Optimized Information Transmission Scheduling Strategy Oriented to Advanced Metering Infrastructure
Directory of Open Access Journals (Sweden)
Weiming Tong
2013-01-01
Full Text Available Advanced metering infrastructure (AMI is considered to be the first step in constructing smart grid. AMI allows customers to make real-time choices about power utilization and enables power utilities to increase the effectiveness of the regional power grids by managing demand load during peak times and reducing unneeded power generation. These initiatives rely heavily on the prompt information transmission inside AMI. Aiming at the information transmission problem, this paper researches the communication scheduling strategy in AMI at a macroscopic view. First, the information flow of AMI is analyzed, and the power users are classified into several grades by their importance. Then, the defect of conventional information transmission scheduling strategy is analyzed. On this basis, two optimized scheduling strategies are proposed. In the wide area, an optimized scheduling strategy based on user importance and time critical is proposed to guarantee the important power users’ information transmission being handled promptly. In the local area, an optimized scheduling strategy based on device and information importance and time critical is proposed to guarantee the important devices and information in AMI user end system being handled promptly. At last, the two optimized scheduling strategies are simulated. The simulation results show that they can effectively improve the real-time performance and reliability of AMI information transmission.
Energy Optimal Transmission Scheduling in Wireless Sensor Networks
Srivastava, Rahul
2010-01-01
One of the main issues in the design of sensor networks is energy efficient communication of time-critical data. Energy wastage can be caused by failed packet transmission attempts at each node due to channel dynamics and interference. Therefore transmission control techniques that are unaware of the channel dynamics can lead to suboptimal channel use patterns. In this paper we propose a transmission controller that utilizes different "grades" of channel side information to schedule packet transmissions in an optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. The wireless channel is modeled as a finite-state Markov channel. We are specifically interested in the case where the transmitter has low-grade channel side information that can be obtained based solely on the ACK/NAK sequence for the previous transmissions. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We als...
AUTOMATING SELECTION OF OPTIMAL PACKET SCHEDULING DURING VOIP-TRAFFIC TRANSMISSION
Directory of Open Access Journals (Sweden)
Yuliya A. Balakshina
2016-11-01
Full Text Available The usage of various packet scheduling disciplines in computer networking devices as a mechanism to ensure the quality of service is described. Stages for selection of necessary parameters values of packet scheduling during VoIP-traffic transmission in computer networks are defined. VoIP-traffic was set as a research object because there are strict requirements of VoIP-applications to the network transmission parameters. With the aid of training and experimental simulation system the numerous experiments for parameters selection of the most common packet scheduling disciplines were carried out (FIFO, WFQ, non-preemptive priority queueing. The example that illustrates the ability to adjust the weighting coefficients of WFQ packet scheduling discipline is presented. Approximate analytical dependences are obtained and they will significantly reduce system administrators’ efforts to assess and modify the parameters of packet scheduling in network devices. A method of automating selection of the optimal packet scheduling discipline is formulated.
Optimized Information Transmission Scheduling Strategy Oriented to Advanced Metering Infrastructure
Weiming Tong; Xianji Jin; Lei Lu
2013-01-01
Advanced metering infrastructure (AMI) is considered to be the first step in constructing smart grid. AMI allows customers to make real-time choices about power utilization and enables power utilities to increase the effectiveness of the regional power grids by managing demand load during peak times and reducing unneeded power generation. These initiatives rely heavily on the prompt information transmission inside AMI. Aiming at the information transmission problem, this paper researches the ...
Institute of Scientific and Technical Information of China (English)
武景燕; 魏巍; 曲婧瑶; 闫清东
2011-01-01
A kind of automatic shift schedule optimization method is provided for a tracked vehicle with hydrodynamic-mechanical transmission in order to improve its dynamic performance. A dynamic model of integrated hydrodynamic-mechanical transmission is built in MATLAB/Simdriveline environment, and an optimum shift schedule is derived by using iSight software to call the dynamic model above, then the shift schedule is achieved after optimization. The simulation results show that the method is significant to improve the dynamic performance and gear-shifting smoothness theoretically and practically.
Hoan, Tran-Nhut-Khai; Hiep, Vu-Van; Koo, In-Soo
2016-03-31
This paper considers cognitive radio networks (CRNs) utilizing multiple time-slotted primary channels in which cognitive users (CUs) are powered by energy harvesters. The CUs are under the consideration that hardware constraints on radio devices only allow them to sense and transmit on one channel at a time. For a scenario where the arrival of harvested energy packets and the battery capacity are finite, we propose a scheme to optimize (i) the channel-sensing schedule (consisting of finding the optimal action (silent or active) and sensing order of channels) and (ii) the optimal transmission energy set corresponding to the channels in the sensing order for the operation of the CU in order to maximize the expected throughput of the CRN over multiple time slots. Frequency-switching delay, energy-switching cost, correlation in spectrum occupancy across time and frequency and errors in spectrum sensing are also considered in this work. The performance of the proposed scheme is evaluated via simulation. The simulation results show that the throughput of the proposed scheme is greatly improved, in comparison to related schemes in the literature. The collision ratio on the primary channels is also investigated.
Optimal Energy-Efficient Downlink Transmission Scheduling for Real-Time Wireless Networks
Miao, Lei; Mao, Jianfeng; Cassandras, Christos G.
2016-01-01
It has been shown that using appropriate channel coding schemes in wireless environments, transmission energy can be significantly reduced by controlling the packet transmission rate. This paper seeks optimal solutions for downlink transmission control problems, motivated by this observation and by the need to minimize energy consumption in real-time wireless networks. Our problem formulation deals with a more general setting than the paper authored by Gamal et. al., in which the MoveRight al...
Directory of Open Access Journals (Sweden)
Rajkumar Ragunathan
2005-01-01
Full Text Available Energy efficiency is imperative to enable the deployment of ad hoc networks. Conventional power management focuses independently on the physical or MAC layer and approaches differ depending on the abstraction level. At the physical layer, the fundamental tradeoff between transmission rate and energy is exploited, which leads to transmit as slow as possible. At MAC level, power reduction techniques aim to transmit as fast as possible to maximize the radios power-off interval. The two approaches seem conflicting and it is not obvious which one is the most appropriate. We propose a transmission strategy that optimally mixes both techniques in a multiuser context. We present a cross-layer solution considering the transceiver power characteristics, the varying system load, and the dynamic channel constraints. Based on this, we derive a low-complexity online scheduling algorithm. Results considering an -ary quadrature amplitude modulation radio show that for a range of scenarios a large power reduction is achieved, compared to the case where only scaling or shutdown is considered.
Improved MLWDF scheduler for LTE downlink transmission
Obinna Nnamani, Christantus; Anioke, Chidera Linda; Ikechukwu Ani, Cosmas
2016-11-01
In long-term evolution (LTE) downlink transmission, modified least weighted delay first (MLWDF) scheduler is a quality of service (QoS) aware scheduling scheme for real-time (RT) services. Nevertheless, MLWDF performs below optimal among the trade-off between strict delay and loss restraints of RT and non-RT traffic flows, respectively. This is further worsened with the implementation of hybrid automatic retransmission request (HARQ). As these restraints grow unabated with increasing number of user demands, the performance of MLWDF further reduces. In order to ameliorate this situation, there is a need to directly incorporate the variations in user demands and HARQ implementation as parameters to the MLWDF scheduler. In this work, an improvement to the MLWDF scheduler is proposed. The improvement entails adding two novel parameters that characterise user demand and HARQ implementation. The scheduler was tested using varying three classes of service in QoS class identifiers (QCIs) table standardised by Third Generation Partnership Project for LTE network to characterise different services. It was also tested on the basis of packet prioritisation. The proposed scheduler was simulated with LTE-SIM simulator and compared with the MLWDF and proportional fairness schedulers. In terms of delay, throughput and packet loss ratio; the proposed scheduler increased overall system performance.
Maritime wideband communication networks video transmission scheduling
Yang, Tingting
2014-01-01
This Springer Brief covers emerging maritime wideband communication networks and how they facilitate applications such as maritime distress, urgency, safety and general communications. It provides valuable insight on the data transmission scheduling and protocol design for the maritime wideband network. This brief begins with an introduction to maritime wideband communication networks including the architecture, framework, operations and a comprehensive survey on current developments. The second part of the brief presents the resource allocation and scheduling for video packet transmission wit
Structure-Aware Stochastic Control for Transmission Scheduling
Fu, Fangwen
2010-01-01
In this paper, we consider the problem of real-time transmission scheduling over time-varying channels. We first formulate the transmission scheduling problem as a Markov decision process (MDP) and systematically unravel the structural properties (e.g. concavity in the state-value function and monotonicity in the optimal scheduling policy) exhibited by the optimal solutions. We then propose an online learning algorithm which preserves these structural properties and achieves -optimal solutions for an arbitrarily small . The advantages of the proposed online method are that: (i) it does not require a priori knowledge of the traffic arrival and channel statistics and (ii) it adaptively approximates the state-value functions using piece-wise linear functions and has low storage and computation complexity. We also extend the proposed low-complexity online learning solution to the prioritized data transmission. The simulation results demonstrate that the proposed method achieves significantly better utility (or de...
Optimal randomized scheduling by replacement
Energy Technology Data Exchange (ETDEWEB)
Saias, I.
1996-05-01
In the replacement scheduling problem, a system is composed of n processors drawn from a pool of p. The processors can become faulty while in operation and faulty processors never recover. A report is issued whenever a fault occurs. This report states only the existence of a fault but does not indicate its location. Based on this report, the scheduler can reconfigure the system and choose another set of n processors. The system operates satisfactorily as long as, upon report of a fault, the scheduler chooses n non-faulty processors. We provide a randomized protocol maximizing the expected number of faults the system can sustain before the occurrence of a crash. The optimality of the protocol is established by considering a closely related dual optimization problem. The game-theoretic technical difficulties that we solve in this paper are very general and encountered whenever proving the optimality of a randomized algorithm in parallel and distributed computation.
Optimal Deadline Scheduling with Commitment
Chen, Shiyao; He, Ting
2011-01-01
We consider an online preemptive scheduling problem where jobs with deadlines arrive sporadically. A commitment requirement is imposed such that the scheduler has to either accept or decline a job immediately upon arrival. The scheduler's decision to accept an arriving job constitutes a contract with the customer; if the accepted job is not completed by its deadline as promised, the scheduler loses the value of the corresponding job and has to pay an additional penalty depending on the amount of unfinished workload. The objective of the online scheduler is to maximize the overall profit, i.e., the total value of the admitted jobs completed before their deadlines less the penalty paid for the admitted jobs that miss their deadlines. We show that the maximum competitive ratio is $3-2\\sqrt{2}$ and propose a simple online algorithm to achieve this competitive ratio. The optimal scheduling includes a threshold admission and a greedy scheduling policies. The proposed algorithm has direct applications to the chargin...
Hubble Systems Optimize Hospital Schedules
2009-01-01
Don Rosenthal, a former Ames Research Center computer scientist who helped design the Hubble Space Telescope's scheduling software, co-founded Allocade Inc. of Menlo Park, California, in 2004. Allocade's OnCue software helps hospitals reclaim unused capacity and optimize constantly changing schedules for imaging procedures. After starting to use the software, one medical center soon reported noticeable improvements in efficiency, including a 12 percent increase in procedure volume, 35 percent reduction in staff overtime, and significant reductions in backlog and technician phone time. Allocade now offers versions for outpatient and inpatient magnetic resonance imaging (MRI), ultrasound, interventional radiology, nuclear medicine, Positron Emission Tomography (PET), radiography, radiography-fluoroscopy, and mammography.
Steps Toward Optimal Competitive Scheduling
Frank, Jeremy; Crawford, James; Khatib, Lina; Brafman, Ronen
2006-01-01
This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, se@sh preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource. This problem arises in many institutional settings where, e.g., different departments, agencies, or personal, compete for a single resource. We are particularly motivated by the problem of scheduling NASA's Deep Space Satellite Network (DSN) among different users within NASA. Access to DSN is needed for transmitting data from various space missions to Earth. Each mission has different needs for DSN time, depending on satellite and planetary orbits. Typically, the DSN is over-subscribed, in that not all missions will be allocated as much time as they want. This leads to various inefficiencies - missions spend much time and resource lobbying for their time, often exaggerating their needs. NASA, on the other hand, would like to make optimal use of this resource, ensuring that the good for NASA is maximized. This raises the thorny problem of how to measure the utility to NASA of each allocation. In the typical case, it is difficult for the central agency, NASA in our case, to assess the value of each interval to each user - this is really only known to the users who understand their needs. Thus, our problem is more precisely formulated as follows: find an allocation schedule for the resource that maximizes the sum of users preferences, when the preference values are private information of the users. We bypass this problem by making the assumptions that one can assign money to customers. This assumption is reasonable; a
Optimization of Daily Flight Training Schedules
2014-03-01
training syllabus . 14. SUBJECT TERMS Scheduling, optimization, flight training, Advance Strike Training, pilot 15. NUMBER OF...SKEDSOs that can help them increase throughput of students in the advanced strike training syllabus . vi THIS PAGE INTENTIONALLY LEFT BLANK vii...instructor pilots with student naval aviators to achieve syllabus events. The schedule is built manually each day by squadron scheduling officers (SKEDSOs
Asymptotically Optimal Downlink Scheduling over Markovian Fading Channels
Ouyang, Wenzhuo; Shroff, Ness B
2011-01-01
We consider the scheduling problem in downlink wireless networks with heterogeneous, Markov-modulated, ON/OFF channels. It is well-known that the performance of scheduling over fading channels heavily depends on the accuracy of the available Channel State Information (CSI), which is costly to acquire. Thus, we consider the CSI acquisition via a practical ARQ-based feedback mechanism whereby channel states are revealed at the end of only scheduled users' transmissions. In the assumed presence of temporally-correlated channel evolutions, the desired scheduler must optimally balance the exploitation-exploration trade-off, whereby it schedules transmissions both to exploit those channels with up-to-date CSI and to explore the current state of those with outdated CSI. In earlier works, Whittle's Index Policy had been suggested as a low-complexity and high-performance solution to this problem. However, analyzing its performance in the typical scenario of statistically heterogeneous channel state processes has remai...
The Novel Heuristic for Data Transmission Dynamic Scheduling Problems
Directory of Open Access Journals (Sweden)
Hao Xu
2013-01-01
Full Text Available The data transmission dynamic scheduling is a process that allocates the ground stations and available time windows to the data transmission tasks dynamically for improving the resource utilization. A novel heuristic is proposed to solve the data transmission dynamic scheduling problem. The characteristic of this heuristic is the dynamic hybridization of simple rules. Experimental results suggest that the proposed algorithm is correct, feasible, and available. The dynamic hybridization of simple rules can largely improve the efficiency of scheduling.
Optimal radiotherapy dose schedules under parametric uncertainty
Badri, Hamidreza; Watanabe, Yoichi; Leder, Kevin
2016-01-01
We consider the effects of parameter uncertainty on the optimal radiation schedule in the context of the linear-quadratic model. Our interest arises from the observation that if inter-patient variability in normal and tumor tissue radiosensitivity or sparing factor of the organs-at-risk (OAR) are not accounted for during radiation scheduling, the performance of the therapy may be strongly degraded or the OAR may receive a substantially larger dose than the allowable threshold. This paper proposes a stochastic radiation scheduling concept to incorporate inter-patient variability into the scheduling optimization problem. Our method is based on a probabilistic approach, where the model parameters are given by a set of random variables. Our probabilistic formulation ensures that our constraints are satisfied with a given probability, and that our objective function achieves a desired level with a stated probability. We used a variable transformation to reduce the resulting optimization problem to two dimensions. We showed that the optimal solution lies on the boundary of the feasible region and we implemented a branch and bound algorithm to find the global optimal solution. We demonstrated how the configuration of optimal schedules in the presence of uncertainty compares to optimal schedules in the absence of uncertainty (conventional schedule). We observed that in order to protect against the possibility of the model parameters falling into a region where the conventional schedule is no longer feasible, it is required to avoid extremal solutions, i.e. a single large dose or very large total dose delivered over a long period. Finally, we performed numerical experiments in the setting of head and neck tumors including several normal tissues to reveal the effect of parameter uncertainty on optimal schedules and to evaluate the sensitivity of the solutions to the choice of key model parameters.
Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems
DEFF Research Database (Denmark)
Pop, Traian; Pop, Paul; Eles, Petru;
2005-01-01
We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource...
Utility Optimal Scheduling in Energy Harvesting Networks
Huang, Longbo
2010-01-01
In this paper, we show how to achieve close-to-optimal utility performance in energy harvesting networks with only finite capacity energy storage devices. In these networks, nodes are capable of harvesting energy from the environment. The amount of energy that can be harvested is time varying and evolves according to some probability law. We develop an \\emph{online} algorithm, called the Energy-limited Scheduling Algorithm (ESA), which jointly manages the energy and makes power allocation decisions for packet transmissions. ESA only has to keep track of the amount of energy left at the network nodes and \\emph{does not require any knowledge} of the harvestable energy process. We show that ESA achieves a utility that is within $O(\\epsilon)$ of the optimal, for any $\\epsilon>0$, while ensuring that the network congestion and the required capacity of the energy storage devices are \\emph{deterministically} upper bounded by bounds of size $O(1/\\epsilon)$. We then also develop the Modified-ESA algorithm (MESA) to ac...
STUDY ON SHIFT SCHEDULE AND SIMULATION OF AUTOMATIC TRANSMISSION
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule,a simulation program using a software package of Matlab/Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.
Optimal Schedules in Multitask Motor Learning.
Lee, Jeong Yoon; Oh, Youngmin; Kim, Sung Shin; Scheidt, Robert A; Schweighofer, Nicolas
2016-04-01
Although scheduling multiple tasks in motor learning to maximize long-term retention of performance is of great practical importance in sports training and motor rehabilitation after brain injury, it is unclear how to do so. We propose here a novel theoretical approach that uses optimal control theory and computational models of motor adaptation to determine schedules that maximize long-term retention predictively. Using Pontryagin's maximum principle, we derived a control law that determines the trial-by-trial task choice that maximizes overall delayed retention for all tasks, as predicted by the state-space model. Simulations of a single session of adaptation with two tasks show that when task interference is high, there exists a threshold in relative task difficulty below which the alternating schedule is optimal. Only for large differences in task difficulties do optimal schedules assign more trials to the harder task. However, over the parameter range tested, alternating schedules yield long-term retention performance that is only slightly inferior to performance given by the true optimal schedules. Our results thus predict that in a large number of learning situations wherein tasks interfere, intermixing tasks with an equal number of trials is an effective strategy in enhancing long-term retention.
Intelligent perturbation algorithms for space scheduling optimization
Kurtzman, Clifford R.
1990-01-01
The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.
Use of Data Mining in Scheduler Optimization
Anderson, George; Nelwamondo, Fulufhelo V
2010-01-01
The operating system's role in a computer system is to manage the various resources. One of these resources is the Central Processing Unit. It is managed by a component of the operating system called the CPU scheduler. Schedulers are optimized for typical workloads expected to run on the platform. However, a single scheduler may not be appropriate for all workloads. That is, a scheduler may schedule a workload such that the completion time is minimized, but when another type of workload is run on the platform, scheduling and therefore completion time will not be optimal; a different scheduling algorithm, or a different set of parameters, may work better. Several approaches to solving this problem have been proposed. The objective of this survey is to summarize the approaches based on data mining, which are available in the literature. In addition to solutions that can be directly utilized for solving this problem, we are interested in data mining research in related areas that have potential for use in operat...
Institute of Scientific and Technical Information of China (English)
周刚; 王红斌
2013-01-01
传统的检修优化模型中，设备的检修状态变量采用0、1二元变量表示，无法用粒子群优化算法（PSO）求解。提出了一种新的输变电设备检修优化模型。该模型用整数表示检修状态变量，使得检修约束得以简化，有利于 PSO 的求解。仿真结果表明，与遗传算法（GA）相比，在该模型下 PSO 收敛速度更快，获得更优的解。%The particle swarm optimizer (PSO) is unable for the traditional maintenance scheduling (MS) as its maintenance status variables of equipments are represented by 0 or 1 of binary number. This paper presents a novel model for maintenance scheduling (MS) for generators and transmission lines, in which the maintenance status variables are represented by integer numbers as to simplify maintenance constraints and favor PSO. The simulation results show that in comparison with the genetic algorithm (GA), the PSO with the proposed scheduling can implement with fast convergence and achieve better solution.
Analysis of Transmissions Scheduling with Packet Fragmentation
Directory of Open Access Journals (Sweden)
Nir Menakerman
2001-12-01
Full Text Available We investigate a scheduling problem in which packets, or datagrams, may be fragmented. While there are a few applications to scheduling with datagram fragmentation, our model of the problem is derived from a scheduling problem present in data over CATV networks. In the scheduling problem datagrams of variable lengths must be assigned (packed into fixed length time slots. One of the capabilities of the system is the ability to break a datagram into several fragments. When a datagram is fragmented, extra bits are added to the original datagram to enable the reassembly of all the fragments. We convert the scheduling problem into the problem of bin packing with item fragmentation, which we define in the following way: we are asked to pack a list of items into a minimum number of unit capacity bins. Each item may be fragmented in which case overhead units are added to the size of every fragment. The cost associated with fragmentation renders the problem NP-hard, therefore an approximation algorithm is needed. We define a version of the well-known Next-Fit algorithm, capable of fragmenting items, and investigate its performance. We present both worst case and average case results and compare them to the case where fragmentation is not allowed.
An introduction to optimal satellite range scheduling
Vázquez Álvarez, Antonio José
2015-01-01
The satellite range scheduling (SRS) problem, an important operations research problem in the aerospace industry consisting of allocating tasks among satellites and Earth-bound objects, is examined in this book. SRS principles and solutions are applicable to many areas, including: Satellite communications, where tasks are communication intervals between sets of satellites and ground stations Earth observation, where tasks are observations of spots on the Earth by satellites Sensor scheduling, where tasks are observations of satellites by sensors on the Earth. This self-contained monograph begins with a structured compendium of the problem and moves on to explain the optimal approach to the solution, which includes aspects from graph theory, set theory, game theory and belief networks. This book is accessible to students, professionals and researchers in a variety of fields, including: operations research, optimization, scheduling theory, dynamic programming and game theory. Taking account of the distributed, ...
Fu, Fangwen
2010-01-01
In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate the dynamic scheduling problem as a Markov decision process (MDP) that explicitly considers the users' heterogeneous multimedia data characteristics (e.g. delay deadlines, distortion impacts and dependencies etc.) and time-varying channel conditions, which are not simultaneously considered in state-of-the-art packet scheduling algorithms. This formulation allows us to perform foresighted decisions to schedule multiple data units for transmission at each time in order to optimize the long-term utilities of the multimedia applications. The heterogeneity of the media data enables us to express the transmission priorities between the different data units as a priority graph, which is a directed acyclic graph (DAG). This priority graph provides us with an elegant structure to decompose the multi-data unit foresighted decision at each time i...
Approach for earth observation satellite real-time and playback data transmission scheduling
Institute of Scientific and Technical Information of China (English)
Jun Li
2015-01-01
The scheduling of earth observation satel ites (EOSs) data transmission is a complex combinatorial optimization prob-lem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either play-back or real-time transmission. Considering the characteristic of the problem, a multi-satel ite real-time and playback data trans-mission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization (QDPSO) is proposed. Furthermore, we design the longest compatible trans-mission chain mutation operator to enhance the performance of the algorithm. Final y, some experiments are implemented to vali-date correctness and practicability of the proposed algorithm.
Rate-optimal scheduling of recursive DSP algorithms based on the scheduling-range chart
Heemstra de Groot, Sonia M.; Herrmann, Otto E.
1990-01-01
A method for rate-optimal scheduling of recursive DSP algorithms is presented. The approach is based on the determination of the scheduling window of each operation and the construction of a scheduling-range chart. The information in the chart is used during scheduling to optimize some quality crite
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 Packet Scheduling on an Energy Harvesting Fading Channel
Ozcelik, F Mehmet; Uysal-Biyikoglu, Elif
2012-01-01
An offline transmission completion time minimization problem for an energy harvesting transmitter is considered. Specifically, optimal power and rate allocation for data packets arriving at arbitrary but known instances is studied. Communication takes place under a fading channel and transmitter is restricted with a limited energy storage capability. An optimal policy takes into account the channel state as well as the state of energy and data buffers. Moreover, the solution needs to strike a tradeoff between energy efficiency and delay. By exhibiting an equivalent convex problem, the unique optimal scheduling solution is obtained through an iterative convex optimization technique, sequential unconstrained minimization. The optimal solution under finite and infinite energy storage is examined on problem instances.
