SPANR RS can provide benefit for several commercial applications, such as airframe design and financial applications. Finally a design is shown whereby SPANR can provide scheduling advice to most resource management systems.

An analysis of topics and vocabulary in Chinese oral narratives by normal speakers and speakers with fluent aphasia.

Science.gov (United States)

Law, Sam-Po; Kong, Anthony Pak-Hin; Lai, Christy

2018-01-01

This study analysed the topic and vocabulary of Chinese speakers based on language samples of personal recounts in a large spoken Chinese database recently made available in the public domain, i.e. Cantonese AphasiaBank ( http://www.speech.hku.hk/caphbank/search/ ). The goal of the analysis is to offer clinicians a rich source for selecting ecologically valid training materials for rehabilitating Chinese-speaking people with aphasia (PWA) in the design and planning of culturally and linguistically appropriate treatments. Discourse production of 65 Chinese-speaking PWA of fluent types (henceforth, PWFA) and their non-aphasic controls narrating an important event in their life were extracted from Cantonese AphasiaBank. Analyses of topics and vocabularies in terms of part-of-speech, word frequency, lexical semantics, and diversity were conducted. There was significant overlap in topics between the two groups. While the vocabulary was larger for controls than that of PWFA as expected, they were similar in distribution across parts-of-speech, frequency of occurrence, and the ratio of concrete to abstract items in major open word classes. Moreover, proportionately more different verbs than nouns were employed at the individual level for both speaker groups. The findings provide important implications for guiding directions of aphasia rehabilitation not only of fluent but also non-fluent Chinese aphasic speakers.

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...

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

Directory of Open Access Journals (Sweden)

Sonia Yassa

2013-01-01

Full Text Available We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

Science.gov (United States)

Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

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.

Evolutionary Scheduler for the Deep Space Network

Science.gov (United States)

Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

2010-01-01

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

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...

Application of cultural algorithm to generation scheduling of hydrothermal systems

International Nuclear Information System (INIS)

Yuan Xiaohui; Yuan Yanbin

2006-01-01

The daily generation scheduling of hydrothermal power systems plays an important role in the operation of electric power systems for economics and security, which is a large scale dynamic non-linear constrained optimization problem. It is difficult to solve using traditional optimization methods. This paper proposes a new cultural algorithm to solve the optimal daily generation scheduling of hydrothermal power systems. The approach takes the water transport delay time between connected reservoirs into consideration and can conveniently deal with the complicated hydraulic coupling simultaneously. An example is used to verify the correctness and effectiveness of the proposed cultural algorithm, comparing with both the Lagrange method and the genetic algorithm method. The simulation results demonstrate that the proposed algorithm has rapid convergence speed and higher solution precision. Thus, an effective method is provided to solve the optimal daily generation scheduling of hydrothermal systems

Case mix classification and a benchmark set for surgery scheduling

NARCIS (Netherlands)

Leeftink, Gréanne; Hans, Erwin W.

Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely

NUMA-Aware Thread Scheduling for Big Data Transfers over Terabits Network Infrastructure

Directory of Open Access Journals (Sweden)

Taeuk Kim

2018-01-01

Full Text Available The evergrowing trend of big data has led scientists to share and transfer the simulation and analytical data across the geodistributed research and computing facilities. However, the existing data transfer frameworks used for data sharing lack the capability to adopt the attributes of the underlying parallel file systems (PFS. LADS (Layout-Aware Data Scheduling is an end-to-end data transfer tool optimized for terabit network using a layout-aware data scheduling via PFS. However, it does not consider the NUMA (Nonuniform Memory Access architecture. In this paper, we propose a NUMA-aware thread and resource scheduling for optimized data transfer in terabit network. First, we propose distributed RMA buffers to reduce memory controller contention in CPU sockets and then schedule the threads based on CPU socket and NUMA nodes inside CPU socket to reduce memory access latency. We design and implement the proposed resource and thread scheduling in the existing LADS framework. Experimental results showed from 21.7% to 44% improvement with memory-level optimizations in the LADS framework as compared to the baseline without any optimization.

A Novel Assembly Line Scheduling Algorithm Based on CE-PSO

Directory of Open Access Journals (Sweden)

Xiaomei Hu

2015-01-01

Full Text Available With the widespread application of assembly line in enterprises, assembly line scheduling is an important problem in the production since it directly affects the productivity of the whole manufacturing system. The mathematical model of assembly line scheduling problem is put forward and key data are confirmed. A double objective optimization model based on equipment utilization and delivery time loss is built, and optimization solution strategy is described. Based on the idea of solution strategy, assembly line scheduling algorithm based on CE-PSO is proposed to overcome the shortcomings of the standard PSO. Through the simulation experiments of two examples, the validity of the assembly line scheduling algorithm based on CE-PSO is proved.

Multiuser switched diversity scheduling schemes

KAUST Repository

Shaqfeh, Mohammad; Alnuweiri, Hussein M.; Alouini, Mohamed-Slim

2012-01-01

Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

Multiuser switched diversity scheduling schemes

KAUST Repository

Shaqfeh, Mohammad

2012-09-01

Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

Directory of Open Access Journals (Sweden)

Kazuhiko Hiramoto

2018-01-01

Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

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...

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

Science.gov (United States)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Real-time disk scheduling in a mixed-media file system

NARCIS (Netherlands)

Bosch, H.G.P.; Mullender, Sape J.

2000-01-01

This paper presents our real-time disk scheduler called the Delta L scheduler, which optimizes unscheduled best-effort disk requests by giving priority to best-effort disk requests while meeting real-time request deadlines. Our scheduler tries to execute real-time disk requests as much as possible

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.

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 ...

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)

Real-Time Disk Scheduling in a Mixed-Media File System

NARCIS (Netherlands)

Bosch, H.G.P.; Mullender, Sape J.

2000-01-01

This paper presents our real-time disk scheduler called the L scheduler, which optimizes unscheduled best-effort disk requests by giving priority to best-effort disk requests while meeting real-time request deadlines. Our scheduler tries to execute real-time disk requests as much as possible in the

Artificial intelligence for the CTA Observatory scheduler

Science.gov (United States)

Colomé, Josep; Colomer, Pau; Campreciós, Jordi; Coiffard, Thierry; de Oña, Emma; Pedaletti, Giovanna; Torres, Diego F.; Garcia-Piquer, Alvaro

2014-08-01

The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint

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.