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....
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 proj...... 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....
Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission.
Qin, Danyang; Yang, Songxiang; Zhang, Yan; Ma, Jingya; Ding, Qun
2017-06-09
Wireless energy collecting technology can effectively reduce the network time overhead and prolong the wireless sensor network (WSN) lifetime. However, the traditional energy collecting technology cannot achieve the balance between ergodic channel capacity and average collected energy. In order to solve the problem of the network transmission efficiency and the limited energy of wireless devices, three improved scheduling mechanisms are proposed: improved signal noise ratio (SNR) scheduling mechanism (IS2M), improved N-SNR scheduling mechanism (INS2M) and an improved Equal Throughput scheduling mechanism (IETSM) for different channel conditions to improve the whole network performance. Meanwhile, the average collected energy of single users and the ergodic channel capacity of three scheduling mechanisms can be obtained through the order statistical theory in Rayleig, Ricean, Nakagami-m and Weibull fading channels. It is concluded that the proposed scheduling mechanisms can achieve better balance between energy collection and data transmission, so as to provide a new solution to realize synchronous information and energy transmission for WSNs.
Optimization Algorithms in School Scheduling Programs: Study, Analysis and Results
Directory of Open Access Journals (Sweden)
Lina PUPEIKIENE
2009-04-01
Full Text Available To create good and optimal school schedule is very important and practical task. Currently in Lithuania schools are using two programs for making the school schedule at the moment. But none of these programs is very effective. Optimization Department of Lithuanian Institute of Mathematics and Informatics (IMI has created ``School schedule optimization program''. It has three optimization algorithms for making best school schedule. A user can choose not only few optimization options and get few optimal schedules, but some subjective and objectives parameters. The making of initial data file is advanced in this program. XML format is used for creating initial data file and getting all optimal results files. The purpose of this study is to analyze used optimization algorithms used in ``School schedule optimization program'' and to compare results with two most popular commercial school scheduling programs in Lithuania.
Optimal feedback scheduling of model predictive controllers
Institute of Scientific and Technical Information of China (English)
Pingfang ZHOU; Jianying XIE; Xiaolong DENG
2006-01-01
Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
Utility Optimal Scheduling in Processing Networks
Huang, Longbo
2010-01-01
We consider the problem of utility optimal scheduling in general \\emph{processing networks} with random arrivals and network conditions. These are generalizations of traditional data networks where commodities in one or more queues can be combined to produce new commodities that are delivered to other parts of the network. This can be used to model problems such as in-network data fusion, stream processing, and grid computing. Scheduling actions are complicated by the \\emph{underflow problem} that arises when some queues with required components go empty. In this paper, we develop the Perturbed Max-Weight algorithm (PMW) to achieve optimal utility. The idea of PMW is to perturb the weights used by the usual Max-Weight algorithm to ``push'' queue levels towards non-zero values (avoiding underflows). We show that when the perturbations are carefully chosen, PMW is able to achieve a utility that is within $O(1/V)$ of the optimal value for any $V\\geq1$, while ensuring an average network backlog of $O(V)$.
Topology-Transparent Transmission Scheduling Algorithms in Wireless Ad Hoc Networks
Institute of Scientific and Technical Information of China (English)
MA Xiao-lei; WANG Chun-jiang; LIU Yuan-an; MA Lei-lei
2005-01-01
In order to maximize the average throughput and minimize the transmission slot delay in wireless Ad Hoc networks,an optimal topology-transparent transmission scheduling algorithm-multichannel Time-Spread Multiple Access(TSMA)is proposed.Further analysis is shown that the maximum degree is very sensitive to the network performance for a wireless Ad Hoc networks with N mobile nodes.Moreover,the proposed multichannel TSMA can improve the average throughput M times and decrease the average transmission slot delay M times,as compared with singlechannel TSMA when M channels are available.
Group Elevator Peak Scheduling Based on Robust Optimization Model
Directory of Open Access Journals (Sweden)
ZHANG, J.
2013-08-01
Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.
Earth Observation Satellites Scheduling Based on Decomposition Optimization Algorithm
Directory of Open Access Journals (Sweden)
Feng Yao
2010-11-01
Full Text Available A decomposition-based optimization algorithm was proposed for solving Earth Observation Satellites scheduling problem. The problem was decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellite scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy were introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony optimization algorithm, which can guide the search process of ant colony optimization. Computation results showed that the approach was effective to the satellites observation scheduling problem.
Throughput Optimal Scheduling with Feedback Cost Reduction
Karaca, Mehmet; Ercetin, Ozgur; Alpcan, Tansu; Boche, Holger
2012-01-01
It is well known that opportunistic scheduling algorithms are throughput optimal under full knowledge of channel and network conditions. However, these algorithms achieve a hypothetical achievable rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. We adopt a channel probing model where $\\beta$ fraction of time slot is consumed for acquiring the channel state information (CSI) of a single channel. In this work, we design a joint scheduling and channel probing algorithm named SDF by considering the overhead of obtaining the channel state information. We analytically prove that when the number of users in the network is greater than 3, then SDF algorithm can achieve $1+\\epsilon$ of the full rate region achieved when all users are probed. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by probing only less than 50% of all channels in a CDM...
An Optimized Round Robin Scheduling Algorithm for CPU Scheduling
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Ajit Singh
2010-10-01
Full Text Available The main objective of this paper is to develop a new approach for round robin scheduling which help to improve the CPU efficiency in real time and time sharing operating system. There are many algorithms available for CPU scheduling. But we cannot implemented in real time operating system because of high context switch rates, large waiting time, large response time, large trn around time and less throughput. The proposed algorithm improves all the drawback of simple round robin architecture. The author have also given comparative analysis of proposed with simple round robin scheduling algorithm. Therefore, the author strongly feel that the proposed architecture solves all the problem encountered in simple round robin architecture by decreasing the performance parameters to desirable extent and thereby increasing the system throughput.
Saghari, Poorya; Kamath, P; Arbab, Vahid R; Haghi, Mahta; Willner, Alan E; Bannister, Joe A; Touch, Joe D
2007-12-10
We experimentally demonstrate a transmission scheduling algorithm to avoid congestion collapse in O-CDMA networks. Our result shows that transmission scheduling increases the performance of the system by orders of magnitude.
Ant Colony Optimization for Train Scheduling: An Analysis
Sudip Kumar Sahana; Aruna Jain; Prabhat Kumar Mahanti
2014-01-01
This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.
Ant Colony Optimization for Train Scheduling: An Analysis
Directory of Open Access Journals (Sweden)
Sudip Kumar Sahana
2014-01-01
Full Text Available This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS and Standard Train Scheduling (STS algorithm has been performed.
An Optimal Transportation Schedule of Mobile Equipment
Directory of Open Access Journals (Sweden)
S. Guillén-Burguete
2012-10-01
Full Text Available Motivated by a problem faced by road construction companies, we develop a new model to obtain an optimaltransportation schedule of mobile machines which have to travel to execute tasks. In this problem, each task ischaracterized by the location where it is to be executed, a work-content in terms of machine-time units, and one ormore time intervals within which it can be performed. The machines can be transported from one location to anotherat any time, thus the problem has an indefinite number of variables. However, this indefinite number of variables canbe reduced to a definite one because, as we prove, the problem has an optimal solution in which the arrivals ofmachines occur only at certain time instants. The objective is to minimize the total transportation cost such that all thetasks are executed within their time intervals. The constraints ensuring that the tasks are processed within theirprescribed time intervals are nonlinear; nevertheless, due to the sets of the possible arrival times of the machinesforming bounded convex polyhedra, our problem can be transformed into a mixed integer linear program by the samedevice used in the decomposition principle of Dantzig-Wolfe.
Optimal Transmission Policies for Energy Harvesting Two-hop Networks
Orhan, Oner
2012-01-01
In this paper, a two-hop communication system with energy harvesting nodes is considered. Unlike battery powered wireless nodes, both the source and the relay are able to harvest energy from environment during communication, therefore, both data and energy causality over the two hops need to be considered. Assuming both nodes know the harvested energies in advance, properties of optimal transmission policies to maximize the delivered data by a given deadline are identified. Using these properties, optimal power allocation and transmission schedule for the case in which both nodes harvest two energy packets is developed.
Serial vs. Concurrent Scheduling of Transmission and Processing Tasks in Collaborative Systems
Junuzovic, Sasa; Dewan, Prasun
In collaboration architectures, a computer must perform both processing and transmission tasks. Intuitively, it seems that these independent tasks should be executed in concurrent threads. We show that when multiple cores are not available to schedule these tasks, a sequential scheme in which the processing (transmission) task is done first tends to optimize feedback (feedthrough) times for most users. The concurrent policy gives feedback and feedthrough times that are in between the ones supported by the sequential policies. However, in comparison to the process-first policy, it can noticeably degrade feedback times, and in comparison to the transmit-first policy, it can noticeably degrade feedthrough times without noticeably improving feedback times. We present definitions, examples, and simulations that explain and compare these three scheduling schemes for centralized and replicated collaboration architectures using both unicast and multicast communication.
Optimizing Transmission Line Matching Circuits
Novak, S.
1996-01-01
When designing transmission line matching circuits, there exist often overlooked, additional, not much used, degree of choice in the selection of the transmission line impedance. In this work are presented results of CAD analysis for the two element transmission line matching networks, demonstrating that selecting matching circuits transmission lines with higher impedance, than usually used 50 or 75 ohms, can in most cases substantially decrease the physical dimension of the final matching ci...
Aircraft nonlinear optimal control using fuzzy gain scheduling
Nusyirwan, I. F.; Kung, Z. Y.
2016-10-01
Fuzzy gain scheduling is a common solution for nonlinear flight control. The highly nonlinear region of flight dynamics is determined throughout the examination of eigenvalues and the irregular pattern of root locus plots that show the nonlinear characteristic. By using the optimal control for command tracking, the pitch rate stability augmented system is constructed and the longitudinal flight control system is established. The outputs of optimal control for 21 linear systems are fed into the fuzzy gain scheduler. This research explores the capability in using both optimal control and fuzzy gain scheduling to improve the efficiency in finding the optimal control gains and to achieve Level 1 flying qualities. The numerical simulation work is carried out to determine the effectiveness and performance of the entire flight control system. The simulation results show that the fuzzy gain scheduling technique is able to perform in real time to find near optimal control law in various flying conditions.
Developing optimal nurses work schedule using integer programming
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.
Energy Technology Data Exchange (ETDEWEB)
Cardoso, Goncalo [Technical Univ. of Lisbon (Portugal); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Center for Energy and Innovation Technologies (Austria); Bozchalui, Mohammed C. [NEC Laboratories American Inc., Irving, TX (United States); Sharma, Ratnesh [NEC Laboratories American Inc., Irving, TX (United States); Marnay, Chris [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Barbosa-Povoa, Ana [Technical Univ. of Lisbon (Portugal); Ferrao, Paulo [Technical Univ. of Lisbon (Portugal)
2013-12-06
The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.
Directory of Open Access Journals (Sweden)
Bingtuan Gao
2015-12-01
Full Text Available In a deregulated environment of the power market, in order to lower their energy price and guarantee the stability of the power network, appropriate transmission lines have to be considered for electricity generators to sell their energy to the end users. This paper proposes a game-theoretic power transmission scheduling for multiple generators to lower their wheeling cost. Based on the embedded cost method, a wheeling cost model consisting of congestion cost, cost of losses and cost of transmission capacity is presented. By assuming each generator behaves in a selfish and rational way, the competition among the multiple generators is formulated as a non-cooperative game, where the players are the generators and the strategies are their daily schedules of power transmission. We will prove that there exists at least one pure-strategy Nash equilibrium of the formulated power transmission game. Moreover, a distributed algorithm will be provided to realize the optimization in terms of minimizing the wheeling cost. Finally, simulations were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game approach for the generators in a deregulated environment.
STUDY ON SHIFT SCHEDULE OF AUTOMATIC TRANSMISSION TO IMPROVE ENGINEERING VEHICULAR EFFICIENCY
Institute of Scientific and Technical Information of China (English)
Gong Jie; Zhao Dingxuan; Huang Haidong; Gong Wenbin; Chen Ying
2004-01-01
New shift schedule for automatic transmission is proposed from the point of view of saving energy.The bench-test of automatic shift adopting this shift schedule is done on automatic transmission's test-bed.The experimental results show the shift schedule is correct.This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynamic drive system of the vehicle.
Particle swarm optimization based space debris surveillance network scheduling
Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao
2017-02-01
The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.
customer-teller scheduling system for optimizing banks service
African Journals Online (AJOL)
Customer satisfaction is a concern to service industries as customers expect to be served promptly ... els Bank Teller scheduling system for optimizing a Banks customer service. The ...... of Terminology, Trends and Application to. Pharmacy ...
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.
Software Project Scheduling Management by Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Dinesh B. Hanchate
2014-12-01
Full Text Available PSO (Particle Swarm Optimization is, like GA, a heuristic global optimization method based on swarm intelligence. In this paper, we present a particle swarm optimization algorithm to solve software project scheduling problem. PSO itself inherits very efficient local search method to find the near optimal and best-known solutions for all instances given as inputs required for SPSM (Software Project Scheduling Management. At last, this paper imparts PSO and research situation with SPSM. The effect of PSO parameter on project cost and time is studied and some better results in terms of minimum SCE (Software Cost Estimation and time as compared to GA and ACO are obtained.
Locomotive Schedule Optimization for Da-qin Heavy Haul Railway
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Ruiye Su
2015-01-01
Full Text Available The main difference between locomotive schedule of heavy haul railways and that of regular rail transportation is the number of locomotives utilized for one train. One heavy-loaded train usually has more than one locomotive, but a regular train only has one. This paper develops an optimization model for the multilocomotive scheduling problem (MLSP through analyzing the current locomotive schedule of Da-qin Railway. The objective function of our paper is to minimize the total number of utilized locomotives. The MLSP is nondeterministic polynomial (NP hard. Therefore, we convert the multilocomotive traction problem into a single-locomotive traction problem. Then, the single-locomotive traction problem (SLTP can be converted into an assignment problem. The Hungarian algorithm is applied to solve the model and obtain the optimal locomotive schedule. We use the variance of detention time of locomotives at stations to evaluate the stability of locomotive schedule. In order to evaluate the effectiveness of the proposed optimization model, case studies for 20 kt and 30 kt heavy-loaded combined trains on Da-qin Railway are both conducted. Compared to the current schedules, the optimal schedules from the proposed models can save 62 and 47 locomotives for 20 kt and 30 kt heavy-loaded combined trains, respectively. Therefore, the effectiveness of the proposed model and its solution algorithm are both valid.
Optimal dynamic scheduling of a power generation system to satisfy multiple criteria
DEFF Research Database (Denmark)
Forouzbakhsh, Farshid; Deiters, Robert M.; Shoushtari Kermanshahi, Bahman
1991-01-01
A computer algorithm for the optimal scheduling of generators in a power system is presented and tested. The algorithm, based on goal programming, automatically and dynami cally schedules the output of each generator in the system for optimal operation. The optimal operation can take into conside......A computer algorithm for the optimal scheduling of generators in a power system is presented and tested. The algorithm, based on goal programming, automatically and dynami cally schedules the output of each generator in the system for optimal operation. The optimal operation can take...... into consideration multiple objectives such as economy, security, and reduction of pollution as well as practical constraints. To validate and test the algorithm, an example system of 5 generators, 10 busses, and 11 transmission lines is optimized for two objectives: minimal generation cost and minimal emission...... of nitrous oxides (NOx). Hourly changes in total power demand in the range of 90% to 110% are considered together with a constraint of maximum permissible total NOx emission. Other practical equality and inequality constraints are incorporated into the optimization algorithm. The simulation results...
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
A microgrid is a system that integrates energy generation, energy storage, and loads and it is able to operate either in interconnected or islanded mode. Energy resources should be scheduled to supply the load properly in order to coordinate optimally the power exchange within the microgrid...... according to a defined objective function. In this paper, an optimal power scheduling for generation and demand side is presented to manage an islanded hybrid PV-wind-battery microgrid implemented in Shanghai-China. The optimization is addressed through a Mixed-Integer Linear Programming (MILP) mathematical......SPACE1006) in which a scaled down model of this microgrid is emulated....
Grid Computing based on Game Optimization Theory for Networks Scheduling
Directory of Open Access Journals (Sweden)
Peng-fei Zhang
2014-05-01
Full Text Available The resource sharing mechanism is introduced into grid computing algorithm so as to solve complex computational tasks in heterogeneous network-computing problem. However, in the Grid environment, it is required for the available resource from network to reasonably schedule and coordinate, which can get a good workflow and an appropriate network performance and network response time. In order to improve the performance of resource allocation and task scheduling in grid computing method, a game model based on non-cooperation game is proposed. Setting the time and cost of user’s resource allocation can increase the performance of networks, and incentive resource of networks uses an optimization scheduling algorithm, which minimizes the time and cost of resource scheduling. Simulation experiment results show the feasibility and suitability of model. In addition, we can see from the experiment result that model-based genetic algorithm is the best resource scheduling algorithm
Synthesis and Stochastic Assessment of Cost-Optimal Schedules
Mader, A.H.; Bohnenkamp, H.C.; Usenko, Y.S.; Jansen, D.N.; J L Hurink; Hermanns, H.
2006-01-01
We treat the problem of generating cost-optimal schedules for orders with individual due dates and cost functions based on earliness/tardiness. Orders can run in parallel in a resource-constrained manufacturing environment, where resources are subject to stochastic breakdowns. The goal is to generate schedules while minimizing the expected costs. First, we estimate the distribution of each order type by simulation (assuming a reasonable machine/load model) and derive from the cost-function an...
Electric power systems advanced forecasting techniques and optimal generation scheduling
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
Spatial Scheduling Optimization Algorithm for Block Assembly in Shipbuilding
Directory of Open Access Journals (Sweden)
Zhengyang Shang
2017-01-01
Full Text Available Block assembly consumes the majority of processing time and resources in shipbuilding, and the block spatial scheduling (BSS related to block assembly has been widely studied as the key to improve shipbuilding efficiency. BSS is a complicated NP-hard problem that aims to minimize the makespan. Since each block has specific building time and space constraints, the BSS problem can be hardly found with an acceptable solution by using constant scheduling rules. Thus, in this study, we considered the BSS problem as a time-constrained 3D bin packing mathematical model and proposed an allocation algorithm, best contact algorithm (BCA, that is more suitable for dynamic processes. Then, for global optimization of the BSS problem, we regarded the starting time of each block as a variable and used the genetic algorithm (GA to operate and optimize the block assembly sequence. Finally, we tested the BCA + GA scheduling system with real data from a shipyard and thereby determined the block scheduling status and the daily utilization rate of the work plate. Comparison shows that the proposed algorithm is able to get shorter makespan and better block scheduling effect; it realized the optimization of the block spatial scheduling dynamically.
A Hybrid Algorithm for Satellite Data Transmission Schedule Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
LI Yun-feng; WU Xiao-yue
2008-01-01
A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission. At first, based on description of satellite data transmission request, satellite data transmission task modal and satellite data transmission scheduling problem model are established. Secondly, the conflicts in scheduling are discussed. According to the meaning of possible conflict, the method to divide possible conflict task set is given. Thirdly, a hybrid algorithm which consists of genetic algorithm and heuristic information is presented. The heuristic information comes from two concepts, conflict degree and conflict number. Finally, an example shows the algorithm's feasibility and performance better than other traditional algorithms.
Scheduling Patients’ Appointments: Allocation of Healthcare Service Using Simulation Optimization
Directory of Open Access Journals (Sweden)
Ping-Shun Chen
2015-01-01
Full Text Available In the service industry, scheduling medical procedures causes difficulties for both patients and management. Factors such as fluctuations in customer demand and service time affect the appointment scheduling systems’ performance in terms of, for example, patients’ waiting time, idle time of resources, and total cost/profits. This research implements four appointment scheduling policies, i.e., constant arrival, mixed patient arrival, three-section pattern arrival, and irregular arrival, in an ultrasound department of a hospital in Taiwan. By simulating the four implemented policies’ optimization procedures, optimal or near-optimal solutions can be obtained for patients per arrival, patients’ inter-arrival time, and the number of the time slots for arrived patients. Furthermore, three objective functions are tested, and the results are discussed. The managerial implications and discussions are summarized to demonstrate how outcomes can be useful for hospital managers seeking to allocate their healthcare service capacities.
An optimal scheduling algorithm based on task duplication
Institute of Scientific and Technical Information of China (English)
Ruan Youlin; Liu Gan; Zhu Guangxi; Lu Xiaofeng
2005-01-01
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O ( v2 ), where v represents the number of tasks.
Scheduling with Bus Access Optimization for Distributed Embedded Systems
DEFF Research Database (Denmark)
Eles, Petru; Doboli, Alex; Pop, Paul;
2000-01-01
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...... 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...... have to be considered during scheduling but also the parameters of the communication protocol should be adapted to fit the particular embedded application. The optimization algorithm, which implies both process scheduling and optimization of the parameters related to the communication protocol...
Study on shift schedule saving energy of automatic transmission of ground vehicles
Institute of Scientific and Technical Information of China (English)
龚捷; 赵丁选; 陈鹰; 陈宁
2004-01-01
To improve ground vehicle efficiency, shift schedule energy saving was proposed for the ground vehicle automatic transmission by studying the function of the torque converter and transmission in the vehicular drivetrain. The shift schedule can keep the torque converter working in the high efficiency range under all the working conditions except in the low efficiency range on the left when the transmission worked at the lowest shift, and in the low efficiency range on the right when the transmission worked at the highest shift. The shift quality key factors were analysed. The automatic trans-mission's bench-test adopting this shift schedule was made on the automatic transmission's test-bed. The experimental results showed that the shift schedule was correct and that the shift quality was controllable.
Study on shift schedule saving energy of automatic transmission of ground vehicles
Institute of Scientific and Technical Information of China (English)
龚捷; 赵丁选; 陈鹰; 陈宁
2004-01-01
To improve ground vehicle efficiency,shift schedule energy saving was proposed for the ground vehicle automatic transmission by studying the function of the torque converter and transmission in the vehicular drivetrain.The shift schedule can keep the torque converter working in the high efficiency range under all the working conditions except in the low efficiency range on the left when the transmission worked at the lowest shift,and in the low efficiency range on the right when the transmission worked at the highest shift.The shift quality key factors were analysed.The automatic transmission's bench-test adopting this shift schedule was made on the automatic transmission's test-bed.The experimental results showed that the shift schedule was correct and that the shift quality was controllable.
Directory of Open Access Journals (Sweden)
Imam Ahmad Ashari
2016-11-01
Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.
Directory of Open Access Journals (Sweden)
Imam Ahmad Ashari
2016-11-01
Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.
Optimal vaccination schedule search using genetic algorithm over MPI technology
Directory of Open Access Journals (Sweden)
Calonaci Cristiano
2012-11-01
Full Text Available Abstract Background Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. Methods To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. Results & Conclusions The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented.
Optimal load scheduling in commercial and residential microgrids
Ganji Tanha, Mohammad Mahdi
Residential and commercial electricity customers use more than two third of the total energy consumed in the United States, representing a significant resource of demand response. Price-based demand response, which is in response to changes in electricity prices, represents the adjustments in load through optimal load scheduling (OLS). In this study, an efficient model for OLS is developed for residential and commercial microgrids which include aggregated loads in single-units and communal loads. Single unit loads which include fixed, adjustable and shiftable loads are controllable by the unit occupants. Communal loads which include pool pumps, elevators and central heating/cooling systems are shared among the units. In order to optimally schedule residential and commercial loads, a community-based optimal load scheduling (CBOLS) is proposed in this thesis. The CBOLS schedule considers hourly market prices, occupants' comfort level, and microgrid operation constraints. The CBOLS' objective in residential and commercial microgrids is the constrained minimization of the total cost of supplying the aggregator load, defined as the microgrid load minus the microgrid generation. This problem is represented by a large-scale mixed-integer optimization for supplying single-unit and communal loads. The Lagrangian relaxation methodology is used to relax the linking communal load constraint and decompose the independent single-unit functions into subproblems which can be solved in parallel. The optimal solution is acceptable if the aggregator load limit and the duality gap are within the bounds. If any of the proposed criteria is not satisfied, the Lagrangian multiplier will be updated and a new optimal load schedule will be regenerated until both constraints are satisfied. The proposed method is applied to several case studies and the results are presented for the Galvin Center load on the 16th floor of the IIT Tower in Chicago.
Scheduling Combination Optimization Research for Bus Lane Line
Directory of Open Access Journals (Sweden)
Yazao Yang
2013-07-01
Full Text Available Different scheduling forms can be adopted in bus lane system to meet passengers’ travel demand well and improve operational efficiency. Therefore, this paper researched the optimal headway and bus scheduling combination of a bus lane line. Bus scheduling combination is composed of a sequence of full-length, express bus and short-turn. We established a model to minimize passengers’ waiting cost and vehicles’ operation cost and to optimize headway and bus scheduling combination, under the constraint of the headway restriction for each stop, the minimum number of vehicle trips in one hour and the proportion of short-turn and express bus trips in total trips. The model was solved by improved genetic algorithm, and the optimal solution was obtained by repeating the operation of genetic algorithm L times. The results of numerical example show that the whole cost can be saved by 21.77% at most after optimization, which indicate the model and algorithm we presented are reasonable and practicable.
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...
Verification and optimization of a PLC control schedule
Brinksma, Ed; Mader, Angelika; Fehnker, Ansgar
2002-01-01
We report on the use of model checking techniques for both the verification of a process control program and the derivation of optimal control schedules. Most of this work has been carried out as part of a case study for the EU VHS project (Verification of Hybrid Systems), in which the program for a
Optimization models for flight test scheduling
Holian, Derreck
As threats around the world increase with nations developing new generations of warfare technology, the Unites States is keen on maintaining its position on top of the defense technology curve. This in return indicates that the U.S. military/government must research, develop, procure, and sustain new systems in the defense sector to safeguard this position. Currently, the Lockheed Martin F-35 Joint Strike Fighter (JSF) Lightning II is being developed, tested, and deployed to the U.S. military at Low Rate Initial Production (LRIP). The simultaneous act of testing and deployment is due to the contracted procurement process intended to provide a rapid Initial Operating Capability (IOC) release of the 5th Generation fighter. For this reason, many factors go into the determination of what is to be tested, in what order, and at which time due to the military requirements. A certain system or envelope of the aircraft must be assessed prior to releasing that capability into service. The objective of this praxis is to aide in the determination of what testing can be achieved on an aircraft at a point in time. Furthermore, it will define the optimum allocation of test points to aircraft and determine a prioritization of restrictions to be mitigated so that the test program can be best supported. The system described in this praxis has been deployed across the F-35 test program and testing sites. It has discovered hundreds of available test points for an aircraft to fly when it was thought none existed thus preventing an aircraft from being grounded. Additionally, it has saved hundreds of labor hours and greatly reduced the occurrence of test point reflight. Due to the proprietary nature of the JSF program, details regarding the actual test points, test plans, and all other program specific information have not been presented. Generic, representative data is used for example and proof-of-concept purposes. Apart from the data correlation algorithms, the optimization associated
MULTICHANNEL COGNITIVE CROSS LAYER OPTIMIZATION FOR IMPROVED VIDEO TRANSMISSION
Directory of Open Access Journals (Sweden)
Manimekalai Thirunavukkarasu
2013-01-01
Full Text Available Multimedia applications particularly real-time video transmission in wireless networks, envisions end to end user perceived video quality as an important QoS parameter to be achieved. Cognitive Radio promising efficient spectrum utilization combined with Cross layer optimization is seen as a powerful combination to achieve the desired video quality. This study proposes Optimal Channel Sensed Multichannel Cognitive MAC (OCSM-CMAC, a QoS driven cross layer system for the joint optimization of different network parameters along the network protocol stack for the improved video transmission. The primary network activity and wireless propagation dependent channel quality are modeled. Depending on the availability of the primary channel and channel condition as provided by an optimal sensing scheme and the encoder parameter in the application layer, cognitive MAC scheduling and PHY layer modulation and coding for the secondary user are optimized to achieve the required QoS. The simulation of channel and the cognitive user activity is done in MATLAB, while the application video coding is performed by H.264/AVC JM 15.1 codec to obtain the results. The results of the proposed OCSM-CMAC scheme demonstrate that improved PSNR and delay performance is achieved under the optimal channel sensing scheme compared to the random sensing scheme.
Joint Throughput Maximization and Fair Uplink Transmission Scheduling in CDMA Systems
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Li Chengzhou
2009-01-01
Full Text Available We study the fundamental problem of optimal transmission scheduling in a code-division multiple-access wireless system in order to maximize the uplink system throughput, while satisfying the users quality-of-service (QoS requirements and maintaining fairness among them. The corresponding problem is expressed as a weighted throughput maximization problem, under certain power and QoS constraints, where the weights are the control parameters reflecting the fairness constraints. With the introduction of the power index capacity, it is shown that this optimization problem can be converted into a binary knapsack problem, where all the corresponding constraints are replaced by the power index capacities at some certain system power index. A two-step approach is followed to obtain the optimal solution. First, a simple method is proposed to find the optimal set of users to receive service for a given fixed target system load, and then the optimal solution is obtained as a global search within a certain range. Furthermore, a stochastic approximation method is presented to effectively identify the required control parameters. The performance evaluation reveals the advantages of our proposed policy over other existing ones and confirms that it achieves very high throughput while maintains fairness among the users, under different channel conditions and requirements.
Nagata, Takeshi; Tao, Yasuhiro; Utatani, Masahiro; Sasaki, Hiroshi; Fujita, Hideki
This paper proposes a multi-agent approach to maintenance scheduling in restructured power systems. The restructuring of electric power industry has resulted in market-based approaches for unbundling a multitude of service provided by self-interested entities such as power generating companies (GENCOs), transmission providers (TRANSCOs) and distribution companies (DISCOs). The Independent System Operator (ISO) is responsible for the security of the system operation. The schedule submitted to ISO by GENCOs and TRANSCOs should satisfy security and reliability constraints. The proposed method consists of several GENCO Agents (GAGs), TARNSCO Agents (TAGs) and a ISO Agent(IAG). The IAG’s role in maintenance scheduling is limited to ensuring that the submitted schedules do not cause transmission congestion or endanger the system reliability. From the simulation results, it can be seen the proposed multi-agent approach could coordinate between generation and transmission maintenance schedules.
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...... the smart charging behavior and detailed parameters needed for charging behavior of an individual EV are analyzed. The individual charging schedule is extended to the EV fleet. Simulation results are presented to illustrate the effectiveness of the proposed model....
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...
Optimal physicians schedule in an Intensive Care Unit
Hidri, L.; Labidi, M.
2016-05-01
In this paper, we consider a case study for the problem of physicians scheduling in an Intensive Care Unit (ICU). The objective is to minimize the total overtime under complex constraints. The considered ICU is composed of three buildings and the physicians are divided accordingly into six teams. The workload is assigned to each team under a set of constraints. The studied problem is composed of two simultaneous phases: composing teams and assigning the workload to each one of them. This constitutes an additional major hardness compared to the two phase's process: composing teams and after that assigning the workload. The physicians schedule in this ICU is used to be done manually each month. In this work, the studied physician scheduling problem is formulated as an integer linear program and solved optimally using state of the art software. The preliminary experimental results show that 50% of the overtime can be saved.
Energy-optimal programming and scheduling of the manufacturing operations
Badea, N.; Frumuşanu, G.; Epureanu, A.
2016-08-01
The shop floor energy system covers the energy consumed for both the air conditioning and manufacturing processes. At the same time, most of energy consumed in manufacturing processes is converted in heat released in the shop floor interior and has a significant influence on the microclimate. Both these components of the energy consumption have a time variation that can be realistic assessed. Moreover, the consumed energy decisively determines the environmental sustainability of the manufacturing operation, while the expenditure for running the shop floor energy system is a significant component of the manufacturing operations cost. Finally yet importantly, the energy consumption can be fundamentally influenced by properly programming and scheduling of the manufacturing operations. In this paper, we present a method for modeling and energy-optimal programming & scheduling the manufacturing operations. In this purpose, we have firstly identified two optimization targets, namely the environmental sustainability and the economic efficiency. Then, we have defined three optimization criteria, which can assess the degree of achieving these targets. Finally, we have modeled the relationship between the optimization criteria and the parameters of programming and scheduling. In this way, it has been revealed that by adjusting these parameters one can significantly improve the sustainability and efficiency of manufacturing operations. A numerical simulation has proved the feasibility and the efficiency of the proposed method.
APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOP SCHEDULING PROBLEM
Institute of Scientific and Technical Information of China (English)
Xia Weijun; Wu Zhiming; Zhang Wei; Yang Genke
2004-01-01
A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based algorithm is a viable and effective approach for the job-shop scheduling problem.
Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm
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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.
Using Optimization Models for Scheduling in Enterprise Resource Planning Systems
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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.
Directory of Open Access Journals (Sweden)
Rachhpal Singh
2016-08-01
Full Text Available Grid computing incorporates dispersed resources to work out composite technical, industrial, and business troubles. Thus a capable scheduling method is necessary for obtaining the objectives of grid. The disputes of parallel computing are commencing with the computing resources for the number of jobs and intricacy, craving, resource malnourishment, load balancing and efficiency. The risk stumbling upon parallel computing is the enthusiasm to scrutinize different optimization techniques to achieve the tasks without unsafe surroundings. Here Cuckoo Genetic Optimization Algorithm (CGOA is established that was motivated from cuckoo optimization algorithm (COA and genetic algorithm (GA for task scheduling in parallel environment (grid computing system. This CGOA is implemented on parallel dealing out for effective scheduling of multiple tasks with less schedule length and load balance. Here transmission time is evaluated with number of job set. This is computed with the help of job-processor relationship. This technique handles the issues well and the results show that complexity, load balance and resource utilization are finely managed.
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 progr......-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 Results and Numerical Simulations for Flow Shop Scheduling Problems
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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.
Global Optimization of Nonlinear Blend-Scheduling Problems
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Pedro A. Castillo Castillo
2017-04-01
Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.
A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids
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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.
Minimum-Energy Wireless Real-Time Multicast by Joint Network Coding and Scheduling Optimization
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Guoping Tan
2015-01-01
Full Text Available For real-time multicast services over wireless multihop networks, to minimize the energy of transmissions with satisfying the requirements of a fixed data rate and high reliabilities, we construct a conflict graph based framework by joint optimizing network coding and scheduling. Then, we propose a primal-dual subgradient optimization algorithm by random sampling K maximal stable sets in a given conflict graph. This method transforms the NP-hard scheduling subproblem into a normal linear programming problem to obtain an approximate solution. The proposed algorithm only needs to adopt centralized technique for solving the linear programming problem while all of the other computations can be distributed. The simulation results show that, comparing with the existing algorithm, this algorithm can not only achieve about 20% performance gain, but also have better performance in terms of convergence and robustness.
An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks
Penumalli, Chakradhar; Palanichamy, Yogesh
2015-01-01
A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results. PMID:26221627
Scheduling in a random environment: stability and asymptotic optimality
Ayesta, U; Jonckheere, M; Verloop, I M
2011-01-01
We investigate the scheduling of a common resource between several concurrent users when the feasible transmission rate of each user varies randomly over time. Time is slotted and users arrive and depart upon service completion. This may model for example the flow-level behavior of end-users in a narrowband HDR wireless channel (CDMA 1xEV-DO). As performance criteria we consider the stability of the system and the mean delay experienced by the users. Given the complexity of the problem we investigate the fluid-scaled system, which allows to obtain important results and insights for the original system: (1) We characterize for a large class of scheduling policies the stability conditions and identify a set of maximum stable policies, giving in each time slot preference to users being in their best possible channel condition. We find in particular that many opportunistic scheduling policies like Score-Based, Proportionally Best or Potential Improvement are stable under the maximum stability conditions, whereas ...
Scheduling Algorithm to Optimize Jobs in Shop Floor
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T. Hemamalini
2010-01-01
Full Text Available Problem statement: The ratio scheduling algorithm to solve the allocation of jobs in the shop floor was proposed. The problem was to find an optimal schedule so as to minimize the maximum completion time, the sum of distinct earliness and tardiness penalties from a given common due date d. Approach: The objective of the proposed algorithm was to reduce the early penalty and the late penalty and to increase the overall profit of the organization. The proposed method was discussed with different possible instances. Results: The test results showed that the algorithm was robust and simple and can be applied for any job size problem. Conclusion: The proposed algorithm gave encouraging result for the bench mark instances when the due date is less than half of the total processing time.
Task scheduling based on ant colony optimization in cloud environment
Guo, Qiang
2017-04-01
In order to optimize the task scheduling strategy in cloud environment, we propose a cloud computing task scheduling algorithm based on ant colony algorithm. The main goal of this algorithm is to minimize the makespan and the total cost of the tasks, while making the system load more balanced. In this paper, we establish the objective function of the makespan and costs of the tasks, define the load balance function. Meanwhile, we also improve the initialization of the pheromone, the heuristic function and the pheromone update method in the ant colony algorithm. Then, some experiments were carried out on the Cloudsim platform, and the results were compared with algorithms of ACO and Min-Min. The results shows that the algorithm is more efficient than the other two algorithms in makespan, costs and system load balancing.
Flow shop scheduling algorithm to optimize warehouse activities
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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.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
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...
Schedule Optimization Study, Hanford RI/FS Program
Energy Technology Data Exchange (ETDEWEB)
1992-12-01
A Schedule Optimization Study (SOS) of the US Department of Energy (DOE) Hanford Site Remedial Investigation/Feasibility Study (RI/FS) Program was conducted by an independent team of professionals from other federal agencies and the private sector experienced in environmental restoration. This team spent two weeks at Hanford in September 1992 examining the reasons for the lengthy RI/FS process at Hanford and developing recommendations to expedite the process. The need for the study arose out of a schedule dispute regarding the submission of the 1100-EM-1 Operable Unit RI/FS Work Plan. This report documents the study called for in the August 29, 1991, Dispute Resolution Committee Decision Statement. Battelle's Environmental Management Operations (EMO) coordinated the effort for DOE's Richland Field Office (RL).
Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources
Energy Technology Data Exchange (ETDEWEB)
Wu, Hongyu; Pratt, Annabelle; Chakraborty, Sudipta
2015-07-03
This paper proposes a stochastic, multi-objective optimization model within a Model Predictive Control (MPC) framework, to determine the optimal operational schedules of residential appliances operating in the presence of renewable energy source (RES). The objective function minimizes the weighted sum of discomfort, energy cost, total and peak electricity consumption, and carbon footprint. A heuristic method is developed for combining different objective components. The proposed stochastic model utilizes Monte Carlo simulation (MCS) for representing uncertainties in electricity price, outdoor temperature, RES generation, water usage, and non-controllable loads. The proposed model is solved using a mixed integer linear programming (MILP) solver and numerical results show the validity of the model. Case studies show the benefit of using the proposed optimization model.
Institute of Scientific and Technical Information of China (English)
TU Wei; CHAKARESKI Jacob; STEINBACH Eckehard
2006-01-01
We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversational and streaming videos. We consider a scenario where conversational and streaming videos share the forwarding resources at a network node. Two buffers are setup on the node to temporarily store the packets for these two types of video applications. For streaming video, a big buffer is used as the associated delay constraint of the application is moderate and a very small buffer is used for conversational video to ensure that the forwarding delay of every packet is limited. A scheduler is located behind these two buffers that dynamically assigns transmission slots on the outgoing link to the two buffers. Rate-distortion side information is used to perform RD-optimized frame dropping in case of node overload. Sharing the data rate on the outgoing link between the conversational and the streaming videos is done either based on the fullness of the two associated buffers or on the mean incoming rates of the respective videos. Simulation results showed that our proposed RD-optimized frame dropping and scheduling approach provides significant improvements in performance over the popular priority-based random dropping (PRD) technique.
Optimal scheduling of sootblowers in power plant boilers
Vasquez-Urbano, Pedro Manuel
1997-11-01
Burning coal or other fossil fuels in a utility boiler fouls the surfaces of its heat exchangers with ash and soot residues. These deposits affect the performance of the power plant since they reduce heat transfer from the combustion gases to the water or steam. Fouling can be removed during the operation of the plant with the use of lances, called sootblowers, that direct high-pressure air or steam onto the fouled surfaces. Sootblowing operations are key to plant efficiency and boiler maintenance, but they also incur operating costs. A utility boiler may have a hundred or so sootblowers placed in fixed locations. Deciding which of these should be used at any moment is complicated by the lack of instrumentation that can monitor fouling levels. This dissertation studies the optimization problem of scheduling sootblowing activities at a utility plant. The objective is to develop an optimization approach to determine which sootblowers should be activated at any moment in order to maximize plant efficiency. To accomplish this, three issues are addressed. First, models are developed that can estimate fouling conditions indirectly during plant operation using commonly available data. The approach used relies on a sequential application of linear regression fits. Secondly, autoregressive exogenous (ARX) models are used to describe the dynamics of the fouling process and to estimate the consequences of fouling on plant efficiency. All the foregoing empirical models are developed using data from a power plant. Finally, using the empirical models, an optimization model is formulated for the sootblowing scheduling problem and different optimization approaches that combine nonlinear programming with heuristics methods are investigated for its solution. The applicability of dynamic programming to this optimization problem is also explored.
Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks
Directory of Open Access Journals (Sweden)
S. Vukmirović
2012-04-01
Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.
MOOPPS: An Optimization System for Multi Objective Scheduling
Geiger, Martin Josef
2008-01-01
In the current paper, we present an optimization system solving multi objective production scheduling problems (MOOPPS). The identification of Pareto optimal alternatives or at least a close approximation of them is possible by a set of implemented metaheuristics. Necessary control parameters can easily be adjusted by the decision maker as the whole software is fully menu driven. This allows the comparison of different metaheuristic algorithms for the considered problem instances. Results are visualized by a graphical user interface showing the distribution of solutions in outcome space as well as their corresponding Gantt chart representation. The identification of a most preferred solution from the set of efficient solutions is supported by a module based on the aspiration interactive method (AIM). The decision maker successively defines aspiration levels until a single solution is chosen. After successfully competing in the finals in Ronneby, Sweden, the MOOPPS software has been awarded the European Academ...
Throughput optimization for dual collaborative spectrum sensing with dynamic scheduling
Cui, Cuimei; Yang, Dezhi
2017-07-01
Cognitive radio technology is envisaged to alleviate both spectrum inefficiency and spectrum scarcity problems by exploiting the existing licensed spectrum opportunistically. However, cognitive radio ad hoc networks (CRAHNs) impose unique challenges due to the high dynamic scheduling in the available spectrum, diverse quality of service (QOS) requirements, as well as hidden terminals and shadow fading issues in a harsh radio environment. To solve these problems, this paper proposes a dynamic and variable time-division multiple-access scheduling mechanism (DV-TDMA) incorporated with dual collaborative spectrum sensing scheme for CRAHNs. This study involves the cross-layered cooperation between the Physical (PHY) layer and Medium Access Control (MAC) layer under the consideration of average sensing time, sensing accuracy and the average throughput of cognitive radio users (CRs). Moreover, multiple-objective optimization algorithm is proposed to maximize the average throughput of CRs while still meeting QOS requirements on sensing time and detection error. Finally, performance evaluation is conducted through simulations, and the simulation results reveal that this optimization algorithm can significantly improve throughput and sensing accuracy and reduce average sensing time.
A New Distributed Optimization for Community Microgrids Scheduling
Energy Technology Data Exchange (ETDEWEB)
Starke, Michael R [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)
2017-01-01
This paper proposes a distributed optimization model for community microgrids considering the building thermal dynamics and customer comfort preference. The microgrid central controller (MCC) minimizes the total cost of operating the community microgrid, including fuel cost, purchasing cost, battery degradation cost and voluntary load shedding cost based on the customers' consumption, while the building energy management systems (BEMS) minimize their electricity bills as well as the cost associated with customer discomfort due to room temperature deviation from the set point. The BEMSs and the MCC exchange information on energy consumption and prices. When the optimization converges, the distributed generation scheduling, energy storage charging/discharging and customers' consumption as well as the energy prices are determined. In particular, we integrate the detailed thermal dynamic characteristics of buildings into the proposed model. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of proposed model.
Microgrid optimal scheduling considering impact of high penetration wind generation
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.
National Aeronautics and Space Administration — Advanced, robust, autonomous planning systems have not focused on the scheduling decisions made by the planner. And high quality, optimizing schedulers have rarely...
Rolling optimization algorithm based on collision window for single machine scheduling problem
Institute of Scientific and Technical Information of China (English)
Wang Changjun; Xi Yugeng
2005-01-01
Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.
WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules
Energy Technology Data Exchange (ETDEWEB)
Jeong, J; Deasy, J O [Memorial Sloan Kettering Cancer Center, New York, NY (United States)
2014-06-15
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.
Optimal Operation of Energy Storage in Power Transmission and Distribution
Akhavan Hejazi, Seyed Hossein
In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit's charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider
OPTIMIZATION OF WOOD MILLING SCHEDULE – A CASE STUDY
Directory of Open Access Journals (Sweden)
Emilia-Adela SALCA
2015-12-01
Full Text Available The paper presents the results of a case study applied to the milling process of solid wood specimens made of black alder wood (Alnus glutinosa L. Gaertn. with a view to find the optimal cutting schedule when two main criteria, such as the minimum power consumption and the best surface quality are fulfilled.The experimental work was performed with black alder wood originating from mature trees from the Buzau Valley region in Romania. All samples were processed on their longitudinal edges by straight milling with a milling cutter having glued straight plates on the vertical milling machine under different cutting schedules. An electronic device connected to the machine engine and an acquisition board were used to record and compute the power consumption during milling. Roughness measurements of the samples were performed by employing an optical profilometer. All data were processed using the regression method and variance analysis. The study revealed that best results are to be obtained in terms of cutting power and surface quality when processing with low feed speeds and light cutting depths.
Biopharmaceutical Process Optimization with Simulation and Scheduling Tools
Directory of Open Access Journals (Sweden)
Demetri Petrides
2014-09-01
Full Text Available Design and assessment activities associated with a biopharmaceutical process are performed at different levels of detail, based on the stage of development that the product is in. Preliminary “back-of-the envelope” assessments are performed early in the development lifecycle, whereas detailed design and evaluation are performed prior to the construction of a new facility. Both the preliminary and detailed design of integrated biopharmaceutical processes can be greatly assisted by the use of process simulators, discrete event simulators or finite capacity scheduling tools. This report describes the use of such tools for bioprocess development, design, and manufacturing. The report is divided into three sections. Section One provides introductory information and explains the purpose of bioprocess simulation. Section Two focuses on the detailed modeling of a single batch bioprocess that represents the manufacturing of a therapeutic monoclonal antibody (MAb. This type of analysis is typically performed by engineers engaged in the development and optimization of such processes. Section Three focuses on production planning and scheduling models for multiproduct plants.
Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem
Directory of Open Access Journals (Sweden)
Mansour Eddaly
2016-10-01
Full Text Available This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.
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.
Energy-Optimal Scheduling in Low Duty Cycle Sensor Networks
Aydin, Nursen; Ercetin, Ozgur
2011-01-01
Energy consumption of a wireless sensor node mainly depends on the amount of time the node spends in each of the high power active (e.g., transmit, receive) and low power sleep modes. It has been well established that in order to prolong node's lifetime the duty-cycle of the node should be low. However, low power sleep modes usually have low current draw but high energy cost while switching to the active mode with a higher current draw. In this work, we investigate a MaxWeightlike opportunistic sleep-active scheduling algorithm that takes into account time- varying channel and traffic conditions. We show that our algorithm is energy optimal in the sense that the proposed ESS algorithm can achieve an energy consumption which is arbitrarily close to the global minimum solution. Simulation studies are provided to confirm the theoretical results.
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.
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.