Solving a chemical batch scheduling problem by local search

NARCIS (Netherlands)

Brucker, P.; Hurink, Johann L.

1999-01-01

A chemical batch scheduling problem is modelled in two different ways as a discrete optimization problem. Both models are used to solve the batch scheduling problem in a two-phase tabu search procedure. The method is tested on real-world data.

A heuristic model for risk and cost impacts of plant outage maintenance schedule

International Nuclear Information System (INIS)

Mohammad Hadi Hadavi, S.

2009-01-01

Cost and risk are two major competing criteria in maintenance optimization problems. If a plant is forced to shutdown because of accident or fear of accident happening, beside loss of revenue, it causes damage to the credibility and reputation of the business operation. In this paper a heuristic model for incorporating three compelling optimization criteria (i.e., risk, cost, and loss) into a single evaluation function is proposed. Such a model could be used in any evaluation engine of outage maintenance schedule optimizer. It is attempted to make the model realistic and to address the ongoing challenges facing a schedule planner in a simple and commonly understandable fashion. Two simple competing schedules for the NPP feedwater system are examined against the model. The results show that while the model successfully addresses the current challenges for outage maintenance optimization, it properly demonstrates the dynamics of schedule in regards to risk, cost, and losses endured by maintenance schedule, particularly when prolonged outage and lack of maintenance for equipments in need of urgent care are of concern.

Multi-objective demand side scheduling considering the operational safety of appliances

International Nuclear Information System (INIS)

Du, Y.F.; Jiang, L.; Li, Y.Z.; Counsell, J.; Smith, J.S.

2016-01-01

Highlights: • Operational safety of appliances is introduced in multi-objective scheduling. • Relationships between operational safety and other objectives are investigated. • Adopted Pareto approach is compared with Weigh and Constraint approaches. • Decision making of Pareto approach is proposed for final appliances’ scheduling. - Abstract: The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling.

Reduced Mechanism Approach of Modeling Premixed Propane-Air Mixture using Ansys Fluent

Directory of Open Access Journals (Sweden)

Christopher Odetunde

2012-01-01

Full Text Available In computational analysis of combustion, reduced mechanisms are often used in place of detailed kinetic chemistry because the computational costs of including all the species in the reactor model are always prohibitively high; for this reason, many different reduced mechanisms have been developed for hydrocarbon oxidation. In this study we employed ANSYS Fluent Computational Fluid Dynamics (CFD package, (hereinafter referred to as Fluent to analyze propane oxidation mechanism in a conical reactor. The model employed for this work is based on the results of Westbrook and Dryer [15]. This simplified model consists of 5 chemical reactions and 12 species, namely, C3H8, CO2, CO, H2O, NO, O2, O, H, N, OH, N2 and H2. The computed mass fractions of the species, C3H8, CO2, CO and H2O were found to be in qualitative agreement with measured values presented in [26]. The results of the present study show that the bulk of the turbulent kinetic was produced in the inlet jet. The turbulent viscosity was found to be an excellent ”mixer” of the species. The computed results of y* were found to confirm that the use of the law-of-the-wall functions was valid and it also showed that the computational mesh used was adequate for resolving the flow dynamics in the near wall region.

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...

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.

Verification of an experimental stand of a 4x4 fuel element model by using the FLUENT code

International Nuclear Information System (INIS)

Haertel, P.

2001-01-01

The results of comparison of FLUENT CFD calculations with data from an experimental stand are presented. The experiment was concerned with velocity and turbulence measurements with focus on the effect of the turbulization blades. (P.A.)

Online Scheduling in Distributed Message Converter Systems

NARCIS (Netherlands)

Risse, Thomas; Wombacher, Andreas; Surridge, Mike; Taylor, Steve; Aberer, Karl

The optimal distribution of jobs among hosts in distributed environments is an important factor to achieve high performance. The optimal strategy depends on the application. In this paper we present a new online scheduling strategy for distributed EDI converter system. The strategy is based on the

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...

Scheduling for energy and reliability management on multiprocessor real-time systems

Science.gov (United States)

Qi, Xuan

Scheduling algorithms for multiprocessor real-time systems have been studied for years with many well-recognized algorithms proposed. However, it is still an evolving research area and many problems remain open due to their intrinsic complexities. With the emergence of multicore processors, it is necessary to re-investigate the scheduling problems and design/develop efficient algorithms for better system utilization, low scheduling overhead, high energy efficiency, and better system reliability. Focusing cluster schedulings with optimal global schedulers, we study the utilization bound and scheduling overhead for a class of cluster-optimal schedulers. Then, taking energy/power consumption into consideration, we developed energy-efficient scheduling algorithms for real-time systems, especially for the proliferating embedded systems with limited energy budget. As the commonly deployed energy-saving technique (e.g. dynamic voltage frequency scaling (DVFS)) will significantly affect system reliability, we study schedulers that have intelligent mechanisms to recuperate system reliability to satisfy the quality assurance requirements. Extensive simulation is conducted to evaluate the performance of the proposed algorithms on reduction of scheduling overhead, energy saving, and reliability improvement. The simulation results show that the proposed reliability-aware power management schemes could preserve the system reliability while still achieving substantial energy saving.

Online scheduling of 2-re-entrant flexible manufacturing systems

NARCIS (Netherlands)

Pinxten, J. van; Waqas, U.; Geilen, M.; Basten, T.; Somers, L.

2017-01-01

Online scheduling of operations is essential to optimize productivity of flexible manufacturing systems (FMSs) where manufacturing requests arrive on the fly. An FMS processes products according to a particular flow through processing stations. This work focusses on online scheduling of re-entrant

An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem

Directory of Open Access Journals (Sweden)

Jianhui Mou

2014-01-01

Full Text Available The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.

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

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

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.

Multiple-Machine Scheduling with Learning Effects and Cooperative Games

Directory of Open Access Journals (Sweden)

Yiyuan Zhou

2015-01-01

Full Text Available Multiple-machine scheduling problems with position-based learning effects are studied in this paper. There is an initial schedule in this scheduling problem. The optimal schedule minimizes the sum of the weighted completion times; the difference between the initial total weighted completion time and the minimal total weighted completion time is the cost savings. A multiple-machine sequencing game is introduced to allocate the cost savings. The game is balanced if the normal processing times of jobs that are on the same machine are equal and an equal number of jobs are scheduled on each machine initially.

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.

Cyclic machine scheduling with tool transportation - additional calculations

NARCIS (Netherlands)

Kuijpers, C.M.H.

2001-01-01

In the PhD Thesis of Kuijpers a cyclic machine scheduling problem with tool transportation is considered. For the problem with two machines, it is shown that there always exists an optimal schedule with a certain structure. This is done by means of an elaborate case study. For a number of cases some

Research and Applications of Shop Scheduling Based on Genetic Algorithms

Directory of Open Access Journals (Sweden)

Hang ZHAO

Full Text Available ABSTRACT Shop Scheduling is an important factor affecting the efficiency of production, efficient scheduling method and a research and application for optimization technology play an important role for manufacturing enterprises to improve production efficiency, reduce production costs and many other aspects. Existing studies have shown that improved genetic algorithm has solved the limitations that existed in the genetic algorithm, the objective function is able to meet customers' needs for shop scheduling, and the future research should focus on the combination of genetic algorithm with other optimized algorithms. In this paper, in order to overcome the shortcomings of early convergence of genetic algorithm and resolve local minimization problem in search process,aiming at mixed flow shop scheduling problem, an improved cyclic search genetic algorithm is put forward, and chromosome coding method and corresponding operation are given.The operation has the nature of inheriting the optimal individual ofthe previous generation and is able to avoid the emergence of local minimum, and cyclic and crossover operation and mutation operation can enhance the diversity of the population and then quickly get the optimal individual, and the effectiveness of the algorithm is validated. Experimental results show that the improved algorithm can well avoid the emergency of local minimum and is rapid in convergence.

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....

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.

Interactive Dynamic Mission Scheduling for ASCA

Science.gov (United States)

Antunes, A.; Nagase, F.; Isobe, T.

The Japanese X-ray astronomy satellite ASCA (Advanced Satellite for Cosmology and Astrophysics) mission requires scheduling for each 6-month observation phase, further broken down into weekly schedules at a few minutes resolution. Two tools, SPIKE and NEEDLE, written in Lisp and C, use artificial intelligence (AI) techniques combined with a graphic user interface for fast creation and alteration of mission schedules. These programs consider viewing and satellite attitude constraints as well as observer-requested criteria and present an optimized set of solutions for review by the planner. Six-month schedules at 1 day resolution are created for an oversubscribed set of targets by the SPIKE software, originally written for HST and presently being adapted for EUVE, XTE and AXAF. The NEEDLE code creates weekly schedules at 1 min resolution using in-house orbital routines and creates output for processing by the command generation software. Schedule creation on both the long- and short-term scale is rapid, less than 1 day for long-term, and one hour for short-term.

The Economy of Fluent Speaking: Phrase-Level Reduction in a Patient with Pure Apraxia of Speech

Science.gov (United States)

Staiger, Anja; Ruttenauer, Anna; Ziegler, Wolfram

2010-01-01

The term "phrase-level reduction" refers to transformations of the phonetic forms of words in connected speech. They are a characteristic property of fluent speech in normal speakers. Phrase-level reductions contribute to a reduction of articulatory-motor effort and constitute an important aspect of speech naturalness. So far, these phenomena have…

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

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

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

A reduced feedback proportional fair multiuser scheduling scheme

KAUST Repository

Shaqfeh, Mohammad

2011-12-01

Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed and ordered scheduling mechanism. A slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we propose a novel proportional fair multiuser switched-diversity scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the per-user feedback thresholds. We demonstrate by numerical examples that our reduced feedback proportional fair scheduler operates within 0.3 bits/sec/Hz from the achievable rates by the conventional full feedback proportional fair scheduler in Rayleigh fading conditions. © 2011 IEEE.

Technology for planning and scheduling under complex constraints

Science.gov (United States)

Alguire, Karen M.; Pedro Gomes, Carla O.

1997-02-01

Within the context of law enforcement, several problems fall into the category of planning and scheduling under constraints. Examples include resource and personnel scheduling, and court scheduling. In the case of court scheduling, a schedule must be generated considering available resources, e.g., court rooms and personnel. Additionally, there are constraints on individual court cases, e.g., temporal and spatial, and between different cases, e.g., precedence. Finally, there are overall objectives that the schedule should satisfy such as timely processing of cases and optimal use of court facilities. Manually generating a schedule that satisfies all of the constraints is a very time consuming task. As the number of court cases and constraints increases, this becomes increasingly harder to handle without the assistance of automatic scheduling techniques. This paper describes artificial intelligence (AI) technology that has been used to develop several high performance scheduling applications including a military transportation scheduler, a military in-theater airlift scheduler, and a nuclear power plant outage scheduler. We discuss possible law enforcement applications where we feel the same technology could provide long-term benefits to law enforcement agencies and their operations personnel.

Planning and scheduling for petroleum refineries using mathematical programming

Directory of Open Access Journals (Sweden)

Joly M.

2002-01-01

Full Text Available The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998 is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG area for maximization of the production of petrochemical-grade propane and product delivery.

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

Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs

Directory of Open Access Journals (Sweden)

Wenming Cheng

2012-01-01

Full Text Available In many real scheduling environments, a job processed later needs longer time than the same job when it starts earlier. This phenomenon is known as scheduling with deteriorating jobs to many industrial applications. In this paper, we study a scheduling problem of minimizing the total completion time on identical parallel machines where the processing time of a job is a step function of its starting time and a deteriorating date that is individual to all jobs. Firstly, a mixed integer programming model is presented for the problem. And then, a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule. To evaluate the performance of the proposed algorithms, computational experiments are performed on randomly generated test instances. Finally, computational results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time even for large-sized problems.

Application of Tabu Search Algorithm in Job Shop Scheduling

Directory of Open Access Journals (Sweden)

Betrianis Betrianis

2010-10-01

Full Text Available Tabu Search is one of local search methods which is used to solve the combinatorial optimization problem. This method aimed is to make the searching process of the best solution in a complex combinatorial optimization problem(np hard, ex : job shop scheduling problem, became more effective, in a less computational time but with no guarantee to optimum solution.In this paper, tabu search is used to solve the job shop scheduling problem consists of 3 (three cases, which is ordering package of September, October and November with objective of minimizing makespan (Cmax. For each ordering package, there is a combination for initial solution and tabu list length. These result then  compared with 4 (four other methods using basic dispatching rules such as Shortest Processing Time (SPT, Earliest Due Date (EDD, Most Work Remaining (MWKR dan First Come First Served (FCFS. Scheduling used Tabu Search Algorithm is sensitive for variables changes and gives makespan shorter than scheduling used by other four methods.