Optimizing Flight Schedules by an Automated Decision Support System
2014-03-01
As a result of this, constructing the solution model in a language which is easy to enhance is critical. VBA is a powerful language for easy and quick...Flight Schedule Model ...............................................................................................45 Manual Assignments by...Scheduling Model .............................................................................62 Contribution of scheduling model to air force(s
Directory of Open Access Journals (Sweden)
Hong-an Yang
2014-01-01
Full Text Available We focus on solving Stochastic Job Shop Scheduling Problem (SJSSP with random processing time to minimize the expected sum of earliness and tardiness costs of all jobs. To further enhance the efficiency of the simulation optimization technique of embedding Evolutionary Strategy in Ordinal Optimization (ESOO which is based on Monte Carlo simulation, we embed Optimal Computing Budget Allocation (OCBA technique into the exploration stage of ESOO to optimize the performance evaluation process by controlling the allocation of simulation times. However, while pursuing a good set of schedules, “super individuals,” which can absorb most of the given computation while others hardly get any simulation budget, may emerge according to the allocating equation of OCBA. Consequently, the schedules cannot be evaluated exactly, and thus the probability of correct selection (PCS tends to be low. Therefore, we modify OCBA to balance the computation allocation: (1 set a threshold of simulation times to detect “super individuals” and (2 follow an exclusion mechanism to marginalize them. Finally, the proposed approach is applied to an SJSSP comprising 8 jobs on 8 machines with random processing time in truncated normal, uniform, and exponential distributions, respectively. The results demonstrate that our method outperforms the ESOO method by achieving better solutions.
Parameters optimization for magnetic resonance coupling wireless power transmission.
Li, Changsheng; Zhang, He; Jiang, Xiaohua
2014-01-01
Taking maximum power transmission and power stable transmission as research objectives, optimal design for the wireless power transmission system based on magnetic resonance coupling is carried out in this paper. Firstly, based on the mutual coupling model, mathematical expressions of optimal coupling coefficients for the maximum power transmission target are deduced. Whereafter, methods of enhancing power transmission stability based on parameters optimal design are investigated. It is found that the sensitivity of the load power to the transmission parameters can be reduced and the power transmission stability can be enhanced by improving the system resonance frequency or coupling coefficient between the driving/pick-up coil and the transmission/receiving coil. Experiment results are well conformed to the theoretical analysis conclusions.
Parameters Optimization for Magnetic Resonance Coupling Wireless Power Transmission
Directory of Open Access Journals (Sweden)
Changsheng Li
2014-01-01
Full Text Available Taking maximum power transmission and power stable transmission as research objectives, optimal design for the wireless power transmission system based on magnetic resonance coupling is carried out in this paper. Firstly, based on the mutual coupling model, mathematical expressions of optimal coupling coefficients for the maximum power transmission target are deduced. Whereafter, methods of enhancing power transmission stability based on parameters optimal design are investigated. It is found that the sensitivity of the load power to the transmission parameters can be reduced and the power transmission stability can be enhanced by improving the system resonance frequency or coupling coefficient between the driving/pick-up coil and the transmission/receiving coil. Experiment results are well conformed to the theoretical analysis conclusions.
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
Optimization of transmission system design based on genetic algorithm
Directory of Open Access Journals (Sweden)
Xianbing Chen
2016-05-01
Full Text Available Transmission system is a crucial precision mechanism for twin-screw chemi-mechanical pulping equipment. The structure of the system designed by traditional method is not optimal because the structure designed by the traditional methods is easy to fall into the local optimum. To achieve the global optimum, this article applies the genetic algorithm which has grown in recent years in the field of structure optimization. The article uses the volume of transmission system as the objective function to optimize the structure designed by traditional method. Compared to the simulation results, the original structure is not optimal, and the optimized structure is tighter and more reasonable. Based on the optimized results, the transmission shafts in the transmission system are designed and checked, and the parameters of the twin screw are selected and calculated. The article provided an effective method to design the structure of transmission system.
A data transmission scheduling algorithm for rapid-response earth-observing operations
Directory of Open Access Journals (Sweden)
Li Jun
2014-04-01
Full Text Available With the development of rapid-response Earth-observing techniques, the demand for reducing a requirements-tasking-effects cycle from 1 day to hours grows rapidly. For instance, a satellite user always wants to receive requested data in near real-time to support their urgent missions, such as dealing with wildfires, volcanoes, flooding events, etc. In this paper, we try to reduce data transmission time for achieving this goal. The new feature of a responsive satellite is that users can receive signals from it directly. Therefore, the traditional satellite control and operational techniques need to be improved to accommodate these changes in user needs and technical upgrading. With that in mind, a data transmission topological model is constructed. Based on this model, we can deal with the satellite data transmission problem as a multi-constraint and multi-objective path-scheduling problem. However, there are many optional data transmission paths for each target based on this model, and the shortest path is preferred. In addition, satellites represent scarce resources that must be carefully scheduled in order to satisfy as many consumer requests as possible. To efficiently balance response time and resource utilization, a K-shortest path genetic algorithm is proposed for solving the data transmission problem. Simulations and analysis show the feasibility and the adaptability of the proposed approach.
Bus Access Optimization for Distributed Embedded Systems Based on Schedulability Analysis
DEFF Research Database (Denmark)
Pop, Paul; Eles, Petru; Peng, Zebo
2000-01-01
We present an approach to bus access optimization and schedulability analysis for the synthesis of hard real-time distribution embedded systems. 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 bus access scheme are developed, and the four approaches to message scheduling are compared using extensive experiments....
Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming
2016-01-01
TECHNICAL REPORT NSWC PCD TR 2015-003 OPTIMIZED WATERSPACE MANAGEMENT AND SCHEDULING USING MIXED-INTEGER LINEAR PROGRAMMING...constraints required for the mathematical formulation of the MCM scheduling problem pertaining to the survey constraints and logistics management . The...Floudas, Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications, Oxford University Press, 1995. [10] M. J. Bays, A. Shende, D. J
Optimizing Department of Defense Acquisition Development Test and Evaluation Scheduling
2015-06-01
Technique PMO program management office R&D research and development RCPSP resource-constrained project scheduling problem SME subject matter...Program Management Office ( PMO ) test personnel develop test schedules manually using time estimates and heuristic subject matter expert (SME) advice...on several SMEs and PMO test personnel to develop the initial test schedule for any new acquisition. This is used to establish an initial plan for
Protocol description and optimization scheduling for multi-fieldbus integration system.
Zhong, Chongquan; Chen, Chen
2015-11-01
Device integration technique is applied to integrate different fieldbus devices into one control system. At present mature integration techniques use appropriative software to support corresponding protocols. New software must be developed when a new fieldbus is integrated. In this research, a universal protocol description method is proposed. It focuses on the packets encapsulation description, and different protocol messages can be encapsulated and parsed by the interpreter in a unified way. Moreover, in order to ensure the communication efficiency and QoS of different kinds of messages, packets encapsulated via protocol description are optimized and scheduled before transmission inside the interpreter. The approaches have been applied in the prototype of a software product and verified in a power monitoring project.
IMPROVING FAULT TOLERANT RESOURCE OPTIMIZED AWARE JOB SCHEDULING FOR GRID COMPUTING
Directory of Open Access Journals (Sweden)
K. Nirmala Devi
2014-01-01
Full Text Available Workflow brokers of existing Grid Scheduling Systems are lack of cooperation mechanism which causes inefficient schedules of application distributed resources and it also worsens the utilization of various resources including network bandwidth and computational cycles. Furthermore considering the literature, all of these existing brokering systems primarily evolved around models of centralized hierarchical or client/server. In such models, vital responsibility such as resource discovery is delegated to the centralized server machines, thus they are associated with well-known disadvantages regarding single point of failure, scalability and network congestion at links that are leading to the server. In order to overcome these issues, we implement a new approach for decentralized cooperative workflow scheduling in a dynamically distributed resource sharing environment of Grids. The various actors in the system namely the users who belong to multiple control domains, workflow brokers and resources work together enabling a single cooperative resource sharing environment. But this approach ignored the fact that each grid site may have its own fault-tolerance strategy because each site is itself an autonomous domain. For instance, if a grid site handles the job check-pointing mechanism, each computation node must have the ability of periodical transmission of transient state of the job execution by computational node to the server. When there is a failure of job, it will migrate to another computational node and resume from the last stored checkpoint. A Glow worm Swarm Optimization (GSO for job scheduling is used to address the issue of heterogeneity in fault-tolerance of computational grid but Weighted GSO that overcomes the position update imperfections of general GSO in a more efficient manner shown during comparison analysis. This system supports four kinds of fault-tolerance mechanisms, including the job migration, job retry, check-pointing and
Optimal Fair Scheduling in S-TDMA Sensor Networks for Monitoring River Plumes
Directory of Open Access Journals (Sweden)
Miguel-Angel Luque-Nieto
2016-01-01
Full Text Available Underwater wireless sensor networks (UWSNs are a promising technology to provide oceanographers with environmental data in real time. Suitable network topologies to monitor estuaries are formed by strings coming together to a sink node. This network may be understood as an oriented graph. A number of MAC techniques can be used in UWSNs, but Spatial-TDMA is preferred for fixed networks. In this paper, a scheduling procedure to obtain the optimal fair frame is presented, under ideal conditions of synchronization and transmission errors. The main objective is to find the theoretical maximum throughput by overlapping the transmissions of the nodes while keeping a balanced received data rate from each sensor, regardless of its location in the network. The procedure searches for all cliques of the compatibility matrix of the network graph and solves a Multiple-Vector Bin Packing (MVBP problem. This work addresses the optimization problem and provides analytical and numerical results for both the minimum frame length and the maximum achievable throughput.
WIFI and WIMAX Optimization Design of Transmission and Application
Wang, Peng; Wu, Xian Li
WIFI (Wireless Fidelity) and WIMAX (Worldwide Interoperability for Microwave Access) using OFDM / OFDMA, MIMO technology to achieve speeds in a unified platform for data, voice, high-definition video and other wireless transmission, For many industries of wireless communication may be short, very considerable practical prospect. This paper focuses on WIFI and WIMAX transmission optimization design, including space-time coding (abbreviated as: STC), multiple-access control (MAC) protocol of the mathematical model; traffic channel terminal mobility cross-layer design to achieve multi-layer co-optimization, improve the WIFI and WIMAX, transmission efficiency, making WIFI and WIMAX transmission are more reasonable.
Efficient Frame Schedule Scheme for Real-time Video Transmission Across the Internet Using TCP
Directory of Open Access Journals (Sweden)
Yonghua Xiong
2009-05-01
Full Text Available The great end-to-end delays are the major factor to influence the visual quality of real-time video across the Internet using TCP as transport layer protocol. In this paper, we present a video frame schedule scheme for rate adaptive real-time video transmission over TCP. The scheme schedules video frames between the application layer sender-buffer, the TCP sender-buffer and TCP receiver-buffer and can regulate automaticlly the video frame rate and play out buffer delays according to the network congestion level. The sheme requires only an extra buffer of application layer and can significantly cut down the end-to-end delays of real-time video without any modification to the network infrastructure or TCP protocol stack. The performance of the proposed solution is evaluated through extensive simulations using the NS-2 simulator.
Ant colony optimization approach for test scheduling of system on chip
Institute of Scientific and Technical Information of China (English)
CHEN Ling; PAN Zhong-liang
2009-01-01
It is necessary to perform the test of system on chip, the test scheduling determines the test start and finishing time of every core in the system on chip such that the overall test time is minimized. A new test scheduling approach based on chaotic ant colony algorithm is presented in this paper. The optimization model of test scheduling was studied, the model uses the information such as the scale of test sets of both cores and user defined logic. An approach based on chaotic ant colony algorithm was proposed to solve the optimization model of test scheduling. The test of signal integrity faults such as crosstalk were also investigated when performing the test scheduling. Experimental results on many circuits show that the proposed approach can be used to solve test scheduling problems.
Integration of Optimal Scheduling with Case-Based Planning.
1995-08-01
integrates Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR) systems. ’ Tachyon : A Constraint-Based Temporal Reasoning Model and Its...Implementation’ provides an overview of the Tachyon temporal’s reasoning system and discusses its possible applications. ’Dual-Use Applications of Tachyon : From...Force Structure Modeling to Manufacturing Scheduling’ discusses the application of Tachyon to real world problems, specifically military force deployment and manufacturing scheduling.
Directory of Open Access Journals (Sweden)
V. Sharma
2011-08-01
Full Text Available This study demonstrates the use of a high-performance feedback neural network optimizer based on a new idea of successive approximation for finding the hourly optimal release schedules of interconnected multi-reservoir power system in such a way to minimize the overall cost of thermal generations spanned over the planning period. The main advantages of the proposed neural network optimizer over the existing neural network optimization models are that no dual variables, penalty parameters or lagrange multipliers are required. This network uses a simple structure with the least number of state variables and has better asymptotic stability. For an arbitrarily chosen initial point, the trajectory of the network converges to an optimal solution of the convex nonlinear programming problem. The proposed optimizer has been tested on a nonlinear practical system consisting of a multi-chain cascade of four linked reservoir type hydro-plants and a number of thermal units represented by a single equivalent thermal power plant and so obtained results have been validated using conventional conjugate gradient method and genetic algorithm based approach.
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%.
Optimal scheduling of logistical support for an emergency roadway repair work schedule
Yan, S.; Lin, C. K.; Chen, S. Y.
2012-09-01
The completion of every disaster rescue task performed by repair work teams relies on the in-time supply of materials to the rescue workers. Up to now, logistical support planning for emergency repair work in Taiwan has been done manually, which is neither effective nor efficient. To remedy the problem, this study presents a logistical support scheduling model for the given emergency repair work schedule. The objective is to minimize the short-term operating cost subject to time constraints and other related operating constraints. This model is formulated as an integer multiple-commodity network flow problem which is characterized as NP-hard. A heuristic algorithm, based on the problem decomposition and variable fixing techniques, is also proposed to efficiently solve this problem. Computational tests are performed using data from Taiwan's 1999 Chi-Chi earthquake. The results show that the model and the solution algorithm would be useful for the logistical support scheduling.
Sensitivity Analysis of the Optimal Parameter Settings of an LTE Packet Scheduler
Fernandez Diaz, I.; Litjens, R.; Berg, J.L. van den; Dimitrova, D.C.; Spaey, K.
2010-01-01
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 traff
Institute of Scientific and Technical Information of China (English)
QI Shen-jun; DING Lie-yun; LUO Han-bin; DONG Xiao-yan
2007-01-01
Lean construction has been newly applied to construction industry. The best performance of a project can be achieved through the precise definition of construction product, rational work break structure, lean supply chain, decrease of resources waste, objective control and so forth. Referring to the characteristics of schedule planning of construction projects and lean construction philosophy, we proposed optimizing methodology of real-time and dynamic schedule of construction projects based on lean construction. The basis of the methodology is process reorganization and lean supply in construction enterprises. The traditional estimating method of the activity duration is fuzzy and random; however, a newly proposed lean forecasting method employs multi-components linear-regression, back-propagation artificial neural networks and learning curve. Taking account of the limited resources and the fixed duration of a project, the optimizing method of the real-time and dynamic schedule adopts the concept of resource driving. To optimize the schedule of a construction project timely and effectively, an intellectualized schedule management system was developed. It can work out the initial schedule, optimize the real-time and dynamic schedule, and display the schedule with the Gant Chart, the net-work graph and the space-time line chart. A case study was also presented to explain the proposed method.
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Yang, Yongheng
2017-01-01
The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...
2010-04-01
... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Limits for percentage adders in rates for transmission services; revision of rate schedules, tariffs or service agreements. 35... Filing Requirements § 35.22 Limits for percentage adders in rates for transmission services; revision...
An Integrated Control and Scheduling Optimization Method of Networked Control Systems
Institute of Scientific and Technical Information of China (English)
HE Jian-qiang; ZHANG Huan-chun; JING Ya-zhi
2004-01-01
Feedback control systems wherein the control loops are closed through a real-time network are called networked control systems (NCSs). The limitation of communication bandwidth results in transport delay, affects the property of real-time system, and degrades the performance of NCSs. An integrated control and scheduling optimization method using genetic algorithm is proposed in this paper.This method can synchronously optimize network scheduling and improve the performance of NCSs. To illustrate its effectiveness, an example is provided.
ARQ-Aware Scheduling and Link Adaptation for Video Transmission over Mobile Broadband Networks
Directory of Open Access Journals (Sweden)
Victoria Sgardoni
2012-01-01
Full Text Available This paper studies the effect of ARQ retransmissions on packet error rate, delay, and jitter at the application layer for a real-time video transmission at 1.03 Mbps over a mobile broadband network. The effect of time-correlated channel errors for various Mobile Station (MS velocities is evaluated. In the context of mobile WiMAX, the role of the ARQ Retry Timeout parameter and the maximum number of ARQ retransmissions is taken into account. ARQ-aware and channel-aware scheduling is assumed in order to allocate adequate resources according to the level of packet error rate and the number of ARQ retransmissions required. A novel metric, namely, goodput per frame, is proposed as a measure of transmission efficiency. Results show that to attain quasi error free transmission and low jitter (for real-time video QoS, only QPSK 1/2 can be used at mean channel SNR values between 12 dB and 16 dB, while 16QAM 1/2 can be used below 20 dB at walking speeds. However, these modes are shown to result in low transmission efficiency, attaining, for example, a total goodput of 3 Mbps at an SNR of 14 dB, for a block lifetime of 90 ms. It is shown that ARQ retransmissions are more effective at higher MS speeds.
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...
Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm (GA), and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.
An Optimization Model for Scheduling Problems with Two-Dimensional Spatial Resource Constraint
Garcia, Christopher; Rabadi, Ghaith
2010-01-01
Traditional scheduling problems involve determining temporal assignments for a set of jobs in order to optimize some objective. Some scheduling problems also require the use of limited resources, which adds another dimension of complexity. In this paper we introduce a spatial resource-constrained scheduling problem that can arise in assembly, warehousing, cross-docking, inventory management, and other areas of logistics and supply chain management. This scheduling problem involves a twodimensional rectangular area as a limited resource. Each job, in addition to having temporal requirements, has a width and a height and utilizes a certain amount of space inside the area. We propose an optimization model for scheduling the jobs while respecting all temporal and spatial constraints.
Senthiil, P. V.; Selladurai, V.; Rajesh, R.
This study introduces a new approach for decentralized scheduling in a parallel machine environment based on the ant colonies optimization algorithm. The algorithm extends the use of the traveling salesman problem for scheduling in one single machine, to a multiple machine problem. The results are presented using simple and illustrative examples and show that the algorithm is able to optimize the different scheduling problems. Using the same parameters, the completion time of the tasks is minimized and the processing time of the parallel machines is balanced.
Topology-optimized broadband surface relief transmission grating
DEFF Research Database (Denmark)
Andkjær, Jacob; Ryder, Christian P.; Nielsen, Peter C.
2014-01-01
We propose a design methodology for systematic design of surface relief transmission gratings with optimized diffraction efficiency. The methodology is based on a gradient-based topology optimization formulation along with 2D frequency domain finite element simulations for TE and TM polarized pla......% in a broadband range going from the ultraviolet region, through the visible region and into the near-infrared region....
Directory of Open Access Journals (Sweden)
Ritu Garg
2013-05-01
Full Text Available The problem of scheduling dependent tasks (DAG is an important version of scheduling, to efficiently exploit the computational capabilities of grid systems. The problem of scheduling tasks of a graph onto a set of different machines is an NP Complete problem. As a result, a number of heuristic and meta-heuristic approaches are used over the years due to their ability of providing high quality solutions with reasonable computation time. Discrete Particle Swarm Optimization is one such meta-heuristic used for solving the discrete problem of grid scheduling, but this method converge to sub optimal solutions due to premature convergence. To deal with premature convergence, in this paper we proposed the design and implementation of hierarchical discrete particle swarm optimization (H-DPSO for dependent task scheduling in grid environment. In H-DPSO particles are arranged in dynamic hierarchy where good particles lying above in hierarchy are having larger influence on the swarm. We consider the bi-objective version of problem to minimize makespan and total cost simultaneously as the optimization criteria. The H-DPSO based scheduler was evaluated under different application task graphs. Simulation analysis manifests that H-DPSO based scheduling is highly viable and effective approach for grid computing.
Zhu, Jun; Yan, Xuefeng; Zhao, Weixiang
2013-10-01
To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.
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.
Simulator for Optimization of Software Project Cost and Schedule
Directory of Open Access Journals (Sweden)
P. K. Suri
2008-01-01
Full Text Available Each phase of the software design consumes some resources and hence has cost associated with it. In most of the cases cost will vary to some extent with the amount of time consumed by the design of each phase .The total cost of project, which is aggregate of the activities costs will also depends upon the project duration, can be cut down to some extent. The aim is always to strike a balance between the cost and time and to obtain an optimum software project schedule. An optimum minimum cost project schedule implies lowest possible cost and the associated time for the software project management. In this research an attempt has been made to solve the cost and schedule problem of software project using PERT network showing the details of the activities to be carried out for a software project development/management with the help of crashing, reducing software project duration at a minimum cost by locating a minimal cut in the duration of an activity of the original project design network. This minimal cut is then utilized to identify the project phases which should experience a duration modification in order to achieve the total software duration reduction. Crashing PERT networks can save a significant amount of money in crashing and overrun costs of a company. Even if there are no direct costs in the form of penalties for late completion of projects, there is likely to be intangible costs because of reputation damage.
Transmission network expansion planning with simulation optimization
Energy Technology Data Exchange (ETDEWEB)
Bent, Russell W [Los Alamos National Laboratory; Berscheid, Alan [Los Alamos National Laboratory; Toole, G. Loren [Los Alamos National Laboratory
2010-01-01
Within the electric power literatW''e the transmi ssion expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models. Often, their approaches are tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (i.e. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a generalization of the powerful Limited Discrepancy Search (LDS) that encapsulates the complexity in a black box that may be queJied for information about the quality of a proposed expansion. This allows the development of a new optimization algOlitlun that is independent of the underlying power model.
Directory of Open Access Journals (Sweden)
E.Kayalvizhi
2015-08-01
Full Text Available Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB.
Directory of Open Access Journals (Sweden)
Litian Duan
2016-11-01
Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.
Optimal Power Scheduling for a Grid-Connected Hybrid PV-Wind-Battery Microgrid System
DEFF Research Database (Denmark)
Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi
2016-01-01
In this paper, a lineal mathematical model is proposed to schedule optimally the power references of the distributed energy resources in a grid-connected hybrid PVwind-battery microgrid. The optimization of the short term scheduling problem is addressed through a mixed-integer linear programming...... mathematical model, wherein the cost of energy purchased from the main grid is minimized and profits for selling energy generated by photovoltaic arrays are maximized by considering both physical constraints and requirements for a feasible deployment in the real system. The optimization model is tested...
Analysis of EPONs Under the Static Priority Scheduling Scheme with Fixed Transmission Times
DEFF Research Database (Denmark)
Holmberg, Torgny
2006-01-01
In this paper, we analyse the Ethernet passive optical network (EPON) and formulate dimensioning problems. Under the assumption that the transmission times are fixed in duration and that the input traffic is rate limited and scheduled by the static priority scheme, we provide means for dimensioning...... the network in order to keep the required hard deadlines. Expressions of the frame delay are derived as functions of the arrival curves of the different traffic sources. Furthermore, the arrival regulator structure is general and the only requirements are that the arrival process is causal...... and that the regulator provides sub-additivity. In this study, we find that the examined structure is highly inefficient with poor utilisation and where the deadlines basically is provided by means of overdimensioning. The static structure of the bandwidth allocation scheme allows no adjustment of the current traffic...
Directory of Open Access Journals (Sweden)
Gil Gye-Tae
2010-01-01
Full Text Available Packet scheduler plays the central role in determining the overall performance of the 3GPP long-term evolution (LTE based on packet-switching operation. In this paper, a novel minimum transmit power-based (MP packet-scheduling algorithm is proposed that can achieve power-efficient transmission to the UEs while providing both system throughput gain and fairness improvement. The proposed algorithm is based on a new scheduling metric focusing on the ratio of the transmit power per bit and allocates the physical resource block (PRB to the UE that requires the least ratio of the transmit power per bit. Through computer simulation, the performance of the proposed MP packet-scheduling algorithm is compared with the conventional packet-scheduling algorithms by two primary criteria: fairness and throughput. The simulation results show that the proposed algorithm outperforms the conventional algorithms in terms of the fairness and throughput.