Applying dynamic priority scheduling scheme to static systems of pinwheel task model in power-aware scheduling.

Science.gov (United States)

Seol, Ye-In; Kim, Young-Kuk

2014-01-01

Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task's priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity of O(n) reduces the energy consumption by 10-80% over the existing algorithms.

Slowly progressive fluent aphasia; Clinical features and an imaging study including MRI, SPECT and PET

Energy Technology Data Exchange (ETDEWEB)

Sakurai, Yasuhisa; Momose, Toshimitsu; Watanabe, Toshiaki; Ishikawa, Takashi; Iwata, Makoto (Tokyo Univ. (Japan). Faculty of Medicine); Bando, Mitsuaki

1991-05-01

Three patients with slowly progressive fluent aphasia are reported. One of the patients presented with memory disturbance. They were characterized clinically by having selective deficits in vocabulary, which resulted in impairment of confrontation naming, and auditory comprehension. MRI showed an atrophy not only in the left temporal lobe (including the superior, middle and inferior temporal gyri), hippocampus, parahippocampual gyrus, and fusiform gyrus, but also in the left parietal lobe. I-123 IMP SPECT and F-18 FDG PET were used to determine regional cerebral blood flow and regional cerebral metabolic rate, respectively. In addition to the decreased tracer uptake in the left temporal and/or parietal lobe, a decreased uptake was seen in the bilateral basal ganglia, the inner side of the temporal lobe (including the bilateral hippocampus), the right anterior temporal lobe, and the left thalamus. These findings may deny the previous thought that lesions are localized in slowly progressive fluent aphasia. Furthermore, noticeable difficulty in naming, i.e., patients unable to recognize the right answer, are considered attributable to widespread lesions from the whole left temporal lobe, including the hippocampus, to the right temporal lobe. (N.K.).

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.

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.

Scheduling algorithms for rapid imaging using agile Cubesat constellations

Science.gov (United States)

Nag, Sreeja; Li, Alan S.; Merrick, James H.

2018-02-01

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ∼40 kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20 kg satellites in Low Earth Orbit for two case studies - rapid imaging of Landsat's land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that

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

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.

Flow-shop scheduling problem under uncertainties: Review and trends

OpenAIRE

Eliana María González-Neira; Jairo R. Montoya-Torres; David Barrera

2017-01-01

Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of co...

Staff Scheduling for Inbound Call and Customer Contact Centers

OpenAIRE

Fukunaga, Alex; Hamilton, Ed; Fama, Jason; Andre, David; Matan, Ofer; Nourbakhsh, Illah

2002-01-01

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some ...

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.

Development of oxidation heat of the coal left in the mined-out area of a longwall face: Modelling using the fluent software

Directory of Open Access Journals (Sweden)

Taraba B.

2008-01-01

Full Text Available A commercial CFD software program, Fluent, was used to study oxidation processes in the longwall mined-out (gob area. A three-dimensional model of the gob area with an advancing coal face has been developed. For the model, typical oxidation behavior of a bituminous coal from the Ostrava-Karviná District was incorporated as resulted from laboratory investigations. The longwall gob area was designed on the basis of the actual longwall face district. Detailed measurements in the district then enabled re-verification of the model outputs with actual data in situ. The main attention was paid to modelling the effect of grain size of the coal left in the mined-out area on the oxidation heat and gases evolution. Numerical simulations confirmed the existence of an 'optimal' zone for intense development of the spontaneous heating process in the gob area.

Runway Scheduling Using Generalized Dynamic Programming

Science.gov (United States)

Montoya, Justin; Wood, Zachary; Rathinam, Sivakumar

2011-01-01

A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway fight sequences that are optimal for both runway throughput and delay. Realistic time-based operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour ad can be 100x faster.

Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Directory of Open Access Journals (Sweden)

Zengqiang Jiang

2014-06-01

Full Text Available Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II based on blood variation for above scheduling model.Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model.Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption.Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm.

Science.gov (United States)

Abdulhamid, Shafi'i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

Science.gov (United States)

Abdulhamid, Shafi’i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. PMID:27384239

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

Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

International Nuclear Information System (INIS)

Liu, Xingrang; Bansal, R.C.

2014-01-01

Highlights: • A coal fired power plant boiler combustion process model based on real data. • We propose multi-objective optimization with CFD to optimize boiler combustion. • The proposed method uses software CORBA C++ and ANSYS Fluent 14.5 with AI. • It optimizes heat flux transfers and maintains temperature to avoid ash melt. - Abstract: The dominant role of electricity generation and environment consideration have placed strong requirements on coal fired power plants, requiring them to improve boiler combustion efficiency and decrease carbon emission. Although neural network based optimization strategies are often applied to improve the coal fired power plant boiler efficiency, they are limited by some combustion related problems such as slagging. Slagging can seriously influence heat transfer rate and decrease the boiler efficiency. In addition, it is difficult to measure slag build-up. The lack of measurement for slagging can restrict conventional neural network based coal fired boiler optimization, because no data can be used to train the neural network. This paper proposes a novel method of integrating non-dominated sorting genetic algorithm (NSGA II) based multi-objective optimization with computational fluid dynamics (CFD) to decrease or even avoid slagging inside a coal fired boiler furnace and improve boiler combustion efficiency. Compared with conventional neural network based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties such as temperature field inside a boiler by adjusting the temperature and velocity of primary and secondary air in coal fired power plant boiler control systems. The temperature in the vicinity of water wall tubes of a boiler can be maintained within the ash melting temperature limit. The incoming ash particles cannot melt and bond to surface of heat transfer equipment of a boiler. So the trend of slagging inside furnace is controlled. Furthermore, the

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.

Natural Convection Analysis with Various Turbulent Models Using FLUENT

International Nuclear Information System (INIS)

Park, Yu Sun

2007-01-01

The buoyancy driven convective flow fields are steady circulatory flows which were made between surfaces maintained at two fixed temperatures. They are ubiquitous in nature and play an important role in many engineering applications. Especially, in last decades, natural convection in a close loop or cavity becomes the main issue in the molecular biology for the polymerase chain reaction (PCR). Application of a natural convection can reduce the costs and efforts remarkably. This paper focuses on the sensitivity study of turbulence analysis using CFD for a natural convection in a closed rectangular cavity. Using commercial CFD code, FLUENT, various turbulent models were applied to the turbulent flow. Results from each CFD model will be compared each other in the viewpoints of flow characteristics. This work will suggest the best turbulent model of CFD for analyzing turbulent flows of the natural convection in an enclosure system

Genetic algorithm to solve the problems of lectures and practicums scheduling

Science.gov (United States)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

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

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

Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem

Science.gov (United States)

Tang, Dunbing; Dai, Min

2015-09-01

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Science.gov (United States)

Aoki, Kenichi; Wakano, Joe Yuichiro; Lehmann, Laurent

2012-06-01

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium. Copyright © 2012 Elsevier Inc. All rights reserved.