Optimal Operation of Energy Storage in Power Transmission and Distribution
2015-01-01
In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty i...
Energy-Efficient BOP-Based Beacon Transmission Scheduling in Wireless Sensor Networks
Kim, Eui-Jik; Youm, Sungkwan; Choi, Hyo-Hyun
Many applications in wireless sensor networks (WSNs) require the energy efficiency and scalability. Although IEEE 802.15.4/Zigbee which is being considered as general technology for WSNs enables the low duty-cycling with time synchronization of all the nodes in network, it still suffer from its low scalability due to the beacon frame collision. Recently, various algorithms to resolve this problem are proposed. However, their manners to implement are somewhat ambiguous and the degradation of energy/communication efficiency is serious by the additional overhead. This paper describes an Energy-efficient BOP-based Beacon transmission Scheduling (EBBS) algorithm. EBBS is the centralized approach, in which a resource-sufficient node called as Topology Management Center (TMC) allocates the time slots to transmit a beacon frame to the nodes and manages the active/sleep schedules of them. We also propose EBBS with Adaptive BOPL (EBBS-AB), to adjust the duration to transmit beacon frames in every beacon interval, adaptively. Simulation results show that by using the proposed algorithm, the energy efficiency and the throughput of whole network can be significantly improved. EBBS-AB is also more effective for the network performance when the nodes are uniformly deployed on the sensor field rather than the case of random topologies.
A Bee Optimization Algorithm for Scheduling a Job Dynamically in Grid Environment
Directory of Open Access Journals (Sweden)
P. Rajeswari M. Prakash
2011-12-01
Full Text Available Grid computing is based on large scale resources sharing in a widely connected network. Grid scheduling is defined as the process of making scheduling decisions involving allocating jobs to resources over multiple administrative domains. Scheduling is the one of the key issues in the research. Matchmaking is a key aspect in the grid environment. Matching a job with available suitable resources has to satisfy certain constraints. Resource discovery is one of the key issues for job scheduling in the grid environment. The proposed Bee optimization algorithm is to analyze Quality of Service (QoS metrics such as service class, job type in the heterogeneous grid environment. QoS parameters play a major role in selecting grid resources and optimizing resources effectively and efficiently. The output of the proposed algorithm is compared with max-min and min-min algorithm.
Institute of Scientific and Technical Information of China (English)
Hitoshi FURUTA; Ken ISHIBASHI; Koichiro NAKATSU; Shun HOTTA
2008-01-01
The purpose of this research is to propose an early restoration for lifeline systems after earthquake disasters. The previous researches show that the optimization of the restoration schedule by using genetic algorithm (GA) is powerful. However, those are not considering the uncertain environment after earthquake disasters. The circumstances of the damage at devastated areas are very changeable due to the aftershock,fire disaster and bad weather. In addition, the restoring works may delay by unexpected accidents. Therefore,it is necessary to obtain the restoration schedule which has robustness, because the actual restoring works could not progress smoothly under the uncertain environment. GA considering uncertainty (GACU) can treat various uncertainties involved, but it is difficult to obtain the robust schedule. In this study, an attempt is made to develop a decision support system of the optimal restoration scheduling by using the improved GACU.
The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
Li, Jingpeng
2008-01-01
Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each persons assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.
HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler
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.
Directory of Open Access Journals (Sweden)
Shang-Kuan Chen
2016-01-01
Full Text Available In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.
A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem
2010-01-01
A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific p...
Bicriteria Optimization in Wireless Sensor Networks: Link Scheduling and Energy Consumption
Directory of Open Access Journals (Sweden)
Jian Chen
2015-01-01
Full Text Available Link scheduling is important for reliable data communication in wireless sensor networks. Previous works mainly focus on how to find the minimum scheduling length but ignore the impact of energy consumption. In this paper, we integrate them together and solve them by multiobjective genetic algorithms. As a contribution, by jointly modeling the route selection and interference-free link scheduling problem, we give a systematical analysis on the relationship between link scheduling and energy consumption. Considering the specific many-to-one communication nature of WSNs, we propose a novel link scheduling scheme based on NSGA-II (Non-dominated Sorting Genetic Algorithm II. Our approach aims to search the optimal routing tree which satisfies the minimum scheduling length and energy consumption for wireless sensor networks. To achieve this goal, the solution representation based on the routing tree, the genetic operations including tree based recombination and mutation, and the fitness evaluation based on heuristic link scheduling algorithm are well designed. Extensive simulations demonstrate that our algorithm can quickly converge to the Pareto optimal solution between the two performance metrics.
Ordinal scheduling problem and its asymptotically optimal algorithms on parallel machine system
Institute of Scientific and Technical Information of China (English)
TAN Zhiyi; HE Yong
2004-01-01
Focusing on the ordinal scheduling problem on a parallel machine system, we discuss the background of ordinal scheduling and the motivation of ordinal algorithms. In addition, for the ordinal scheduling problem on identical parallel machines with the objective to maximize the minimum machine load, we then give two asymptotically optimal algorithm classes which have worst-case ratios very close to the upper bound of the problem for any given m. These results greatly improve the results proposed by He Yong and Tan Zhiyi in 2002.
Directory of Open Access Journals (Sweden)
Rongxiang Yuan
2016-04-01
Full Text Available In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS. The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs, and responsive loads (RLs, seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.
Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine
Directory of Open Access Journals (Sweden)
Zhang Yu
2013-01-01
Full Text Available By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.
Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine
Zhang Yu; Xiaomei Yang
2013-01-01
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove it...
Full glowworm swarm optimization algorithm for whole-set orders scheduling in single machine.
Yu, Zhang; Yang, Xiaomei
2013-01-01
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.
Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals
Rodríguez Molins, Mario
2015-01-01
Despite the continuous evolution in computers and information technology, real-world combinatorial optimization problems are NP-problems, in particular in the domain of planning and scheduling. Thus, although exact techniques from the Operations Research (OR) field, such as Linear Programming, could be applied to solve optimization problems, they are difficult to apply in real-world scenarios since they usually require too much computational time, i.e: an optimized solution is ...
A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling
Institute of Scientific and Technical Information of China (English)
Jinfeng Wang; Xiaoliang Fan; Chaowei Zhang; Shuting Wan
2014-01-01
This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). General y, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimiza-tion algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.
Stochastic optimization of mine production scheduling with uncertain ore/metal/waste supply
Institute of Scientific and Technical Information of China (English)
Leite Andre; Dimitrakopoulos Roussos
2014-01-01
Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming (SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the pres-ence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29%higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.
Scheduling Internal Audit Activities: A Stochastic Combinatorial Optimization Problem
Rossi, R.; Tarim, S.A.; Hnich, B.; Prestwich, S.; Karacaer, S.
2010-01-01
The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments neithe
Scheduling home-appliances to optimize energy consumption
DEFF Research Database (Denmark)
Rossello Busquet, Ana
In order to optimize the energy consumption, energy demand peaks should be avoided, and energy consumption should be smoothly distributed over time. This can be achieved by setting a maximum energy consumption per user’s household. In other words, the overall consumption of the user’s appliances ...
On using priced timed automata to achieve optimal scheduling
DEFF Research Database (Denmark)
Rasmussen, Jacob Illum; Larsen, Kim Guldstrand; Subramani, K.
2006-01-01
projects VHS (VHS 2005) and AMETIST (AMETIST 2005) and are available in the recently released UPPAAL CORA (UPPAAL CORA 2005), a variant of the real-time verification tool UPPAAL (Larsen, Pettersson, & Yi 1997; Behrmann, David, & Larsen 2004) specialized for cost-optimal reachability for the extended model...
Scheduling Internal Audit Activities: A Stochastic Combinatorial Optimization Problem
Rossi, R.; Tarim, S.A.; Hnich, B.; Prestwich, S.; Karacaer, S.
2010-01-01
The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments
Scheduling home-appliances to optimize energy consumption
DEFF Research Database (Denmark)
Rossello Busquet, Ana
In order to optimize the energy consumption, energy demand peaks should be avoided, and energy consumption should be smoothly distributed over time. This can be achieved by setting a maximum energy consumption per user’s household. In other words, the overall consumption of the user’s appliances...
Optimal Transmission Power in a Nonlinear VLC System
Institute of Scientific and Technical Information of China (English)
ZHAO Shuang; CAI Sunzeng; KANG Kai; QIAN Hua
2016-01-01
In a visible light communication (VLC) system, the light emitting diode (LED) is nonlinear for large signals, which limits the trans⁃mission power or equivalently the coverage of the VLC system. When the input signal amplitude is large, the nonlinear distortion creates harmonic and intermodulation distortion, which degrades the transmission error vector magnitude (EVM). To evaluate the impact of nonlinearity on system performance, the signal to noise and distortion ratio (SNDR) is applied, defined as the linear sig⁃nal power over the thermal noise plus the front end nonlinear distortion. At a given noise level, the optimal system performance can be achieved by maximizing the SNDR, which results in high transmission rate or long transmission range for the VLC system. In this paper, we provide theoretical analysis on the optimization of SNDR with a nonlinear Hammerstein model of LED. Simula⁃tion results and lab experiments validate the theoretical analysis.
Optimal Parallel Algorithms for Two Processor Scheduling with Tree Precedence Constraints
Ernst W. Mayr; Hans Stadtherr
2016-01-01
Consider the problem of finding a minimum length schedule for an unit execution time tasks on m processors with tree-like precedence constraints. A sequential algorithm can solve this problem in linear time. The fastest known parallel algorithm needs O(log n) time using n^2 processors. For the case m=2 we present two work optimal parallel algorithms that produce greedy optimal schedules for intrees and outtrees. Both run in O(log n) time using n/(log n) processors of an EREW PRAM.
Multi-agent Optimization Design for Multi-resource Job Shop Scheduling Problems
Xue, Fan; Fan, Wei
As a practical generalization of the job shop scheduling problem, multi-resource job shop scheduling problem (MRJSSP) is discussed in this paper. In this problem, operations may be processed by a type of resources and jobs have individual deadlines. How to design and optimize this problem with DSAFO, a novel multi-agent algorithm, is introduced in detail by a case study, including problem analysis, agent role specification, and parameter selection. Experimental results show the effectiveness and efficiency of designing and optimizing MRJSSPs with multi-agent.
Optimal cognitive transmission exploiting redundancy in the primary ARQ process
DEFF Research Database (Denmark)
Michelusi, Nicholo; Simeone, Osvaldo; Levorato, Marco
2011-01-01
transmissions to the SU. We investigate secondary transmission policies that take advantage of this redundancy. The basic idea is that, if a Secondary Receiver (SR) learns the Primary Message (PM) in a given primary retransmission, then it can use this knowledge to cancel the primary interference...... in the subsequent slots in case of primary retransmissions, thus achieving a larger secondary throughput. This gives rise to interesting trade-offs in the design of the secondary policy. In fact, on the one hand, a secondary transmission potentially increases the secondary throughput but, on the other, causes...... on the given interference constraint at the PR. It is proved that the optimal secondary strategy prioritizes transmissions in the states where the PM is known to the SR, due to the ability of the latter to perform interference mitigation and obtain a larger secondary throughput. Moreover, when the primary...
Directory of Open Access Journals (Sweden)
Yu Wang
2017-01-01
Full Text Available This article develops a systematic model to study electric vehicle powertrain system efficiency by combining a detailed model of two-speed dual-clutch transmission system efficiency losses with an electric vehicle powertrain system model. In this model, the design factors including selection of the electric machine, gear ratios’ change, multi-plate wet clutch design, and gear shift schedule design are considered. Meanwhile, the application of detailed model for drag torque losses in the gearbox is discussed. Furthermore, the proposed model, developed with the MATLAB/Simulink platform, is applied to optimize/maximize the efficiency of the electric vehicle powertrain system using genetic algorithms. The optimization results demonstrate that the optimal results are different between simulations via New Europe Drive Cycle and Urban Dynamometer Driving Schedule, and comprehensive design and optimization of the powertrain system are necessary.
Inner Random Restart Genetic Algorithm for Practical Delivery Schedule Optimization
Sakurai, Yoshitaka; Takada, Kouhei; Onoyama, Takashi; Tsukamoto, Natsuki; Tsuruta, Setsuo
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires solving several tens to hundreds but less than 2 thousands cities Traveling Salesman Problems (TSP) within interactive response time (less than about 3 second), with expert-level accuracy (less than about 3% of error rate). Further, to make things more difficult, the optimization is subjects to special requirements or preferences of each various delivery sites, persons, or societies. To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) is proposed and developed. This method combines meta-heuristics such as random restart and GA having different types of simple heuristics. Such simple heuristics are 2-opt and NI (Nearest Insertion) methods, each applied for gene operations. The proposed method is hierarchical structured, integrating meta-heuristics and heuristics both of which are multiple but simple. This method is elaborated so that field experts as well as field engineers can easily understand to make the solution or method easily customized and extended according to customers' needs or taste. Comparison based on the experimental results and consideration proved that the method meets the above requirements more than other methods judging from not only optimality but also simplicity, flexibility, and expandability in order for this method to be practically used.
Directory of Open Access Journals (Sweden)
Arias-Londoño Andrés
2014-01-01
Full Text Available One of the major causes for the interruption of power service supply is the contact between vegetation and the power distribution lines. In this paper, two multiobjective mathematical models are proposed to minimize the vegetation negative impact on the electricity network quality, minimizing in turn, the cost of the vegetation pruning. In the first mathematical model, the level of energy not served due the failures from vegetation is minimized and in the second one the average percentage of violation into the safe zone between the vegetation and the overhead power distribution systems is minimized. In both models, the second objective function is to minimize the cost of maintenance of vegetation, considering restrictions associated with equipment availability, reliability in the electrical service and maximum number of prunings on a network segment for the period of vegetation maintenance planning. The scheduling result is pruning activities for a planning period of one year. The elitist non-dominated sorting genetic algorithm (NSGA-II is the multi-objective optimization technique used to solve this problem on a test system.
Model and Solution for Single-group Maintenance Scheduling of Transmission Lines%输电线路单组检修计划模型及求解
Institute of Scientific and Technical Information of China (English)
于宏涛; 高立群; 李丽霞
2012-01-01
Aiming at the difficult problem of making transmission lines maintenance scheduling, a mode! Based on time restrains Travelling Salesman Problem(TSP) for transmission lines maintenance scheduling is presented. Taking account of importance of lines, and all line's maintenance time is in the range of its maintenance time-choice during the search, the target in searching for the best maintenance scheduling is the minimal economic loss that bases on failure rate. The scheduling is made by a novel improved ant colony algorithm, which can improve the ability of escaping from local optimal solution. Results show that the mode! And the algorithm are suitable for solving transmission lines maintenance problem.%为保证电力系统运行的安全性和可靠性,建立一种基于单组维修输电线路检修计划的时间约束旅行商问题模型.考虑线路重要性,同时保证线路检修时段始终控制在可选范围内,以可靠性理论中故障率为基础的经济损失风险最小为目标,设计出一种新的改进蚁群算法对模型进行求解,以便改善基本蚁群算法易于陷入局部最优解的缺点.实验结果表明,应用该算法的模型能够较好地解决输电线路检修计划的制定问题.
OPTIMAL WORK-REST SCHEDULE FOR COMPUTER USERS
Directory of Open Access Journals (Sweden)
Mervat Abdelrahman Mohamed
2017-04-01
Full Text Available Background: Musculoskeletal disorders are the most common health problems for computer users who work for an extended period. The aim of this study was to identify the best work-rest schedule with the three different work-rest groups: no rest break, mid-rest break, and multiple- rest breaks, which was associated with the least EMG activities of the upper trapezius muscle and would be beneficial for musculoskeletal health. Methods: Forty-five right-handed females complaining of neck discomfort were randomly assigned into three equal groups, Group1 (no rest break they were be engaged in sixty minutes of typing followed by ten minutes break (60-10, group 2 (mid-rest break thirty minutes of typing followed by five minutes break (30-5, and group 3 (multiple rest breaks fifteen minutes of typing followed by 2.5 minutes break (15-2.5. Surface EMG was used to pick up the electrical activity of right and left upper trapezius throughout the computer typing task. Results: There was a statistically significant reduction of normalized RMS (p<0.05 between the three groups for both right and left upper trapezius. Also, our results demonstrated a positive effect of mid and multiple rest breaks regarding reduced muscle activity in the upper trapezius muscle during a computer work. Conclusion: There is a positive effect of mid and multiple rest breaks regarding reduced muscle activity in the upper trapezius muscle throughout a computer work in subjects with neck and shoulder discomfort.
Directory of Open Access Journals (Sweden)
Saraiva J. T.
2012-10-01
Full Text Available The basic objective of Transmission Expansion Planning (TEP is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
Power plant maintenance scheduling using ant colony optimization: an improved formulation
Foong, Wai Kuan; Maier, Holger; Simpson, Angus
2008-04-01
It is common practice in the hydropower industry to either shorten the maintenance duration or to postpone maintenance tasks in a hydropower system when there is expected unserved energy based on current water storage levels and forecast storage inflows. It is therefore essential that a maintenance scheduling optimizer can incorporate the options of shortening the maintenance duration and/or deferring maintenance tasks in the search for practical maintenance schedules. In this article, an improved ant colony optimization-power plant maintenance scheduling optimization (ACO-PPMSO) formulation that considers such options in the optimization process is introduced. As a result, both the optimum commencement time and the optimum outage duration are determined for each of the maintenance tasks that need to be scheduled. In addition, a local search strategy is presented in this article to boost the robustness of the algorithm. When tested on a five-station hydropower system problem, the improved formulation is shown to be capable of allowing shortening of maintenance duration in the event of expected demand shortfalls. In addition, the new local search strategy is also shown to have significantly improved the optimization ability of the ACO-PPMSO algorithm.
Optimization based scheduling for a class of production systems with integral constraints
Institute of Scientific and Technical Information of China (English)
GUAN XiaoHong; ZHAI QiaoZhu; FENG YongHan; GAO Feng
2009-01-01
Operation scheduling for a class of production systems with "instantly consumed" products is very important.It is challenging to satisfy the real time system demand and to consider the realizability of the production schedules.This paper formulates a new model for optimization based production scheduling problems with integral constraints.Based on the detailed analysis of the production rate constraints,it is proved that this type of optimization problems is equivalent to a smooth nonlinear programming problem.The reachable upper and lower bounds of the production amount in every period can be expressed as functions of two variables,i.e.,the production rate at the start and end of that period.It is also proved that the gradients of these functions are monotonic,and their convexity or concavity is guaranteed.When the production cost function is convex,this type of optimization problems is equivalent to a convex programming problem.With the above analysis,a two-stage solution method is developed to solve the production scheduling problems with integral constraints,and in many applications the global optimal solution can be obtained efficiently.With the new model and solution method,the difficulties caused by the constraints on production rate can be overcome and the optimal schedule can be obtained with the real time system demand satisfied.Numerical testing for scheduling of electric power production systems is performed and the testing results are discussed.It is demonstrated that the new model and solution method are effective.
Optimization based scheduling for a class of production systems with integral constraints
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Operation scheduling for a class of production systems with"instantly consumed"products is very important.It is challenging to satisfy the real time system demand and to consider the realizability of the production schedules.This paper formulates a new model for optimization based production scheduling problems with integral constraints.Based on the detailed analysis of the production rate constraints,it is proved that this type of optimization problems is equivalent to a smooth nonlinear programming problem.The reachable upper and lower bounds of the production amount in every period can be expressed as functions of two variables,i.e.,the production rate at the start and end of that period.It is also proved that the gradients of these functions are monotonic,and their convexity or concavity is guaranteed.When the production cost function is convex,this type of optimization problems is equivalent to a convex programming problem.With the above analysis,a two-stage solution method is developed to solve the production scheduling problems with integral constraints,and in many applications the global optimal solution can be obtained efficiently.With the new model and solution method,the difficulties caused by the constraints on production rate can be overcome and the optimal schedule can be obtained with the real time system demand satisfied.Numerical testing for scheduling of electric power production systems is performed and the testing results are discussed.It is demonstrated that the new model and solution method are effective.
A two-phase tabu search approach to scheduling optimization in container terminals
Institute of Scientific and Technical Information of China (English)
ZENG Qing-cheng; YANG Zhong-zhen
2007-01-01
An optimization model for scheduling of quay cranes (QCs) and yard trailers was proposed to improve the overall efficiency of container terminals. To implement this model, a two-phase tabu search algorithra was designed. In the QCs scheduling phase of the algorithm, a search was performed to determine a good QC unloading operation order. For each QC unloading operation order generated during the QC's scheduling phase, another search was run to obtain a good yard trailer routing for the given QC's unloading order. Using this information, the time required for the operation was estimated,then the time of return to availability of the units was fed back to the QC scheduler. Numerical tests show that the two-phase Tabu Search algorithm searches the solution space efficiently, decreases the empty distance yard trailers must travel, decreases the number of trailers needed, and thereby reduces time and costs and improves the integration and reliability of container terminal operation systems.
Zhang, Cong; Xiong, Zhihua; Ye, Hao
2014-07-01
In system identification, a data set needs to be informative to guarantee that the identification criterion has a unique global minimum asymptotically and the parameter estimation is consistent. In this paper, we study the informativity of the data set in a multiple-input and multiple-output (MIMO) networked control system (NCS), which contains possible network-induced delays, packet dropout, transmission scheduling, or a combination of these factors in network transmission. Moreover, to guarantee the data set of this MIMO NCS to be informative, a group of conditions for network transmission and controller's proportional term are developed. Finally, simulation studies are given to illustrate the result.
Optimization of Field Development Scheduling, East Unity Oil Field, Sudan
Directory of Open Access Journals (Sweden)
Tagwa A. Musa
2005-01-01
Full Text Available In order to improve the reservoir performance in East Unity oil field Sudan, the studies focused on characterization, modeling and simulation of the actual performance and future development. A model was constructed using a three-phase, three dimensional, black oil simulator (ECLIPSE. In this study a data from East Unity oil field Sudan started production at July 1999 was used to perform the optimal oil rate and designing the best location of the new operating wells. Cumulative oil production, oil production rate, Water cut and recovery factor were used as key criteria to see if adding new wells in the area under study are economic risk.
An Automated Tool for Optimization of FMS Scheduling With Meta Heuristic Approach
Directory of Open Access Journals (Sweden)
A. V. S. Sreedhar Kumar
2014-03-01
Full Text Available The evolutions of manufacturing systems have reflected the need and requirement of the market which varies from time to time. Flexible manufacturing systems have contributed a lot to the development of efficient manufacturing process and production of variety of customized limited volume products as per the market demand based on customer needs. Scheduling of FMS is a crucial operation in maximizing throughput, reducing the wastages and increasing the overall efficiency of the manufacturing process. The dynamic nature of the Flexible Manufacturing Systems makes them unique and hence a generalized solution for scheduling is difficult to be abstracted. Any Solution for optimizing the scheduling should take in to account a multitude of parameters before proposing any solution. The primary objective of the proposed research is to design a tool to automate the optimization of scheduling process by searching for solution in the search spaces using Meta heuristic approaches. The research also validates the use of reward as means for optimizing the scheduling by including it as one of the parameters in the Combined Objective Function.
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.
Institute of Scientific and Technical Information of China (English)
陈义保; 姚建初; 钟毅芳
2002-01-01
Identical parallel machine scheduling problem for minimizing the makespan is a very important productionscheduling problem. When its scale is large, many difficulties will arise in the course of solving identical parallel machinescheduling problem. Ant system based optimization algorithm (ASBOA) has shown great advantages in solving thecombinatorial optimization problem in view of its characteristics of high efficiency and suitability for practical applications.In this paper, an ASBOA for minimizing the makespan in identical machine scheduling problem is presented. Twodifferent scale numerical examples demonstrate that the ASBOA proposed is efficient and fit for large-scale identicalparallel machine scheduling problem for minimizing the makespan, the quality of its solution has advantages over heuristicprocedure and simulated annealing method, as well as genetic algorithm.
Optimal trajectory planning and train scheduling for urban rail transit systems
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...
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.
Institute of Scientific and Technical Information of China (English)
ZHAO Peng; MU Xin; YAO Jin-hua; WANG Yong; YANG Xiu-tai
2007-01-01
We established an integrated and optimized model of vehicle scheduling problem and vehicle filling problem for solving an extremely complex delivery mode-multi-type vehicles, non-full loads, pickup and delivery in logistics and delivery system. The integrated and optimized model is based on our previous research result-effective space method. An integrated algorithm suitable for the integrated and optimized model was proposed and corresponding computer programs were designed to solve practical problems. The results indicates the programs can work out optimized delivery routes and concrete loading projects. The model and algorithm have many virtues and are valuable in practice.
Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
Directory of Open Access Journals (Sweden)
Shuang Zhao
2015-01-01
Full Text Available Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.
Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems
Directory of Open Access Journals (Sweden)
Z. Ismail
2009-01-01
Full Text Available Problem statement: Southern Waste Management environment (SWM environment is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations. The company is implementing an integrated solid waste management system where it involved in the optimization of resources to ensure the effectiveness of its services. Formulating this real life problem into vehicle routing problem with stochastic demand model and using some designed algorithms to minimize operation cost of solid waste management. Approach: The implementation of Ant Colony Optimization (ACO for solving solid waste collection problem as a VRPSD model was described. A set of data modified from the well known 50 customers problems were used to find the route such that the expected traveling cost was minimized. The total cost was minimized by adopting a preventive restocking policy which was trading off the extra cost of returning to depot after a stock-out with the cost of returning depot for restocking before a stock-out actually occurs. For comparison purposes, Simulated Annealing (SA was used to generate the solution under the same condition. Results: For the problem size with 12 customers with vehicle capacity 10 units, both algorithms obtained the same best cost which is 69.4358 units. But the percentage deviations of averages from the associated best cost are 0.1322 and 0.7064 for ACS and SA. The results indicated that for all demand ranges, proposed ACO algorithm showed better performance than SA algorithm. Conclusion: SA was able to obtain good solutions for small ranges especially small size of problem. For ACS, it is always provide good results for all tested ranges and problems sizes of the tested problem.
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 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...
Robust Optimization-Based Generation Self-Scheduling under Uncertain Price
Directory of Open Access Journals (Sweden)
Xiao Luo
2011-01-01
Full Text Available This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.
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...
2010-01-01
scheduling problems at the oper- ational level fall in the broad category of the well- known vehicle routing problem ( VRP ), which entails designing the optimal...motivated by its practical relevance and by its con- siderable difficulty (Toth and Vigo 2002). GFSMP has requirements not typically found in a standard VRP
Optimal Scheme for Search State Space and Scheduling on Multiprocessor Systems
Youness, Hassan A.; Sakanushi, Keishi; Takeuchi, Yoshinori; Salem, Ashraf; Wahdan, Abdel-Moneim; Imai, Masaharu
A scheduling algorithm aims to minimize the overall execution time of the program by properly allocating and arranging the execution order of the tasks on the core processors such that the precedence constraints among the tasks are preserved. In this paper, we present a new scheduling algorithm by using geometry analysis of the Task Precedence Graph (TPG) based on A* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity and pruning techniques to produce an optimal solution for the allocation/scheduling problem of a parallel application to parallel and multiprocessor architecture. The main goal of this work is to significantly reduce the search space and achieve the optimality or near optimal solution. We implemented the algorithm on general task graph problems that are processed on most of related search work and obtain the optimal scheduling with a small number of states. The proposed algorithm reduced the exhaustive search by at least 50% of search space. The viability and potential of the proposed algorithm is demonstrated by an illustrative example.
Directory of Open Access Journals (Sweden)
Mr. Kaustubh N. Kalaspurkar
2013-05-01
Full Text Available Increased competition in the market, ever increasing demands of products and delivery of the quality product within committed dates forcing the manufacturers to involve newer and more optimized techniques in their production scheduling. This technique either involves costly Automation and Flexible Manufacturing System (FMS or the techniques forcing on elimination of unnecessary and unproductive operation (i.e. motion during the production. An assembly line is designed by determining the sequences of operations for manufacture of each component as well as the final product. In this paper, a case study at one of leading tractor manufacturing company in India for one of its production operation i.e. assembly of Rear Axle Carrier (Transmission System of tractor is presented using the technique of time and motion study. For this technique such as Predetermined Motion Time Study (PMTS, Method Time Measurement (MTM, various process charts are used for analysis and optimize their present methodology of assembling Rear Axle Carrier (RAC.
Optimization-based sale transactions and hydrothermal scheduling
Energy Technology Data Exchange (ETDEWEB)
Prasannan, B.; Luh, P.B. [Univ. of Connecticut, Storrs, CT (United States). Dept. of Electrical and Systems Engineering; Yan, H. [Southern California Edison Co., Alhambra, CA (United States). Systems Operation Div.; Palmberg, J.A. [Northeast Utilities Service, Berlin, CT (United States); Zhang, L. [Advanced Control Systems, Norcross, GA (United States)
1995-12-31
Selling and purchasing power are important activities for utilities because of potential savings. When a selling utility presents an offer including prices, power levels and durations, a purchasing utility selects power levels and durations within the offered range subject to relevant constraints. The decisionmaking process is complicated because transactions are coupled with system demand and reserve, therefore decisions have to be made in conjunction with the commitment and dispatching of units. Furthermore, transaction decisions have to be made in almost real time in view of the competitiveness of the power market caused by deregulation. In this paper, transactions are analyzed from a selling utility`s viewpoint for a system consisting of thermal, hydro and pumped-storage units. To effectively solve the problem, linear sale revenues are approximated by nonlinear functions, and non-profitable options are identified and eliminated from consideration. The multipliers are then updated at the high level by using a modified subgradient method to obtain near optimal solutions quickly. Testing results show that the algorithm produces good sale offers efficiently.
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.
Dynamic security risk assessment and optimization of power transmission system
Institute of Scientific and Technical Information of China (English)
2008-01-01
The paper presents a practical dynamic security region (PDSR) based dynamic security risk assessment and optimization model for power transmission system. The cost of comprehensive security control and the influence of uncertainties of power injections are considered in the model of dynamic security risk assessment. The transient stability constraints and uncertainties of power injections can be considered easily by PDSR in form of hyper-box. A method to define and classify contingency set is presented, and a risk control optimization model is given which takes total dynamic insecurity risk as the objective function for a dominant con-tingency set. An optimal solution of dynamic insecurity risk is obtained by opti-mizing preventive and emergency control cost and contingency set decomposition. The effectiveness of this model has been proved by test results on the New Eng-land 10-genarator 39-bus system.
An Optimal Scheduling Algorithm for Real Time Applications in Grid System
Directory of Open Access Journals (Sweden)
S.Baghavathi Priya
2013-01-01
Full Text Available The objective of the proposed work is to use an optimal scheduling algorithm for real-time application. A grid is considered to be an infrastructure that bonds and unifies globally remote and diverse resources in order to provide computing support for a wide range of applications. Real time applications in an industrialized technological infrastructure such as telecommunication systems, factories, defense systems, aircraft and space stations pose relatively rigid requirements on their performance. Aircraft scheduling represents the best example of real-time applications. The main focus of this work is to check the time taken for turn-around activities which comprises of taxi in, load/unload baggage, deboarding, water fueling, cleaning, catering, boarding, de-icing, take off processes, thus relating in the lowest flight delays and shortest waiting time. The optimal scheduling algorithm is used for aircraft take-offs. The penalties are associated with proper scheduling but delayed turn around activities, improper scheduling and early/late takeoffs.
Directory of Open Access Journals (Sweden)
R.Muthu Selvi
2015-10-01
Full Text Available This paper presents a new approach to optimize the schedule of the variable length multimedia packets in Ad hoc networks using hybridized Genetic Algorithm (HGA.Existing algorithm called Virtual Deadline Scheduling (VDS attempts to guarantee m out of k job instances (consecutive packets in a real-time stream by their deadlines are serviced. VDS is capable of generating a feasible window constrained schedule that utilizes 100% of resources. However, when VDS either services a packet or switches to a new request period, it must update the corresponding virtual deadline. Updating the service constraints is a bottleneck for the algorithm which increases the time complexity. HGA overcomes the problem of updating the service constraints that leads to the increased time complexity. The packet length and the number of packets to be serviced are the two conflicting criteria which are affecting the throughput of scheduling. Using HGA, a trade off can be achieved between the packet length and the number of packets to be serviced. HGA produces an optimized schedule for the multimedia packets. Journals.
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
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Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
The nurse scheduling problem: a goal programming and nonlinear optimization approaches
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.
Intermodal Energy Transfer in a Tapered Optical Fiber: Optimizing Transmission
Ravets, S; Kordell, P R; Wong-Campos, J D; Rolston, S L; Orozco, L A
2013-01-01
We present an experimental and theoretical study of the energy transfer between modes during the tapering process of an optical nanofiber through spectrogram analysis. The results allow optimization of the tapering process, and we measure transmission in excess of 99.95% for the fundamental mode. We quantify the adiabaticity condition through calculations and place an upper bound on the amount of energy transferred to other modes at each step of the tapering, giving practical limits to the tapering angle.
Efficient algorithms for optimal arrival scheduling and air traffic flow management
Saraf, Aditya
The research presented in this dissertation is motivated by the need for new, efficient algorithms for the solution of two important problems currently faced by the air-traffic control community: (i) optimal scheduling of aircraft arrivals at congested airports, and (ii) optimal National Airspace System (NAS) wide traffic flow management. In the first part of this dissertation, we present an optimal airport arrival scheduling algorithm, which works within a hierarchical scheduling structure. This structure consists of schedulers at multiple points along the arrival-route. Schedulers are linked through acceptance-rate constraints, which are passed up from downstream metering-points. The innovation in this scheduling algorithm is that these constraints are computed by using an Eulerian model-based optimization scheme. This rate computation removes inefficiencies introduced in the schedule through ad hoc acceptance-rate computations. The scheduling process at every metering-point uses its optimal acceptance-rate as a constraint and computes optimal arrival sequences by using a combinatorial search-algorithm. We test this algorithm in a dynamic air-traffic environment, which can be customized to emulate different arrival scenarios. In the second part of this dissertation, we introduce a novel two-level control system for optimal traffic-flow management. The outer-level control module of this two-level control system generates an Eulerian-model of the NAS by aggregating aircraft into interconnected control-volumes. Using this Eulerian model of the airspace, control strategies like Model Predictive Control are applied to find the optimal inflow and outflow commands for each control-volume so that efficient flows are achieved in the NAS. Each control-volume has its separate inner-level control-module. The inner-level control-module takes in the optimal inflow and outflow commands generated by the outer control-module as reference inputs and uses hybrid aircraft models to
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
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.
Configuration, Deployment, and Scheduling Models for Management and Optimization of Patrol Services
Directory of Open Access Journals (Sweden)
Bin Yang
2015-01-01
Full Text Available This paper presents a decision support system (DSS and its models for patrol service center (PSC. PSC plays an important role in public security and emergency management. The configuration, deployment, and scheduling of resources of PSC are important for improving the efficiency of patrol-related resources, service quantity, and emergency response capability. A series of decision-making models of the DSS are studied. First, the criteria and models are proposed for configuring and deploying PSCs; second, three types of models for incremental, direct, and redeployment optimization are built in views for decisions aiming at PSC configuration, deployment, and scheduling problems; third, considering three typical patrol-related service scenarios (alarm assignment, main road blockade, and besiege program, three scheduling models are built, respectively, for PSC-related service and coordination of multiple PSCs. This work contributes to the literature on patrol services and network optimization problems in the following aspects: based on a series of models, a DSS framework is designed for PSCs; the models are formulated for resource management and scheduling upon geography information system; coordination strategies among close PSCs are incorporated into decision models. These features are examined in integration manners. The assessment criteria and optimization models studied in the paper are beneficial for building DSSs for PSC.
Wrapper/TAM Co-Optimization and constrained Test Scheduling for SOCs Using Rectangle Bin Packing
Babu, Hafiz Md Hasan; Karim, Muhammad Rezaul; Mahmud, Abdullah Al; Islam, Md Saiful
2010-01-01
This paper describes an integrated framework for SOC test automation. This framework is based on a new approach for Wrapper/TAM co-optimization based on rectangle packing considering the diagonal length of the rectangles to emphasize on both TAM widths required by a core and its corresponding testing time .In this paper, an efficient algorithm has been proposed to construct wrappers that reduce testing time for cores. Rectangle packing has been used to develop an integrated scheduling algorithm that incorporates power constraints in the test schedule. The test power consumption is important to consider since exceeding the system's power limit might damage the system.
Institute of Scientific and Technical Information of China (English)
韩帮军; 潘军; 范秀敏; 马登哲
2004-01-01
The recursion relation of preventive maintenance (PM) cycle is built up concerning the concept of effective age and age setback factor proposed in this paper, which illustrates the dynamic relationship between failure rate and preventive maintenance activity. And the nonlinear optimal PM policy model satisfying the reliability constraints in finite time horizon following Weibull distribution is proposed. The model built in this paper avoids the shortcoming of steady analytical PM model in infinite time horizon and can be used to aid scheduling the maintenance plan and providing decision supporting for job shop scheduling.
Directory of Open Access Journals (Sweden)
Xiang Yan
2013-01-01
Full Text Available This paper addresses the optimal bandwidth scheduling problem for a double-layer networked learning control system (NLCS. To deal with this issue, auction mechanism is employed, and a dynamic bandwidth scheduling methodology is proposed to allocate the bandwidth for each subsystem. A noncooperative game fairness model is formulated, and the utility function of subsystems is designed. Under this framework, estimation of distribution algorithm (EDA is used to obtain Nash equilibrium for NLCS. Finally, simulation and experimental results are given to demonstrate the effectiveness of the proposed approach.
Optimization of Time Structures in Manufacturing Management by using Scheduling Software Lekin
Directory of Open Access Journals (Sweden)
Michal Balog
2016-08-01
Full Text Available In each manufacturing plant it is one of the basic requirements to produce the largest quantity of products in the shortest time and at the lowest price. In performance of these requirements used are diverse modern methods, technologies and software which ensure the efficiency improvement of manufacturing, costs minimization, production time minimization etc. Presented article is focused on time structures optimization of real manufacturing process of engineering component by using scheduling software Lekin. It is based on theoretical scientific knowledge on which is afterwards found the optimal layout of the manufacturing process in practice. The optimal layout it created by construction and analysis of Gantt charts in scheduling software Lekin with minimum production time condition.
Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints
Jaramillo, Juan Jose; Ying, Lei
2010-01-01
This paper studies the problem of scheduling in single-hop wireless networks with real-time traffic, where every packet arrival has an associated deadline and a minimum fraction of packets must be transmitted before the end of the deadline. Using optimization and stochastic network theory we propose a framework to model the quality of service (QoS) requirements under delay constraints. The model allows for fairly general arrival models with heterogeneous constraints. The framework results in an optimal scheduling algorithm which fairly allocates data rates to all flows while meeting long-term delay demands. We also prove that under a simplified scenario our solution translates into a greedy strategy that makes optimal decisions with low complexity.
Short term hydrothermal scheduling via improved honey-bee mating optimization algorithm
Directory of Open Access Journals (Sweden)
hamed baradaran tavakoli
2012-11-01
Full Text Available In this paper, a new approach for solving short term hydrothermal scheduling problem is suggested, to minimize the total production cost and to produce electrical energy in an optimized way, by using honey-bee mating optimization algorithm. In the proposed method, lots of the hydrothermal system constraints such as power balance, water balance, time delay between reservoirs, volume limits and the operation limits of hydro and thermal plants, are considered. Therefore, the problem of short term hydrothermal scheduling becomes a complicated and nonlinear problem. In this paper, in addition to implementing the honey-bee mating optimization on a sample system, the improved honey-bee mating optimization algorithm has also been tested and analyzed. With regard to the simulation results, it is apparent that the improved honey-bee mating optimization has far higher convergence speed and takes less time, and less total cost in comparison with honey-bee mating optimization algorithm, genetic algorithm, particle swarm optimization algorithm and other optimization methods.
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...
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.
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
In this paper, we investigate the problem of scheduling a 6 DOF robotic arm to carry out a sequence of spray painting tasks. The duration of any given painting task is process dependent and fixed, but the duration of an “intertask”, corresponding to the process of relocating and reorienting...... the robot arm from one painting task to the next one, is influenced by the order of tasks and must be minimized by the scheduler. There are multiple solutions for reaching any given painting task and tasks can be performed in either of two different directions. Further complicating the problem...... 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...
Fuzzy Optimization of Construction Engineering Project Schedule Based on Critical Chain Management
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Jianbing LIU
2013-07-01
Full Text Available Critical chain management was the very important theory innovation in the development of construction project schedule management field in recent years. Compared with the traditional methods of construction project schedule management, the methods of critical chain management considered resource constraints into construction project schedule management, the construction project cycle was shortened and the efficiency was enhanced by using the dynamic thinking and circulation pattern to manage whole project. In view of the progress of critical chain management of the article, fuzzy theory was used to calculate and optimize the buffer zone sizes with the determination of the buffer size of the existing resources in certain critical chain management so as to shorten the cycle..
Institute of Scientific and Technical Information of China (English)
Aijun Liu; Michele Pfund; John Fowler
2016-01-01
How to deal with the colaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the sub-tasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finaly, the appli-cation feasibility of the model and algorithm is ultimately vali-dated through an application case study.
Joint Optimization in UMTS-Based Video Transmission
Directory of Open Access Journals (Sweden)
Attila Zsiros
2007-01-01
Full Text Available A software platform is exposed, which was developed to enable demonstration and capacity testing. The platform simulates a joint optimized wireless video transmission. The development succeeded within the frame of the IST-PHOENIX project and is based on the system optimization model of the project. One of the constitutive parts of the model, the wireless network segment, is changed to a detailed, standard UTRA network simulation module. This paper consists of (1 a brief description of the projects simulation chain, (2 brief description of the UTRAN system, and (3 the integration of the two segments. The role of the UTRAN part in the joint optimization is described, with the configuration and control of this element. Finally, some simulation results are shown. In the conclusion, we show how our simulation results translate into real-world performance gains.
Optimally tuned vibration absorbers to control sound transmission
Grissom, Michael; Belegundu, Ashok; Koopmann, Gary
2002-05-01
A design optimization method is proposed for controlling broadband vibration of a structure and it concomitant acoustic radiation using multiple-tuned absorbers. A computationally efficient model of a structure is developed and coupled with a nonlinear optimization search algorithm. The eigenvectors of the original structure are used as repeated basis functions in the analysis of the structural dynamic re-analysis problem. The re-analysis time for acoustic power computations is reduced by calculating and storing modal radiation resistance matrices at discrete frequencies. The matrices are then interpolated within the optimization loop for eigenvalues that fall between stored frequencies. The method is demonstrated by applying multiple-tuned vibration absorbers to an acoustically-excited composite panel. The absorber parameters are optimized with an objective of maximizing the panel's sound power transmission loss. It is shown that in some cases the optimal solution includes vibration absorbers that are tuned very closely in frequency, thus acting effectively as a broadband vibration absorber (BBVA). The numerical model and design optimization method are validated experimentally, and the BBVA is found to be an effective noise abatement tool.
Directory of Open Access Journals (Sweden)
Deepika Saxena
2016-02-01
Full Text Available Cloud computing has become buzzword today. It is a digital service where dynamically scalable and virtualized resources are provided as a service over internet. Task scheduling is premier research topic in cloud computing. It is always a challenging task to map variety of complex task on various available heterogenous resources in scalable and efficient way. The very objective of this paper is to dynamically optimize task scheduling at system level as well as user level. This paper relates benefit-fairness algorithm based on weighted-fair Queuing model which is much more efficient than simple priority queuing. In proposed algorithm, we have classified and grouped all tasks as deadline based and minimum cost based constraints and after dynamic optimization, priority of fairness is applied. Here different priority queue (high, mid, low are implemented in round-robin fashion as per weights assign to them .We recompile the CloudSim and simulate the proposed algorithm and results of this algorithm is compared with sequential task scheduling and simple constraints (cost and deadline based task scheduling algorithm. The experimental results indicates that proposed algorithm is, not only beneficial to user and service provider, but also provides better efficiency and fairness at priority level, i.e. benefit at system level.
Directory of Open Access Journals (Sweden)
R.Muthu Selvi
2011-09-01
Full Text Available This paper presents a new approach to optimize the schedule of the variable length multimedia packets inAd hoc networks using hybridized Genetic Algorithm (HGA.Existing algorithm called Virtual DeadlineScheduling (VDS attempts to guarantee m out of k job instances (consecutive packets in a real-timestream by their deadlines are serviced. VDS is capable of generating a feasible window constrainedschedule that utilizes 100% of resources. However, when VDS either services a packet or switches to a newrequest period, it must update the corresponding virtual deadline. Updating the service constraints is abottleneck for the algorithm which increases the time complexity. HGA overcomes the problem of updatingthe service constraints that leads to the increased time complexity. The packet length and the number ofpackets to be serviced are the two conflicting criteria which are affecting the throughput of scheduling.Using HGA, a trade off can be achieved between the packet length and the number of packets to beserviced. HGA produces an optimized schedule for the multimedia packets. Journals
Optimal control of diarrhea transmission in a flood evacuation zone
Erwina, N.; Aldila, D.; Soewono, E.
2014-03-01
Evacuation of residents and diarrhea disease outbreak in evacuation zone have become serious problem that frequently happened during flood periods. Limited clean water supply and infrastructure in evacuation zone contribute to a critical spread of diarrhea. Transmission of diarrhea disease can be reduced by controlling clean water supply and treating diarrhea patients properly. These treatments require significant amount of budget, which may not be fulfilled in the fields. In his paper, transmission of diarrhea disease in evacuation zone using SIRS model is presented as control optimum problem with clean water supply and rate of treated patients as input controls. Existence and stability of equilibrium points and sensitivity analysis are investigated analytically for constant input controls. Optimum clean water supply and rate of treatment are found using optimum control technique. Optimal results for transmission of diarrhea and the corresponding controls during the period of observation are simulated numerically. The optimum result shows that transmission of diarrhea disease can be controlled with proper combination of water supply and rate of treatment within allowable budget.
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.
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)
Directory of Open Access Journals (Sweden)
Hang Zhou
2015-01-01
Full Text Available Reasonable airport runway scheduling is an effective measure to alleviate air traffic congestion. This paper proposes a new model and algorithm for flight scheduling. Considering the factors such as operating conditions and flight safety interval, the runway throughput, flight delays cost, and controller workload composes a multiobjective optimization model. The genetic algorithm combined with sliding time window algorithm is used to solve the model proposed in this paper. Simulation results show that the algorithm presented in this paper gets the optimal results, the runway throughput is increased by 12.87%, the delay cost is reduced by 61.46%, and the controller workload is also significantly reduced compared with FCFS (first come first served. Meanwhile, compared with the general genetic algorithm, it also reduces the time complexity and improves real-time and work efficiency significantly. The analysis results can provide guidance for air traffic controllers to make better air traffic control.
Optimal Preemptive Online Algorithms for Scheduling with Known Largest Size on Two Uniform Machines
Institute of Scientific and Technical Information of China (English)
Yong HE; Yi Wei JIANG; Hao ZHOU
2007-01-01
In this paper, we consider the semi-online preemptive scheduling problem with known largest job sizes on two uniform machines. Our goal is to maximize the continuous period of time (starting from time zero) when both machines are busy, which is equivalent to maximizing the minimummachine completion time if idle time is not introduced. We design optimal deterministic semi-onlinealgorithms for every machine speed ratio s ∈ [1, ∞), and show that idle time is required to achieve the optimality during the assignment procedure of the algorithm for any s (s2 + 3s + 1)/(s2 + 2s + 1).The competitive ratio of the algorithms is (s2 + 3s + 1)/(s2 + 2s + 1), which matches the randomized lower bound for every s ≥ 1. Hence randomization does not help for the discussed preemptive scheduling problem.
Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju
2014-04-01
Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.
Optimization and Operation Scheduling for a Steel Plate yard Based on Greedy Algorithm
Directory of Open Access Journals (Sweden)
Zhiying Zhang
2013-07-01
Full Text Available The inbound and outbound operation of plate yards in shipyards lacks effective scheduling with high operation costs. Based on the analysis of steel-in and steel-out operation process, an optimization model aiming to minimize the operation cost was established. The model was formulated as a multi-level combinatorial optimization model, which is finding proper storage locations during the steel-in stage to minimize the cost during the steel-out stage. Furthermore, greedy algorithm was implemented to solve this problem. Finally, application data obtained from a shipyard was used to validate the model, and the result shows that the proposed algorithm is effective to solve the steel stockyards scheduling problem.