Modulation of N400 in Chronic Non-Fluent Aphasia Using Low Frequency Repetitive Transcranial Magnetic Stimulation (rTMS)

Science.gov (United States)

Barwood, Caroline H. S.; Murdoch, Bruce E.; Whelan, Brooke-Mai; Lloyd, David; Riek, Stephan; O'Sullivan, John D.; Coulthard, Alan; Wong, Andrew

2011-01-01

Low frequency Repetitive Transcranial Magnetic Stimulation (rTMS) has previously been applied to language homologues in non-fluent populations of persons with aphasia yielding significant improvements in behavioral language function up to 43 months post stimulation. The present study aimed to investigate the electrophysiological correlates…

Schedulability-Driven Communication Synthesis for Time Triggered Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

We present an approach to static priority preemptive process scheduling for the synthesis of hard real-time distributed embedded systems where communication plays an important role. The communication model is based on a time-triggered protocol. We have developed an analysis for the communication...... delays proposing four different message scheduling policies over a time-triggered communication channel. Optimization strategies for the synthesis of communication are developed, and the four approaches to message scheduling are compared using extensive experiments...

Schedulability-Driven Communication Synthesis for Time Triggered Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

1999-01-01

We present an approach to static priority preemptive process scheduling for the synthesis of hard real-time distributed embedded systems where communication plays an important role. The communication model is based on a time-triggered protocol. We have developed an analysis for the communication...... delays proposing four different message scheduling policies over a time-triggered communication channel. Optimization strategies for the synthesis of communication are developed, and the four approaches to message scheduling are compared using extensive experiments....

Schedulability-Driven Communication Synthesis for Time Triggered Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2004-01-01

We present an approach to static priority preemptive process scheduling for the synthesis of hard real-time distributed embedded systems where communication plays an important role. The communication model is based on a time-triggered protocol. We have developed an analysis for the communication...... delays with four different message scheduling policies over a time-triggered communication channel. Optimization strategies for the synthesis of communication are developed, and the four approaches to message scheduling are compared using extensive experiments....

Using a vision cognitive algorithm to schedule virtual machines

OpenAIRE

Zhao Jiaqi; Mhedheb Yousri; Tao Jie; Jrad Foued; Liu Qinghuai; Streit Achim

2014-01-01

Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the...

Scheduling Additional Train Unit Services on Rail Transit Lines

OpenAIRE

Zhibin Jiang; Yuyan Tan; Özgür Yalçınkaya

2014-01-01

This paper deals with the problem of scheduling additional train unit (TU) services in a double parallel rail transit line, and a mixed integer programming (MIP) model is formulated for integration strategies of new trains connected by TUs with the objective of obtaining higher frequencies in some special sections and special time periods due to mass passenger volumes. We took timetable scheduling and TUs scheduling as an integrated optimization model with two objectives: minimizing travel ti...

Designing of vague logic based multilevel feedback queue scheduler

Directory of Open Access Journals (Sweden)

Supriya Raheja

2016-03-01

Full Text Available Multilevel feedback queue scheduler suffers from major issues of scheduling such as starvation for long tasks, fixed number of queues, and static length of time quantum in each queue. These factors directly affect the performance of the scheduler. At many times impreciseness exists in attributes of tasks which make the performance even worse. In this paper, our intent is to improve the performance by providing a solution to these issues. We design a multilevel feedback queue scheduler using a vague set which we call as VMLFQ scheduler. VMLFQ scheduler intelligently handles the impreciseness and defines the optimum number of queues as well as the optimal size of time quantum for each queue. It also resolves the problem of starvation. This paper simulates and analyzes the performance of VMLFQ scheduler with the other multilevel feedback queue techniques using MatLab.

Design issues of an efficient distributed database scheduler for telecom

NARCIS (Netherlands)

Bodlaender, M.P.; Stok, van der P.D.V.

1998-01-01

We optimize the speed of real-time databases by optimizing the scheduler. The performance of a database is directly linked to the environment it operates in, and we use environment characteristics as guidelines for the optimization. A typical telecom environment is investigated, and characteristics

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.

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...

Comparison of Different Approaches to the Cutting Plan Scheduling

Directory of Open Access Journals (Sweden)

Peter Bober

2011-10-01

Full Text Available Allocation of specific cutting plans and their scheduling to individual cutting machines presents a combinatorial optimization problem. In this respect, various approaches and methods are used to arrive to a viable solution. The paper reports three approaches represented by three discreet optimization methods. The first one is back-tracing algorithm and serves as a reference to verify functionality of the other two ones. The second method is optimization using genetic algorithms, and the third one presents heuristic approach to optimization based on anticipated properties of an optimal solution. Research results indicate that genetic algorithms are demanding to calculate though not dependant on the selected objective function. Heuristic algorithm is fast but dependant upon anticipated properties of the optimal solution. Hence, at change of the objective function it has to be changed. When the scheduling by genetic algorithms is solvable in a sufficiently short period of time, it is more appropriate from the practical point than the heuristic algorithm. The back-tracing algorithm usually does not provide a result in a feasible period of time.

Neural Feedback Scheduling of Real-Time Control Tasks

OpenAIRE

Xia, Feng; Tian, Yu-Chu; Sun, Youxian; Dong, Jinxiang

2008-01-01

Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimi...

Reusable rocket engine preventive maintenance scheduling using genetic algorithm

International Nuclear Information System (INIS)

Chen, Tao; Li, Jiawen; Jin, Ping; Cai, Guobiao

2013-01-01

This paper deals with the preventive maintenance (PM) scheduling problem of reusable rocket engine (RRE), which is different from the ordinary repairable systems, by genetic algorithm. Three types of PM activities for RRE are considered and modeled by introducing the concept of effective age. The impacts of PM on all subsystems' aging processes are evaluated based on improvement factor model. Then the reliability of engine is formulated by considering the accumulated time effect. After that, optimization model subjected to reliability constraint is developed for RRE PM scheduling at fixed interval. The optimal PM combination is obtained by minimizing the total cost in the whole life cycle for a supposed engine. Numerical investigations indicate that the subsystem's intrinsic reliability characteristic and the improvement factor of maintain operations are the most important parameters in RRE's PM scheduling management

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.)

Using the principles of circadian physiology enhances shift schedule design

International Nuclear Information System (INIS)

Connolly, J.J.; Moore-Ede, M.C.

1987-01-01

Nuclear power plants must operate 24 h, 7 days a week. For the most part, shift schedules currently in use at nuclear power plants have been designed to meet operational needs without considering the biological clocks of the human operators. The development of schedules that also take circadian principles into account is a positive step that can be taken to improve plant safety by optimizing operator alertness. These schedules reduce the probability of human errors especially during backshifts. In addition, training programs that teach round-the-clock workers how to deal with the problems of shiftwork can help to optimize performance and alertness. These programs teach shiftworkers the underlying causes of the sleep problems associated with shiftwork and also provide coping strategies for improving sleep and dealing with the transition between shifts. When these training programs are coupled with an improved schedule, the problems associated with working round-the-clock can be significantly reduced

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Directory of Open Access Journals (Sweden)

Sean Yaw

2017-10-01

Full Text Available This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.

Review of job shop scheduling research and its new perspectives under Industry 4.0

OpenAIRE

Zhang, Jian; Ding, Guofu; Zou, Yisheng; Qin, Sheng-feng; Fu, Jianlin

2017-01-01

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to t...

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

Science.gov (United States)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

It felt fluent, and I liked it: subjective feeling of fluency rather than objective fluency determines liking.

Science.gov (United States)

Forster, Michael; Leder, Helmut; Ansorge, Ulrich

2013-04-01

According to the processing-fluency explanation of aesthetics, more fluently processed stimuli are preferred (R. Reber, N. Schwarz, & P. Winkielman, 2004, Processing fluency and aesthetic pleasure: Is beauty in the perceiver's processing experience? Personality and Social Psychology Review, Vol. 8, pp. 364-382.). In this view, the subjective feeling of ease of processing is considered important, but this has not been directly tested in perceptual processing. In two experiments, we therefore objectively manipulated fluency (ease of processing) with subliminal perceptual priming (Study 1) and variations in presentation durations (Study 2). We assessed the impact of objective fluency on feelings of fluency and liking, as well as their interdependence. In line with the processing-fluency account, we found that objectively more fluent images were indeed judged as more fluent and were also liked more. Moreover, differences in liking were even stronger when data were analyzed according to felt fluency. These findings demonstrate that perceptual fluency is not only explicitly felt, it can also be reported and is an important determinant of liking. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Prior knowledge of deaf students fluent in brazilian sign languages regarding the algebraic language in high school

Directory of Open Access Journals (Sweden)

Silvia Teresinha Frizzarini

2014-06-01

Full Text Available There are few researches with deeper reflections on the study of algebra with deaf students. In order to validate and disseminate educational activities in that context, this article aims at highlighting the deaf students’ prior knowledge, fluent in Brazilian Sign Language, referring to the algebraic language used in high school. The theoretical framework used was Duval’s theory, with analysis of the changes, by treatment and conversion, of different registers of semiotic representation, in particular inequalities. The methodology used was the application of a diagnostic evaluation performed with deaf students, all fluent in Brazilian Sign Language, in a special school located in the north of Paraná State. We emphasize the need to work in both directions of conversion, in different languages, especially when the starting record is the graphic. Therefore, the conclusion reached was that one should not separate the algebraic representation from other records, due to the need of sign language perform not only the communication function, but also the functions of objectification and treatment, fundamental in cognitive development.

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...

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)

Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front

Directory of Open Access Journals (Sweden)

Ziaul Huque

2012-01-01

Full Text Available A Computational Fluid Dynamics (CFD and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient (CL and Drag Coefficient (CD data at a range of 0° to 12° angles of attack (α and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422 for all the six airfoils were obtained. Realizable k-ε turbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front.

Scheduling projects with multiskill learning effect.

Science.gov (United States)

Zha, Hong; Zhang, Lianying

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost.

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.

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Directory of Open Access Journals (Sweden)

Maria Drakaki

2017-02-01

Full Text Available Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs and reinforcement learning (RL. CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.

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...

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.

Generation-Side Power Scheduling in a Grid-Connected DC Microgrid

DEFF Research Database (Denmark)

Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Meng, Lexuan

2015-01-01

In this paper, a constrained mixed-integer programming model for scheduling the active power supplied by the generation units in storage-based DC microgrids is presented. The optimization problem minimizes operating costs taking into account a two-stage mode operation of the energy storage system...... so that a more accurate model for optimization of the microgrid operation can be obtained. The model is used in a particular grid-connected DC microgrid that includes two renewable energy sources and an energy storage system which supply a critical load. The results of the scheduling process...

A hybrid approach for minimizing makespan in permutation flowshop scheduling

DEFF Research Database (Denmark)

Govindan, Kannan; Balasundaram, R.; Baskar, N.

2017-01-01

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan (total completion time). To solve scheduling problems, a combination of Decision Tree (DT) and Scatter Search (SS) algorithms are used. Initially, the DT is used to generate a seed...... solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification...

Cultivating Culturally Fluent Leaders for the Future of Kinesiology. 2016 NAKHE Hally Beth Poindexter Young Scholar Award

Science.gov (United States)

James-Hassan, Martha J.

2016-01-01

This article posits that each content area that falls under the umbrella of kinesiology must develop a future cadre of professionals who are competent in their content knowledge and are trained as leaders who are able to understand the essential impacts of culture on their work. The intentional development of culturally fluent leaders should be a…

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.

Scheduling the scheduling task : a time management perspective on scheduling

NARCIS (Netherlands)

Larco Martinelli, J.A.; Wiers, V.C.S.; Fransoo, J.C.

2013-01-01

Time is the most critical resource at the disposal of schedulers. Hence, an adequate management of time from the schedulers may impact positively on the scheduler’s productivity and responsiveness to uncertain scheduling environments. This paper presents a field study of how schedulers make use of

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

Prescribed Travel Schedules for Fatigue Management

Science.gov (United States)

Whitmire, Alexandra; Johnston, Smith; Lockley, Steven

2011-01-01

The NASA Fatigue Management Team is developing recommendations for managing fatigue during travel and for shift work operations, as Clinical Practice Guidelines for the Management of Circadian Desynchrony in ISS Operations. The Guidelines provide the International Space Station (ISS ) flight surgeons and other operational clinicians with evidence-based recommendations for mitigating fatigue and other factors related to sleep loss and circadian desynchronization. As much international travel is involved both before and after flight, the guidelines provide recommendations for: pre-flight training, in-flight operations, and post-flight rehabilitation. The objective of is to standardize the process by which care is provided to crewmembers, ground controllers, and other support personnel such as trainers, when overseas travel or schedule shifting is required. Proper scheduling of countermeasures - light, darkness, melatonin, diet, exercise, and medications - is the cornerstone for facilitating circadian adaptation, improving sleep, enhancing alertness, and optimizing performance. The Guidelines provide, among other things, prescribed travel schedules that outline the specific implementation of these mitigation strategies. Each travel schedule offers evidence based protocols for properly using the NASA identified countermeasures for fatigue. This presentation will describe the travel implementation schedules and how these can be used to alleviate the effects of jet lag and/or schedule shifts.

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.

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.

Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming

Energy Technology Data Exchange (ETDEWEB)

Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo

2013-05-23

This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.

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.

Load scheduling for decentralized CHP plants

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg, orlov 31.07.2008; Madsen, Henrik; Nielsen, Torben Skov

) an interactive decision support tool by which optimal schedules can be found given the forecasts or user-defined modifications of the forecasts, and (iii) an automatic on-line system for monitoring when conditions have changed so that rescheduling is appropriate. In this report the focus is on methods applicable...... be obtained. Furthermore, we believe that all relevant forecasting methods are far too complicated to allow for this integration; both uncertainties originating from the dependence of heat load on climate and from meteorological forecasts need to be taken into account. Instead we suggest that the decision....... By letting the system find optimal schedules for each of these realizations the operator can gain some insight into the importance of the uncertainties. It is shown that with modern personal computers (e.g. 1 GHz Pentium III), operating systems (e.g. RedHat Linux 6.0), and compilers (e.g. GNU C 2...

Speech entrainment enables patients with Broca’s aphasia to produce fluent speech

Science.gov (United States)

Hubbard, H. Isabel; Hudspeth, Sarah Grace; Holland, Audrey L.; Bonilha, Leonardo; Fromm, Davida; Rorden, Chris

2012-01-01

A distinguishing feature of Broca’s aphasia is non-fluent halting speech typically involving one to three words per utterance. Yet, despite such profound impairments, some patients can mimic audio-visual speech stimuli enabling them to produce fluent speech in real time. We call this effect ‘speech entrainment’ and reveal its neural mechanism as well as explore its usefulness as a treatment for speech production in Broca’s aphasia. In Experiment 1, 13 patients with Broca’s aphasia were tested in three conditions: (i) speech entrainment with audio-visual feedback where they attempted to mimic a speaker whose mouth was seen on an iPod screen; (ii) speech entrainment with audio-only feedback where patients mimicked heard speech; and (iii) spontaneous speech where patients spoke freely about assigned topics. The patients produced a greater variety of words using audio-visual feedback compared with audio-only feedback and spontaneous speech. No difference was found between audio-only feedback and spontaneous speech. In Experiment 2, 10 of the 13 patients included in Experiment 1 and 20 control subjects underwent functional magnetic resonance imaging to determine the neural mechanism that supports speech entrainment. Group results with patients and controls revealed greater bilateral cortical activation for speech produced during speech entrainment compared with spontaneous speech at the junction of the anterior insula and Brodmann area 47, in Brodmann area 37, and unilaterally in the left middle temporal gyrus and the dorsal portion of Broca’s area. Probabilistic white matter tracts constructed for these regions in the normal subjects revealed a structural network connected via the corpus callosum and ventral fibres through the extreme capsule. Unilateral areas were connected via the arcuate fasciculus. In Experiment 3, all patients included in Experiment 1 participated in a 6-week treatment phase using speech entrainment to improve speech production

Using a vision cognitive algorithm to schedule virtual machines

Directory of Open Access Journals (Sweden)

Zhao Jiaqi

2014-09-01

Full Text Available Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

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.)

Preventive maintenance scheduling by variable dimension evolutionary algorithms

International Nuclear Information System (INIS)

Limbourg, Philipp; Kochs, Hans-Dieter

2006-01-01

Black box optimization strategies have been proven to be useful tools for solving complex maintenance optimization problems. There has been a considerable amount of research on the right choice of optimization strategies for finding optimal preventive maintenance schedules. Much less attention is turned to the representation of the schedule to the algorithm. Either the search space is represented as a binary string leading to highly complex combinatorial problem or maintenance operations are defined by regular intervals which may restrict the search space to suboptimal solutions. An adequate representation however is vitally important for result quality. This work presents several nonstandard input representations and compares them to the standard binary representation. An evolutionary algorithm with extensions to handle variable length genomes is used for the comparison. The results demonstrate that two new representations perform better than the binary representation scheme. A second analysis shows that the performance may be even more increased using modified genetic operators. Thus, the choice of alternative representations leads to better results in the same amount of time and without any loss of accuracy

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...

A New Evolutionary Algorithm Based on Bacterial Evolution and Its Application for Scheduling A Flexible Manufacturing System

Directory of Open Access Journals (Sweden)

Chandramouli Anandaraman

2012-01-01

Full Text Available A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs. AGVs are used for carrying jobs between the Load/Unload (L/U station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.

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.

Verbal and non-verbal semantic impairment: From fluent primary progressive aphasia to semantic dementia

Directory of Open Access Journals (Sweden)

Mirna Lie Hosogi Senaha

Full Text Available Abstract Selective disturbances of semantic memory have attracted the interest of many investigators and the question of the existence of single or multiple semantic systems remains a very controversial theme in the literature. Objectives: To discuss the question of multiple semantic systems based on a longitudinal study of a patient who presented semantic dementia from fluent primary progressive aphasia. Methods: A 66 year-old woman with selective impairment of semantic memory was examined on two occasions, undergoing neuropsychological and language evaluations, the results of which were compared to those of three paired control individuals. Results: In the first evaluation, physical examination was normal and the score on the Mini-Mental State Examination was 26. Language evaluation revealed fluent speech, anomia, disturbance in word comprehension, preservation of the syntactic and phonological aspects of the language, besides surface dyslexia and dysgraphia. Autobiographical and episodic memories were relatively preserved. In semantic memory tests, the following dissociation was found: disturbance of verbal semantic memory with preservation of non-verbal semantic memory. Magnetic resonance of the brain revealed marked atrophy of the left anterior temporal lobe. After 14 months, the difficulties in verbal semantic memory had become more severe and the semantic disturbance, limited initially to the linguistic sphere, had worsened to involve non-verbal domains. Conclusions: Given the dissociation found in the first examination, we believe there is sufficient clinical evidence to refute the existence of a unitary semantic system.

Optimal design of distributed control and embedded systems

CERN Document Server

Çela, Arben; Li, Xu-Guang; Niculescu, Silviu-Iulian

2014-01-01

Optimal Design of Distributed Control and Embedded Systems focuses on the design of special control and scheduling algorithms based on system structural properties as well as on analysis of the influence of induced time-delay on systems performances. It treats the optimal design of distributed and embedded control systems (DCESs) with respect to communication and calculation-resource constraints, quantization aspects, and potential time-delays induced by the associated  communication and calculation model. Particular emphasis is put on optimal control signal scheduling based on the system state. In order to render  this complex optimization problem feasible in real time, a time decomposition is based on periodicity induced by the static scheduling is operated. The authors present a co-design approach which subsumes the synthesis of the optimal control laws and the generation of an optimal schedule of control signals on real-time networks as well as the execution of control tasks on a single processor. The a...

Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems

Directory of Open Access Journals (Sweden)

Hesam Izakian

2009-07-01

Full Text Available Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem.

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.

Scheduling Aircraft Landings under Constrained Position Shifting

Science.gov (United States)

Balakrishnan, Hamsa; Chandran, Bala

2006-01-01

Optimal scheduling of airport runway operations can play an important role in improving the safety and efficiency of the National Airspace System (NAS). Methods that compute the optimal landing sequence and landing times of aircraft must accommodate practical issues that affect the implementation of the schedule. One such practical consideration, known as Constrained Position Shifting (CPS), is the restriction that each aircraft must land within a pre-specified number of positions of its place in the First-Come-First-Served (FCFS) sequence. We consider the problem of scheduling landings of aircraft in a CPS environment in order to maximize runway throughput (minimize the completion time of the landing sequence), subject to operational constraints such as FAA-specified minimum inter-arrival spacing restrictions, precedence relationships among aircraft that arise either from airline preferences or air traffic control procedures that prevent overtaking, and time windows (representing possible control actions) during which each aircraft landing can occur. We present a Dynamic Programming-based approach that scales linearly in the number of aircraft, and describe our computational experience with a prototype implementation on realistic data for Denver International Airport.

Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation

International Nuclear Information System (INIS)

Tian, Hao; Yuan, Xiaohui; Ji, Bin; Chen, Zhihuan

2014-01-01

Highlights: • An improved non-dominated sorting gravitational search algorithm (NSGSA-CM) is proposed. • NSGSA-CM is used to solve the problem of short-term multi-objective hydrothermal scheduling. • We enhance the search capability of NSGSA-CM by chaotic mutation. • New strategies are devised to handle various constraints in NSGSA-CM. • We obtain better compromise solutions with less fuel cost and emissions. - Abstract: This paper proposes a non-dominated sorting gravitational search algorithm with chaotic mutation (NSGSA-CM) to solve short-term economic/environmental hydrothermal scheduling (SEEHTS) problem. The SEEHTS problem is formulated as a multi-objective optimization problem with many equality and inequality constraints. By introducing the concept of non-dominated sorting and crowding distance, NSGSA-CM can optimize two objectives of fuel cost and pollutant emission simultaneously and obtain a set of Pareto optimal solutions in one trial. In order to improve the performance of NSGSA-CM, the paper introduces particle memory character and population social information in velocity update process. And a chaotic mutation is adopted to prevent the premature convergence. Furthermore, NSGSA-CM utilizes an elitism strategy which selects better solutions in parent and offspring populations based on their non-domination rank and crowding distance to update new generations. When dealing with the constraints of the SEEHTS, new strategies without penalty factors are proposed. In order to handle the water dynamic balance and system load balance constraints, this paper uses a combined strategy which adjusts the violation averagely to each decision variable at first and adjusts the rest violation randomly later. Meanwhile, a new symmetrical adjustment strategy by modifying the discharges at current and later interval without breaking water dynamic balance is adopted to handle reservoir storage constraints. To test the performance of the proposed NSGSA

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

OpenAIRE

Chen, Ruey-Maw; Wang, Chuin-Mu

2011-01-01

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimo...

An investigation of FLUENT's fan model including the effect of swirl velocity

International Nuclear Information System (INIS)

El Saheli, A.; Barron, R.M.

2002-01-01

The purpose of this paper is to investigate and discuss the reliability of simplified models for the computational fluid dynamics (CFD) simulation of air flow through automotive engine cooling fans. One of the most widely used simplified fan models in industry is a variant of the actuator disk model which is available in most commercial CFD software, such as FLUENT. In this model, the fan is replaced by an infinitely thin surface on which pressure rise across the fan is specified as a polynomial function of normal velocity or flow rate. The advantages of this model are that it is simple, it accurately predicts the pressure rise through the fan and the axial velocity, and it is robust

Mixed Criticality Scheduling for Industrial Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Xi Jin

2016-08-01

Full Text Available Wireless sensor networks (WSNs have been widely used in industrial systems. Their real-time performance and reliability are fundamental to industrial production. Many works have studied the two aspects, but only focus on single criticality WSNs. Mixed criticality requirements exist in many advanced applications in which different data flows have different levels of importance (or criticality. In this paper, first, we propose a scheduling algorithm, which guarantees the real-time performance and reliability requirements of data flows with different levels of criticality. The algorithm supports centralized optimization and adaptive adjustment. It is able to improve both the scheduling performance and flexibility. Then, we provide the schedulability test through rigorous theoretical analysis. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones.

Analysis and Optimization of Distributed Real-Time Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

and scheduling policies. In this context, the task of designing such systems is becoming increasingly difficult. The success of new adequate design methods depends on the availability of efficient analysis as well as optimization techniques. In this paper, we present both analysis and optimization approaches...... characteristic to this class of systems: mapping of functionality, the optimization of the access to the communication channel, and the assignment of scheduling policies to processes. Optimization heuristics aiming at producing a schedulable system, with a given amount of resources, are presented....