Jaramillo, Juan Jose
2009-01-01
This paper studies the problem of congestion control and scheduling in ad hoc wireless networks that have to support a mixture of best-effort and real-time traffic. Optimization and stochastic network theory have been successful in designing architectures for fair resource allocation to meet long-term throughput demands. However, to the best of our knowledge, strict packet delay deadlines were not considered in this framework previously. In this paper, we propose for the first time a model for incorporating the quality of service (QoS) requirements of packets with deadlines in the optimization framework. The solution to the problem results in a joint congestion control and scheduling algorithm which fairly allocates resources to meet the fairness objectives of both elastic and inelastic flows, and per-packet delay requirements of inelastic flows.
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.
Optimal cognitive transmission exploiting redundancy in the primary ARQ process
DEFF Research Database (Denmark)
Michelusi, Nicholo; Simeone, Osvaldo; Levorato, Marco
2011-01-01
interference to the reception of the PM at the Primary Receiver (PR) and SR. Such interference may induce retransmissions of the same PM, which plays to the advantage of the secondary user, while at the same time making decoding of the PM more difficult also at the SR and reducing the available margin...... on the given interference constraint at the PR. It is proved that the optimal secondary strategy prioritizes transmissions in the states where the PM is known to the SR, due to the ability of the latter to perform interference mitigation and obtain a larger secondary throughput. Moreover, when the primary...
Önal, Hayri; Woodford, Philip; Tweddale, Scott A; Westervelt, James D; Chen, Mengye; Dissanayake, Sahan T M; Pitois, Gauthier
2016-04-15
Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a discrete dynamic optimization model to determine an optimum land restoration for a given training schedule and availability of financial resources to minimize the adverse effects of training on military lands. The model considers weather forecasts, scheduled maneuver exercises, and unique qualities and importance of the maneuver areas. An application of this approach to Fort Riley, Kansas, shows that: i) starting with natural conditions, the total amount of training damages would increase almost linearly and exceed a quarter of the training area and 228 gullies would be formed (mostly in the intensive training areas) if no restoration is carried out over 10 years; ii) assuming an initial state that resembles the present conditions, sustaining the landscape requires an annual restoration budget of $957 thousand; iii) targeting a uniform distribution of maneuver damages would increase the total damages and adversely affect the overall landscape quality, therefore a selective restoration strategy may be preferred; and iv) a proactive restoration strategy would be optimal where land degradations are repaired before they turn into more severe damages that are more expensive to repair and may pose a higher training risk. The last finding can be used as a rule-of-thumb for land restoration efforts in other installations with similar characteristics.
Reliability-based optimization of maintenance scheduling of mechanical components under fatigue.
Beaurepaire, P; Valdebenito, M A; Schuëller, G I; Jensen, H A
2012-05-01
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.
A new method on hydrothermal scheduling optimization in electric power market
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The hydrothermal scheduling in the electric power market becomes difficult because of introducing competition and considering sorts of constraints. An augmented Lagrangian approach is adopted to solve the problem, which adds to the standard Lagrangian function a quadratic penalty term without changing its dual property, and reduces the oscillation in iterations. According to the theory of large system coordination and decomposition, the problem is divided into hydro sub-problem and thermal sub-problem, which are coordinated by updating the Lagrangian multipliers, then the optimal solution is obtained. Our results for a test system show that the augmented Lagrangian approach can make the problem converge into the optimal solution quickly.
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.
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.
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. PMID:27348127
DEFF Research Database (Denmark)
Linker, Raphael; Ioslovich, Ilya; Sylaios, Georgios
2016-01-01
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......-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...... should use an irrigation schedule that maximizes the yield and abides to the quota constraints. In contrast to the widespread use of irrigation scheduling based on agronomy practices, irrigation scheduling may be considered as a constrained optimization problem. When drip irrigation is used, the decision...
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.
Optimal scheduling of biocide dosing for seawater-cooled power and desalination plants
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.
Transmission Dynamics and Optimal Control of Malaria in Kenya
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Gabriel Otieno
2016-01-01
Full Text Available This paper proposes and analyses a mathematical model for the transmission dynamics of malaria with four-time dependent control measures in Kenya: insecticide treated bed nets (ITNs, treatment, indoor residual spray (IRS, and intermittent preventive treatment of malaria in pregnancy (IPTp. We first considered constant control parameters and calculate the basic reproduction number and investigate existence and stability of equilibria as well as stability analysis. We proved that if R0≤1, the disease-free equilibrium is globally asymptotically stable in D. If R0>1, the unique endemic equilibrium exists and is globally asymptotically stable. The model also exhibits backward bifurcation at R0=1. If R0>1, the model admits a unique endemic equilibrium which is globally asymptotically stable in the interior of feasible region D. The sensitivity results showed that the most sensitive parameters are mosquito death rate and mosquito biting rates. We then consider the time-dependent control case and use Pontryagin’s Maximum Principle to derive the necessary conditions for the optimal control of the disease using the proposed model. The existence of optimal control problem is proved. Numerical simulations of the optimal control problem using a set of reasonable parameter values suggest that the optimal control strategy for malaria control in endemic areas is the combined use of treatment and IRS; for epidemic prone areas is the use of treatment and IRS; for seasonal areas is the use of treatment; and for low risk areas is the use of ITNs and treatment. Control programs that follow these strategies can effectively reduce the spread of malaria disease in different malaria transmission settings in Kenya.
NOVEL METHOD OF REALIZING THE OPTIMAL TRANSMISSION OF THE CRANK-AND-ROCKER MECHANISM DESIGN
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A novel method of realizing the optimal transmission of the crank-and-rocker mechanism is presented. The optimal combination design is made by finding the related optimal transmission parameters. The diagram of the optimal transmission is drawn. In the diagram, the relation among minimum transmission angle, the coefficient of travel speed variation, the oscillating angle of the rocker and the length of the bars is shown, concisely, conveniently and directly. The method possesses the main characteristic. That it is to achieve the optimal transmission parameters under the transmission angle by directly choosing in the diagram, according to the given requirements. The characteristics of the mechanical transmission can be improved to gain the optimal transmission effect by the method. Especially, the method is simple and convenient in practical use.
Multi Objective Optimization of Coordinated Scheduling of Cranes and Vehicles at Container Terminals
Directory of Open Access Journals (Sweden)
Seyed Mahdi Homayouni
2013-01-01
Full Text Available According to previous researches, automated guided vehicles and quay cranes in container terminals have a high potential synergy. In this paper, a mixed integer programming model is formulated to optimize the coordinated scheduling of cranes and vehicles in container terminals. Objectives of the model are to minimize total traveling time of the vehicles and delays in tasks of cranes. A genetic algorithm is developed to solve the problem in reasonable computational time. The most appropriate control parameters for the proposed genetic algorithm are investigated in a medium size numerical test case. It is shown that balanced crossover and mutation rates have the best performance in finding a near optimal solution for the problem. Then, ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the genetic algorithm since it can find near optimal solutions, precisely and accurately.
Directory of Open Access Journals (Sweden)
Souad Mekni
2014-11-01
Full Text Available In this paper, a modified invasive weed optimization (IWO algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs with the criteria to minimize the maximum completion time (makespan, the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.
DEFF Research Database (Denmark)
Lavrinenko, Andrei; Tetu, Amelie; Frandsen, Lars Hagedorn
2007-01-01
We present results on broadband transmission through photonic crystal waveguide bends optimized for slowlight modes. Theoretical analysis and topology optimization are complemented by experimental verification of designs fabricated in SOI material.......We present results on broadband transmission through photonic crystal waveguide bends optimized for slowlight modes. Theoretical analysis and topology optimization are complemented by experimental verification of designs fabricated in SOI material....
Koutsopoulos, Iordanis
2010-01-01
The smart power grid aims at harnessing information and communication technologies to enhance reliability and enforce sensible use of energy. Its realization is geared by the fundamental goal of effective management of demand load. In this work, we envision a scenario with real-time communication between the operator and consumers. The grid operator controller receives requests for power demands from consumers, with different power requirement, duration, and a deadline by which it is to be completed. The objective is to devise a power demand task scheduling policy that minimizes the grid operational cost over a time horizon. The operational cost is a convex function of instantaneous power consumption and reflects the fact that each additional unit of power needed to serve demands is more expensive as demand load increases.First, we study the off-line demand scheduling problem, where parameters are fixed and known. Next, we devise a stochastic model for the case when demands are generated continually and sched...
Optimizing lightwave transmission through a nano-tip
Directory of Open Access Journals (Sweden)
Xiangsheng Xie
2011-06-01
Full Text Available Optical microscopy with spatial resolution below the diffraction limit is at present attracting extensive attentions. Further advancement of the near-field scanning optical microscopy (NSOM, a practical super-resolution microscopy, is mainly limited by the low transmission of optical power through the nano-meter apex. This work shows that lightwave can be efficiently delivered to a sub-100 nm apex inside a tapered metallic guiding structure. The enhanced light delivery, about 5-fold, is made possible with an adaptive optimization of the transmission via a spatial light phase-modulator. Numerical simulation shows the mechanism for the efficient light delivery to be the selective excitation of predominantly the lowest-order transverse component of standing wavevector with proper input wavefront modulation, hence favoring the transmission of lightwave in the longitudinal direction. The demonstration of such efficient focusing, to about full-width at half-maximum of a quarter wavelength, has a direct and immediate application in the improvement of the existing NSOMs.
基于蚁群算法输电线路检修计划的制定%Maintenance scheduling of transmission lines based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
于宏涛; 高立群; 李丽霞
2011-01-01
为了提高制定输电线路检修计划的工作效率,提出了一种输电线路检修计划模型.该模型为任务量均分的多旅行商问题模型,综合考虑了线路缺陷的严重程度和重要性,在保证线路检修时间始终控制在允许范围内,以可靠性理论中故障率为基础的经济损失风险最小为目标.应用了改进蚁群算法和基本蚁群算法对模型进行仿真比较,结果显示前者求解质量较好,这表明了改进蚁群算法能够改善基本蚁群算法易于陷入局部最优解的缺点.%In order to improve efficiency of making transmission lines maintenance scheduling, presented a model for transmission lines maintenance scheduling. The model based on a multiple traveling salesman problem of equal task, took account of defect severity and importance of lines. Treated the minimal economic loss based on failure rate as the target in searching for the best maintenance scheduling. Limited meanwhile all line' s maintenance time to the range of its maintenance time-choice during the search. Applied both an improved ant colony algorithm and conventional ant colony algorithm to the problem. By contrast, the improved ant colony algorithm was superior to conventional ant colony algorithm in quality. The simulation results show the improved ant colony algorithm can improve the ability of escaping from local optimal solution.
Pugatch, Rami
2015-02-24
Bacterial self-replication is a complex process composed of many de novo synthesis steps catalyzed by a myriad of molecular processing units, e.g., the transcription-translation machinery, metabolic enzymes, and the replisome. Successful completion of all production tasks requires a schedule-a temporal assignment of each of the production tasks to its respective processing units that respects ordering and resource constraints. Most intracellular growth processes are well characterized. However, the manner in which they are coordinated under the control of a scheduling policy is not well understood. When fast replication is favored, a schedule that minimizes the completion time is desirable. However, if resources are scarce, it is typically computationally hard to find such a schedule, in the worst case. Here, we show that optimal scheduling naturally emerges in cellular self-replication. Optimal doubling time is obtained by maintaining a sufficiently large inventory of intermediate metabolites and processing units required for self-replication and additionally requiring that these processing units be "greedy," i.e., not idle if they can perform a production task. We calculate the distribution of doubling times of such optimally scheduled self-replicating factories, and find it has a universal form-log-Frechet, not sensitive to many microscopic details. Analyzing two recent datasets of Escherichia coli growing in a stationary medium, we find excellent agreement between the observed doubling-time distribution and the predicted universal distribution, suggesting E. coli is optimally scheduling its replication. Greedy scheduling appears as a simple generic route to optimal scheduling when speed is the optimization criterion. Other criteria such as efficiency require more elaborate scheduling policies and tighter regulation.
DEFF Research Database (Denmark)
Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob
2009-01-01
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...
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.
Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry
Institute of Scientific and Technical Information of China (English)
李磊; 李红娟
2015-01-01
To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.
Construction schedule simulation of a diversion tunnel based on the optimized ventilation time.
Wang, Xiaoling; Liu, Xuepeng; Sun, Yuefeng; An, Juan; Zhang, Jing; Chen, Hongchao
2009-06-15
Former studies, the methods for estimating the ventilation time are all empirical in construction schedule simulation. However, in many real cases of construction schedule, the many factors have impact on the ventilation time. Therefore, in this paper the 3D unsteady quasi-single phase models are proposed to optimize the ventilation time with different tunneling lengths. The effect of buoyancy is considered in the momentum equation of the CO transport model, while the effects of inter-phase drag, lift force, and virtual mass force are taken into account in the momentum source of the dust transport model. The prediction by the present model for airflow in a diversion tunnel is confirmed by the experimental values reported by Nakayama [Nakayama, In-situ measurement and simulation by CFD of methane gas distribution at a heading faces, Shigen-to-Sozai 114 (11) (1998) 769-775]. The construction ventilation of the diversion tunnel of XinTangfang power station in China is used as a case. The distributions of airflow, CO and dust in the diversion tunnel are analyzed. A theory method for GIS-based dynamic visual simulation for the construction processes of underground structure groups is presented that combines cyclic operation network simulation, system simulation, network plan optimization, and GIS-based construction processes' 3D visualization. Based on the ventilation time the construction schedule of the diversion tunnel is simulated by the above theory method.
Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid
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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.
Optimal Cluster Mill Pass Scheduling With an Accurate and Rapid New Strip Crown Model
Malik, Arif S.; Grandhi, Ramana V.; Zipf, Mark E.
2007-05-01
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
Locality Aware Optimal Task Scheduling Algorithm for TriBA——A Novel Scalable Architecture
Institute of Scientific and Technical Information of China (English)
KHAN Haroon-Ur-Rashid; SHI Feng
2008-01-01
An optimal algorithmic approach to task scheduling for,triplet based architecture(TriBA),is proposed in this paper.TriBA is considered to be a high performance,distributed parallel computing architecture.TriBA consists of a 2D grid of small,programmable processing units,each physically connected to its three neighbors.In parallel or distributed environment an efficient assignment of tasks to the processing elements is imperatire to achieve fast job turnaround time.Moreover,the sojourn time experienced by each individual job should be minimized.The arriving jobs are comprised of parallel applications,each consisting of multiple-independent tasks that must be instantaneously assigned to processor queues,as they arrive.The processors indeDendently and concurrently service these tasks.The key scheduling issues is,when some queue backlogs are small,an incoming job should first spread its tasks to those lightly loaded queues in order to take advantage of the parallel processing gain.Our algorithmic approach achieves optimality in task scheduling by assigning consecutive tasks to a triplet of processors exploiting locality in tasks.The experimental results show that tasks allocatian to triplets of processing elements is efficient and optimal.Comparison to well accepted interconnection strategy,2D mesh,is shown to prove the effectiveness of our algorithmic approach for TriBA.Finally we conclude that TriBA can be an efficient interconnection strategy for computations intensive applications,if tasks assignment is carried out optimally using algorithmic approach.
Institute of Scientific and Technical Information of China (English)
Jianjun Qi; Bo Guo; Hongtao Lei; Tao Zhang
2014-01-01
This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem (RACP) by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion (PPSO) combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.
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.
A Modified Biogeography-Based Optimization for the Flexible Job Shop Scheduling Problem
Directory of Open Access Journals (Sweden)
Yuzhen Yang
2015-01-01
Full Text Available The flexible job shop scheduling problem (FJSSP is a practical extension of classical job shop scheduling problem that is known to be NP-hard. In this paper, an effective modified biogeography-based optimization (MBBO algorithm with machine-based shifting is proposed to solve FJSSP with makespan minimization. The MBBO attaches great importance to the balance between exploration and exploitation. At the initialization stage, different strategies which correspond to two-vector representation are proposed to generate the initial habitats. At global phase, different migration and mutation operators are properly designed. At local phase, a machine-based shifting decoding strategy and a local search based on insertion to the habitat with best makespan are introduced to enhance the exploitation ability. A series of experiments on two well-known benchmark instances are performed. The comparisons between MBBO and other famous algorithms as well as BBO variants prove the effectiveness and efficiency of MBBO in solving FJSSP.
Directory of Open Access Journals (Sweden)
Xiaohui Yan
2015-01-01
Full Text Available Melting-casting is the first process in copper alloy strip production. The schedule scheme on this process affects the subsequent processes greatly. In this paper, we build the multiobjective model of melting-casting scheduling problem, which considers minimizing the makespan and total weighted earliness and tardiness penalties comprehensively. A novel algorithm, which we called Multiobjective Artificial Bee Colony/Decomposition (MOABC/D algorithm, is proposed to solve this model. The algorithm combines the framework of Multiobjective Evolutionary Algorithm/Decomposition (MOEA/D and the neighborhood search strategy of Artificial Bee Colony algorithm. The results on instances show that the proposed MOABC/D algorithm outperforms the other two comparison algorithms both on the distributions of the Pareto front and the priority in the optimal selection results.
Schedule Optimization Study, Hanford RI/FS Program. Volume 2, Final report
Energy Technology Data Exchange (ETDEWEB)
1992-12-01
A Schedule Optimization Study (SOS) of the US Department of Energy (DOE) Hanford Site Remedial Investigation/Feasibility Study (RI/FS) Program was conducted by an independent team of professionals from other federal agencies and the private sector experienced in environmental restoration. This team spent two weeks at Hanford in September 1992 examining the reasons for the lengthy RI/FS process at Hanford and developing recommendations to expedite the process. The need for the study arose out of a schedule dispute regarding the submission of the 1100-EM-1 Operable Unit RI/FS Work Plan. This report documents the study called for in the August 29, 1991, Dispute Resolution Committee Decision Statement. Battelle`s Environmental Management Operations (EMO) coordinated the effort for DOE`s Richland Field Office (RL).
Institute of Scientific and Technical Information of China (English)
Aijun Zhu; Chuanpei Xu; Zhi Li; Jun Wu; Zhenbing Liu
2015-01-01
A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evo-lution (HGWO). Because basic grey wolf optimization (GWO) is easy to fal into stagnation when it carries out the operation of at-tacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE’s strong searching ability. The proposed algorithm can accele-rate the convergence speed of GWO and improve its performance. Twenty-three wel-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
Cui, Laizhong; Jiang, Yong; Wu, Jianping; Xia, Shutao
Most large-scale Peer-to-Peer (P2P) live streaming systems are constructed as a mesh structure, which can provide robustness in the dynamic P2P environment. The pull scheduling algorithm is widely used in this mesh structure, which degrades the performance of the entire system. Recently, network coding was introduced in mesh P2P streaming systems to improve the performance, which makes the push strategy feasible. One of the most famous scheduling algorithms based on network coding is R2, with a random push strategy. Although R2 has achieved some success, the push scheduling strategy still lacks a theoretical model and optimal solution. In this paper, we propose a novel optimal pull-push scheduling algorithm based on network coding, which consists of two stages: the initial pull stage and the push stage. The main contributions of this paper are: 1) we put forward a theoretical analysis model that considers the scarcity and timeliness of segments; 2) we formulate the push scheduling problem to be a global optimization problem and decompose it into local optimization problems on individual peers; 3) we introduce some rules to transform the local optimization problem into a classical min-cost optimization problem for solving it; 4) We combine the pull strategy with the push strategy and systematically realize our scheduling algorithm. Simulation results demonstrate that decode delay, decode ratio and redundant fraction of the P2P streaming system with our algorithm can be significantly improved, without losing throughput and increasing overhead.
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
Optimization of rewards in single machine scheduling in the rewards-driven systems
Directory of Open Access Journals (Sweden)
Abolfazl Gharaei
2015-06-01
Full Text Available The single machine scheduling problem aims at obtaining the best sequence for a set of jobs in a manufacturing system with a single machine. In this paper, we optimize rewards in single machine scheduling in rewards-driven systems such that total reward is maximized while the constraints contains of limitation in total rewards for earliness and learning, independent of earliness and learning and etc. are satisfied. In mentioned systems as for earliness and learning the bonus is awarded to operators, we consider only rewards in mentioned systems and it will not be penalized under any circumstances. Our objective is to optimize total rewards in mentioned system by taking the rewards in the form of quadratic for both learning and earliness. The recently-developed sequential quadratic programming (SQP, is used by solve the problem. Results show that SQP had satisfactory performance in terms of optimum solutions, number of iterations, infeasibility and optimality error. Finally, a sensitivity analysis is performed on the change rate of the objective function obtained based on the change rate of the “amount of earliness for jobs (Ei parameter”.
Dao, Son Duy; Abhary, Kazem; Marian, Romeo
2017-01-01
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to "learn" from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.
Dao, Son Duy; Abhary, Kazem; Marian, Romeo
2017-01-01
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to "learn" from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.
Optimal Switchable Load Sizing and Scheduling for Standalone Renewable Energy Systems
Habib, Abdulelah H.; Disfani, Vahid R.; Kleissl, Jan; de Callafon, Raymond A.
2017-01-01
The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent switching of loads in the absence of an energy storage system causes wear and tear and should be avoided. Yet, the amount of solar energy utilized should be maximized and the problem of finding the optimal static load size of a finite number of discrete electri...
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.
Directory of Open Access Journals (Sweden)
Ming Liu
2015-11-01
Full Text Available Purpose: This paper aims to propose an optimal scheduling for medical resources order and shipment in community health service centers (CHSCs.Design/methodology/approach: This paper presents two logistical support models for scheduling medical resources in CHSCs. The first model is a deterministic planning model (DM, which systematically considers the demands for various kinds of medical resources, the lead time of supplier, the storage capacity and other constraints, as well as the integrated shipment planning in the dimensions of time and space. The problem is a multi-commodities flow problem and is formulated as a mixed 0-1 integer programming model. Considering the demand for medical resources is always stochastic in practice, the second model is constructed as a stochastic programming model (SM. A solution procedure is developed to solve the proposed two models and a simulation-based evaluation method is proposed to compare the performances of the proposed models. Findings andFindings: The main contributions of this paper includes the following two aspects: (1 While most research on medical resources optimization studies a static problem taking no consideration of the time evolution and especially the dynamic demand for such resources, the proposed models in our paper integrate time-space network technique, which can find the optimal scheduling of logistical support for medical resources order and shipment in CHSCs effectively. (2 The logistics plans in response to the deterministic demand and the time-varying demand are constructed as 0-1 mixed integer programming model and stochastic integer programming model, respectively. The optimal solutions not only minimize the operation cost of the logistics system, but also can improve the order and shipment operation in practice.Originality/value: Currently, medical resources in CHSCs are purchased by telephone or e-mail. The important parameters in decision making, i.e. order/shipment frequency
A Novel Particle Swarm Optimization for Flow Shop Scheduling with Fuzzy Processing Time
Institute of Scientific and Technical Information of China (English)
NIU Qun; GU Xing-sheng
2008-01-01
Since in most practical cases the processing time of scheduling is not deterministic,flow shop scheduling model with fuzzy processing time is established.It is assumed that the processing times of jobs on the machines are described by triangular fuzzy sets.In order to find a sequence that minimizes the mean makespan and the spread of the makespan,Lee and Li fuzzy ranking method is adopted and modified to solve the problem.Particle swarm optimization (PSO) is a population-based stochyastic appmxilmtion aigorithm that has been applied to a wide range of problems,but there is little reported in respect of application to scheduling problems because of its unsuitability for them.In the paper,PSO is redefined and modified by introducing genetic operations such as crossover and mutation to update the particles,which is called GPSO and successfully employed to solve the formulated problem.A series of benchmarks with fuzzy processing time are used to verify GPSO.Extensive experiments show the feasibility and effectiveness of the proposed method.
Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems
Energy Technology Data Exchange (ETDEWEB)
Ferrandi, Fabrizio; Lanzi, Pier Luca; Pilato, Christian; Sciuto, Donatella; Tumeo, Antonino
2013-06-24
Modern heterogeneous embedded platforms, com- posed of several digital signal, application specific and general purpose processors, also include reconfigurable devices support- ing partial dynamic reconfiguration. These devices can change the behavior of some of their parts during execution, allowing hardware acceleration of more sections of the applications. Never- theless, partial dynamic reconfiguration imposes severe overheads in terms of latency. For such systems, a critical part of the design phase is deciding on which processing elements (mapping) and when (scheduling) executing a task, but also how to place them on the reconfigurable device to guarantee the most efficient reuse of the programmable logic. In this paper we propose an algorithm based on Ant Colony Optimization (ACO) that simultaneously executes the scheduling, the mapping and the linear placing of tasks, hiding reconfiguration overheads through prefetching. Our heuristic gradually constructs solutions and then searches around the best ones, cutting out non-promising areas of the design space. We show how to consider the partial dynamic reconfiguration constraints in the scheduling, placing and mapping problems and compare our formulation to other heuristics that address the same problems. We demonstrate that our proposal is more general and robust, and finds better solutions (16.5% in average) with respect to competing solutions.
Directory of Open Access Journals (Sweden)
Minru Bai
2014-01-01
Full Text Available As a major energy-saving industry, power industry has implemented energy-saving generation dispatching. Apart from security and economy, low carbon will be the most important target in power dispatch mechanisms. In this paper, considering a power system with many thermal power generators which use different petrochemical fuels (such as coal, petroleum, and natural gas to produce electricity, respectively, we establish a self-scheduling model based on the forecasted locational marginal prices, particularly taking into account CO2 emission constraint, CO2 emission cost, and unit heat value of fuels. Then, we propose a distributionally robust self-scheduling optimization model under uncertainty in both the distribution form and moments of the locational marginal prices, where the knowledge of the prices is solely derived from historical data. We prove that the proposed robust self-scheduling model can be solved to any precision in polynomial time. These arguments are confirmed in a practical example on the IEEE 30 bus test system.
Directory of Open Access Journals (Sweden)
Nirmeen A. Bahnasawy
2011-11-01
Full Text Available In distributed computing, the schedule by which tasks are assigned to processors is critical to minimizing the execution time of the application. However, the problem of discovering the schedule that gives the minimum execution time is NP-complete. In this paper, a new task scheduling algorithm called Sorted Nodes in Leveled DAG Division (SNLDD is introduced and developed for HeDCSs with consider a bounded number of processors. The main principle of the developed algorithm is to divide the Directed Acyclic Graph (DAG into levels and sort the tasks in each level according to their computation size in descending order. To evaluate the performance of the developed SNLDD algorithm, a comparative study has been done between the developed SNLDD algorithm and the Longest Dynamic Critical Path (LDCP algorithm which is considered the most efficient existing algorithm. According to the comparative results, it is found that the performance of the developed algorithm provides better performance than the LDCP algorithm in terms of speedup, efficiency, complexity, and quality. Also, a new procedure called Superior Performance Optimization Procedure (SPOP has been introduced and implemented in the developed SNLDD algorithm and the LDCP algorithm to minimize the sleek time of the processors in the system. Again, the performance of the SNLDD algorithm outperforms the existing LDCP algorithm after adding the SPOP procedure.
A discrete multi-swarm optimizer for radio frequency identification network scheduling
Institute of Scientific and Technical Information of China (English)
陈瀚宁; 朱云龙
2014-01-01
Due to the effectiveness, simple deployment and low cost, radio frequency identification (RFID) systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another’s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called PS2O to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS2O algorithm is proposed. With seven discrete benchmark functions, PS2O is proved to have significantly better performance than the original PSO and a binary genetic algorithm. PS2O is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.
Optimal online algorithms for scheduling on two identical machines under a grade of service
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This work is aimed at investigating the online scheduling problem on two parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels, so each job and machine are labelled with the GoS levels, and each job can be processed by a particular machine only when its GoS level is no less than that of the machine. The goal is to minimize the makespan. For non-preemptive version, we propose an optimal online algorithm with competitive ratio 5/3. For preemptive version, we propose an optimal online algorithm with competitive ratio 3/2.
Directory of Open Access Journals (Sweden)
Peng Liang
2015-01-01
Full Text Available This research considers an unrelated parallel machine scheduling problem with energy consumption and total tardiness. This problem is compounded by two challenges: differences of unrelated parallel machines energy consumption and interaction between job assignments and machine state operations. To begin with, we establish a mathematical model for this problem. Then an ant optimization algorithm based on ATC heuristic rule (ATC-ACO is presented. Furthermore, optimal parameters of proposed algorithm are defined via Taguchi methods for generating test data. Finally, comparative experiments indicate the proposed ATC-ACO algorithm has better performance on minimizing energy consumption as well as total tardiness and the modified ATC heuristic rule is more effectively on reducing energy consumption.
Design of synchromesh mechanism to optimization manual transmission's electric vehicle
Zainuri, Fuad; Sumarsono, Danardono A.; Adhitya, Muhammad; Siregar, Rolan
2017-03-01
Significant research has been attempted on a vehicle that lead to the development of transmission that can reduce energy consumption and improve vehicle efficiency. Consumers also expect safety, convenience, and competitive prices. Automatic transmission (AT), continuously variable transmission (CVT), and dual clutch transmission (DCT) is the latest transmission developed for road vehicle. From literature reviews that have been done that this transmission is less effective on electric cars which use batteries as a power source compared to type manual transmission, this is due to the large power losses when making gear changes. Zeroshift system is the transmission can do shift gears with no time (zero time). It was developed for the automatic manual transmission, and this transmission has been used on racing vehicles to eliminate deceleration when gear shift. Zeroshift transmission still use the clutch to change gear in which electromechanical be used to replace the clutch pedal. Therefore, the transmission is too complex for the transmission of electric vehicles, but its mechanism is considered very suitable to increase the transmission efficiency. From this idea, a new innovation design transmission will be created to electric car. The combination synchromesh with zeroshift mechanism for the manual transmission is a transmission that is ideal for improving the transmission efficiency. Installation synchromesh on zeroshift mechanism is expected to replace the function of the clutch MT, and assisted with the motor torque setting when to change gear. Additionally to consider is the weight of the transmission, ease of manufacturing, ease of installation with an electric motor, as well as ease of use by drivers is a matter that must be done to obtain a new transmission system that is suitable for electric cars.
Hanish Nithin, Anu; Omenzetter, Piotr
2017-04-01
Optimization of the life-cycle costs and reliability of offshore wind turbines (OWTs) is an area of immense interest due to the widespread increase in wind power generation across the world. Most of the existing studies have used structural reliability and the Bayesian pre-posterior analysis for optimization. This paper proposes an extension to the previous approaches in a framework for probabilistic optimization of the total life-cycle costs and reliability of OWTs by combining the elements of structural reliability/risk analysis (SRA), the Bayesian pre-posterior analysis with optimization through a genetic algorithm (GA). The SRA techniques are adopted to compute the probabilities of damage occurrence and failure associated with the deterioration model. The probabilities are used in the decision tree and are updated using the Bayesian analysis. The output of this framework would determine the optimal structural health monitoring and maintenance schedules to be implemented during the life span of OWTs while maintaining a trade-off between the life-cycle costs and risk of the structural failure. Numerical illustrations with a generic deterioration model for one monitoring exercise in the life cycle of a system are demonstrated. Two case scenarios, namely to build initially an expensive and robust or a cheaper but more quickly deteriorating structures and to adopt expensive monitoring system, are presented to aid in the decision-making process.
MULTIOBJECTIVE FLEXIBLE JOB SHOP SCHEDULING USING A MODIFIED INVASIVE WEED OPTIMIZATION
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Souad Mekni
2015-02-01
Full Text Available Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs with the criteria to minimize the maximum completion time (makespan, the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.
Study on the Flexible Optimization Schedule Models of Cigarette Producing Line%卷烟生产线柔性优化调度模型研究
Institute of Scientific and Technical Information of China (English)
刘永梅; 陈庄; 崔贯勋; 何昭全
2003-01-01
Through analyzing technological process of cigarette producing line and utilizing the basic idea of modern graph theory, the connected network system based on continuous material flow is drawn from cigarette producing line. The thought of flexible optimization schedule is proposed and the optimization schedule models of cigarette producing line are created. Then their visualized example is given. These optimization schedule models have already been used for actual cigarette producing schedule and they have direction meaning for instructing manager at producing ground to carry on optimization schedule.
Institute of Scientific and Technical Information of China (English)
Li Dawei; Zhang Zhihua; Zhong Qianghui; Zhai Yali
2014-01-01
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 influ-ence 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 deterio-ration 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 sys-tem expected life-cycle cost per unit time and a constraint on system survival probability for the dura-tion 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 strat-egy. 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.
Optimal Scheduling Method of Controllable Loads in DC Smart Apartment Building
Shimoji, Tsubasa; Tahara, Hayato; Matayoshi, Hidehito; Yona, Atsushi; Senjyu, Tomonobu
2015-12-01
From the perspective of global warming suppression and the depletion of energy resources, renewable energies, such as the solar collector (SC) and photovoltaic generation (PV), have been gaining attention in worldwide. Houses or buildings with PV and heat pumps (HPs) are recently being used in residential areas widely due to the time of use (TOU) electricity pricing scheme which is essentially inexpensive during middle-night and expensive during day-time. If fixed batteries and electric vehicles (EVs) can be introduced in the premises, the electricity cost would be even more reduced. While, if the occupants arbitrarily use these controllable loads respectively, power demand in residential buildings may fluctuate in the future. Thus, an optimal operation of controllable loads such as HPs, batteries and EV should be scheduled in the buildings in order to prevent power flow from fluctuating rapidly. This paper proposes an optimal scheduling method of controllable loads, and the purpose is not only the minimization of electricity cost for the consumers, but also suppression of fluctuation of power flow on the power supply side. Furthermore, a novel electricity pricing scheme is also suggested in this paper.
Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer
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
Energy Technology Data Exchange (ETDEWEB)
Garzillo, A.; Innorta, M.; Marannino, P.; Mognetti, F., Cova, B.
1988-09-01
This paper presents some criteria applied to the optimization of voltage profiles and reactive power generation distribution among various resources in daily scheduling and VAR planning. The mathematical models employed in the representation of the two problems are quite similar in spite of the different objective functions and control variable set. The solution is based upon the implementation of two optimal reactive power flow (ORPF) programs. The first ORPF determines a feasible operating point in daily scheduling application, or the minimum investment installations required by system security in VAR planning application. It utilizes a linear algorithm (gradient protection) suggested by Rosen which has been found to be a favourable alternative to the commonly suited simplex method. The second ORPF determines the minimum losses operating point, in the reactive power dispatch, or the most beneficial installation of reactive compensations in VAR planning. The solution of the economy problems is carried out by the Han-Powell algorithm. It essentially solves a set of quadratic sub-problems. In the adopted procedure, the quadratic sub-problems are solved by exploiting an active constraint strategy in the QUADRI subroutine used as an alternative to the well-known Beale method.
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.
A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem
Directory of Open Access Journals (Sweden)
Xiaohui Li
2010-01-01
Full Text Available A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.
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.
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.
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
Optimal switchable load sizing and scheduling for standalone renewable energy systems
Habib, Abdulelah H.; Disfani, Vahid R.; Kleissl, Jan; de Callafon, Raymond A.
2017-03-01
The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent switching of loads in the absence of an energy storage system causes wear and tear and should be avoided. Yet, the amount of solar energy utilized should be maximized and the problem of finding the optimal static load size of a finite number of discrete electric loads on the basis of a load response optimization is considered in this paper. The objective of the optimization is to maximize solar energy utilization without the need for costly energy storage systems in an off-grid system. Conceptual and real data for solar photovoltaic power production is provided the input to the off-grid system. Given the number of units, the following analytical solutions and computational algorithms are proposed to compute the optimal load size of each unit: mixed-integer linear programming and constrained least squares. Based on the available solar power profile, the algorithms select the optimal on/off switch times and maximize solar energy utilization by computing the optimal static load sizes. The effectiveness of the algorithms is compared using one year of solar power data from San Diego, California and Thuwal, Saudi Arabia. It is shown that the annual system solar energy utilization is optimized to 73% when using two loads and can be boosted up to 98% using a six load configuration.
Directory of Open Access Journals (Sweden)
A. Meenakshi
2016-08-01
Full Text Available Resource allocation is the task of convenient resources to different uses. In the context of an resources, entire economy, can be assigned by different means, such as markets or central planning. Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere. The fundamental motive of the resource allocation is to allot the available resource in the most effective manner. In the initial phase, a representative resource usage distribution for a group of nodes with identical resource usage patterns is evaluated as resource bundle which can be easily employed to locate a group of nodes fulfilling a standard criterion. In the document, an innovative clustering-based resource aggregation viz. the Improved Hierarchal Agglomerative Clustering Algorithm (IHAC is elegantly launched to realize the compact illustration of a set of identically behaving nodes for scalability. In the subsequent phase concerned with energetic resource allocation procedure, the hybrid optimization technique is brilliantly brought in. The novel technique is devised for scheduling functions to cloud resources which duly consider both financial and evaluation expenses. The efficiency of the novel Resource allocation system is assessed by means of several parameters such the reliability, reusability and certain other metrics. The optimal path choice is the consequence of the hybrid optimization approach. The new-fangled technique allocates the available resource based on the optimal path.
DEFF Research Database (Denmark)
Lavrinenko, Andrei; Têtu, Amélie; Frandsen, Lars Hagedorn
2006-01-01
We present results for broadband transmission through photonic crystal waveguide bends optimized for slow-light modes. Theoretical analysis is complemented by experimental verification of designs including topology optimized ones fabricated in SOI material.......We present results for broadband transmission through photonic crystal waveguide bends optimized for slow-light modes. Theoretical analysis is complemented by experimental verification of designs including topology optimized ones fabricated in SOI material....
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.
Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report
Energy Technology Data Exchange (ETDEWEB)
Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T
2011-08-04
The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.
Optimization of Heating Schedules for Measurement of Helium Diffusion in Monazite
Day, C.; Grove, M.; Peterman, E.
2010-12-01
. Consequently, fine-scale variation in T is needed to distribute He release over many steps. This critical interval in T depends upon E and thus varies as a function of monazite composition. (2) The impact of r is smaller but not insignificant. Reducing r shifts the sensitive T range of He release to lower T and vice versa. (3) In the diffusion equation, Do and t occur in the numerator while r squared is present within the denominator. Consequently, the impact of increasing either Do or t upon the amount of He release is significantly less than that realized by equivalent reduction of r. It is thus most useful to optimize r and simply select values of t that are most convenient for the experiment. The above principles guide definition of useful T-t schedules for diffusion experiments. The optimal heating schedule for a given monazite—including initial and final temperatures—depends upon r and monazite composition. For example, for r = 175 μm, a heating schedule that increased in 50°C increments over 10 minute intervals should start at 400°C. Virtually all of the gas would be exhausted from this monazite by 1000°C.
Hemmati, Reza; Saboori, Hedayat
2016-05-01
Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics.
Hendricks Franssen, H.; Han, X.; Martinez, F.; Jimenez, M.; Manzano, J.; Chanzy, A.; Vereecken, H.
2013-12-01
Data assimilation (DA) techniques, like the local ensemble transform Kalman filter (LETKF) not only offer the opportunity to update model predictions by assimilating new measurement data in real time, but also provide an improved basis for real-time (DA-based) control. This study focuses on the optimization of real-time irrigation scheduling for fields of citrus trees near Picassent (Spain). For three selected fields the irrigation was optimized with DA-based control, and for other fields irrigation was optimized on the basis of a more traditional approach where reference evapotranspiration for citrus trees was estimated using the FAO-method. The performance of the two methods is compared for the year 2013. The DA-based real-time control approach is based on ensemble predictions of soil moisture profiles, using the Community Land Model (CLM). The uncertainty in the model predictions is introduced by feeding the model with weather predictions from an ensemble prediction system (EPS) and uncertain soil hydraulic parameters. The model predictions are updated daily by assimilating soil moisture data measured by capacitance probes. The measurement data are assimilated with help of LETKF. The irrigation need was calculated for each of the ensemble members, averaged, and logistic constraints (hydraulics, energy costs) were taken into account for the final assigning of irrigation in space and time. For the operational scheduling based on this approach only model states and no model parameters were updated by the model. Other, non-operational simulation experiments for the same period were carried out where (1) neither ensemble weather forecast nor DA were used (open loop), (2) Only ensemble weather forecast was used, (3) Only DA was used, (4) also soil hydraulic parameters were updated in data assimilation and (5) both soil hydraulic and plant specific parameters were updated. The FAO-based and DA-based real-time irrigation control are compared in terms of soil moisture
Use of an administrative data set to determine optimal scheduling of an alcohol intervention worker.
Peterson, Timothy A; Desmond, Jeffrey S; Cunningham, Rebecca
2012-06-01
Brief alcohol interventions are efficacious in reducing alcohol-related consequences among emergency department (ED) patients. Use of non-clinical staff may increase alcohol screening and intervention; however, optimal scheduling of an alcohol intervention worker (AIW) is unknown. Determine optimal scheduling of an AIW based on peak discharge time of alcohol-related ED visits. Discharge times for consecutive patients with an alcohol-related diagnosis were abstracted from an urban ED's administrative data set from September 2005 through August 2007. Queuing theory was used to identify optimal scheduling. Data for weekends and weekdays were analyzed separately. Stationary independent period-by-period analysis was performed for hourly periods. An M/M/s queuing model, for Markovian inter-arrival time/Markovian service time/and potentially more than one server, was developed for each hour assuming: 1) a single unlimited queue; 2) 75% of patients waited no longer than 30 min for intervention; 3) AIW spent an average 20 min/patient. Estimated average utilization/hour was calculated; if utilization/hour exceeded 25%, AIW staff was considered necessary. There were 2282 patient visits (mean age 38 years, range 11-84 years). Weekdays accounted for 45% of visits; weekends 55%. On weekdays, one AIW from 6:00 a.m.-9:00 a.m. (max utilization 42%/hour) would accommodate 28% of weekday alcohol-related patients. On weekends, 5:00 a.m.-11:00 a.m. (max utilization 50%), one AIW would cover 54% of all weekend alcohol-related visits. During other hours the utilization rate falls below 25%/hour. Evaluating 2 years of discharge data revealed that 30 h of dedicated AIW time--18 weekend hours (5:00 a.m.-11:00 a.m.), 12 weekday hours (6:00 a.m.-9:00 a.m.)--would allow maximal patient alcohol screening and intervention with minimal additional burden to clinical staff. Copyright © 2012 Elsevier Inc. All rights reserved.
Research on Shift Schedule of Automatic Transmission Based on Matlab%基于 Matlab 的自动变速器换档规律研究
Institute of Scientific and Technical Information of China (English)
高扬; 高洪
2012-01-01
According to two parameters shift schedule, an automatic shift schedule model is built by stateflow. An automatic transmission shift model is built by software simulink, and shift schedule of full vehicle automatic transmission is simulated. Results show that the automatic transmission system works as expected.% 按照两参数换档规律理论，在 stateflow 软件中建立了自动换档规律模型，利用 simulink 软件建立了自动变速器换档模型，仿真研究了整车自动换档规律。结果表明，此自动变速器模型工作与期望相符
Directory of Open Access Journals (Sweden)
Seyedehfarzaneh Nojabaei
2014-01-01
Full Text Available Efficiency is becoming a pivotal aspect in each manufacturing system and scheduling plays a crucial role in sustaining it. The applicability of distributed computing to coordinate and execute jobs has been investigated in the past literature. Moreover, it is significant that even for sensitive industrial systems the only criterion of allocating jobs to appropriate machines is the FIFO policy. On the other flip, many researchers are of the opinion that the main reason behind failing to provide fairness in distributed systems is considering the only criterion of time stamp to judge upon and form the queue of jobs with the aim of allocating those jobs to the machines. In order to increase the efficiency of sensitive industrial system, this study takes into consideration of three criteria of each job including priority, time action and time stamp. The methodology adopted by this study is definition of job scheduler and positioning jobs in temporary queue and sorting via developing bubble sort. In sorting algorithm criterion of priority, time action should be considered besides time stamp to recognize the tense jobs for processing earlier. To evaluate this algorithm first a numerical test case (simulation is programmed and then the case study performing in order to optimize efficiency of applying this method in real manufacturing system. Eventually the results of this study provided evidence on that the rate of efficiency is increased.
Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm
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R. Tavakkoli-Moghaddam
2013-06-01
Full Text Available Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA method and the results are discussed.
Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem
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S. V. Kamble
2015-03-01
Full Text Available Hybrid algorithm based on Particle Swarm Optimization (PSO and Simulated annealing (SA is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP. Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.
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...
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.
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Hugo Morais
2016-01-01
Full Text Available The need for developing new methodologies in order to improve power system stability has increased due to the recent growth of distributed energy resources. In this paper, the inclusion of a voltage stability index in distributed energy resources scheduling is proposed. Two techniques were used to evaluate the resulting multiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programming methodology. With this new methodology, the system operators can consider both the costs and voltage stability. Priority can be assigned to one objective function according to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks. One detailed case study considering a distribution network with high penetration of distributed energy resources is presented to analyse the proposed methodology. Additionally, the methodology is tested in a real distribution network.
High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience
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Dingjun Chen
2016-01-01
Full Text Available 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 is established. The model aims at minimizing stop cost and maximizing passenger travel convenience. Several constraints are applied to the model establishment, including the number of stops made by individual trains, the frequency of train service received by each station, the operation section, and the 0-1 variable. A hybrid genetic algorithm is designed to solve the model. Both the model and the algorithm are validated through case study.
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.
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Gang Du
2017-02-01
Full Text Available As a new service model, home health care can provide effective health care by adopting door-to-door service. The reasonable arrangements for nurses and their routes not only can reduce medical expenses, but also can enhance patient satisfaction. This research focuses on the home health care scheduling optimization problem with known demands and service capabilities. Aimed at minimizing the total cost, an integer programming model was built in this study, which took both the priorities of patients and constraints of time windows into consideration. The genetic algorithm with local search was used to solve the proposed model. Finally, a case study of Shanghai, China, was conducted for the empirical analysis. The comparison results verify the effectiveness of the proposed model and methodology, which can provide the decision support for medical administrators of home health care.
Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The author introduced particle swarm optimization as a new method for power transmission network expansion planning. A new discrete method for particle swarm optimization, was developed, which is suitable for power transmission network expansion planning, and requires less computer s memory. The optimization fitness function construction, parameter selection, convergence judgement, and their characters were analyzed. Numerical simulation demonstrated the effectiveness and correctness of the method, This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.
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P. Mathiyalagan
2013-10-01
Full Text Available As grid is a heterogeneous environment, finding an optimal schedule for the job is always a complex task. In this paper, a hybridization technique using intelligent water drops and Ant colony optimization which are nature-inspired swarm intelligence approaches are used to find the best resource for the job. Intelligent water drops involves in finding out all matching resources for the job requirements and the routing information (optimal path to reach those resources. Ant Colony optimization chooses the best resource among all matching resources for the job. The objective of this approach is to converge to the optimal schedule faster, minimize the make span of the job, improve load balancing of resources and efficient utilization of available resources.
Institute of Scientific and Technical Information of China (English)
赵岳
2012-01-01
卫星数传调度策略评价是一类多属性决策问题.对调度策略的选用和提升有重要的理论意义和实际价值.在介绍了卫星数传调度模型和调度策略原理的基础上,构建任务调度实例,获取了调度方案和算法运行的数据.根据所建评价指标体系,应用TOPSIS法综合评价卫星数传调度策略.评价结果表明,STK/Scheduler的五种调度策略的求解能力和适用范围存在较大差异.基于TOPSIS法对卫星数传调度策略进行评价,具有一定的可行性和科学性,能够为调度策略的选择提供参考依据.同时对复杂调度策略的评价有借鉴作用.%Evaluation of satellite data transmission scheduling strategy is a kind of multiple attribute decision making problems with a very important significance for selecting and improve scheduling strategy. Data of scheduling schema and algorithm running on the basis of discussion of satellite data transmission scheduling model and feathers of scheduling strategy principle are got. According to the created evaluation index system, overall evaluations of satellite data transmission scheduling strategy with TOPSIS are maken. The results of evaluation method show that there is a big difference between five scheduling strategies of STK/Scheduler in solving ability and scope of application. Evaluation of satellite data transmission scheduling strategy based on TOPSIS can offer valuable references for selecting scheduling strategy and has some reference for evaluation of complex scheduling strategy with feasibility and scientific nature.
Arakawa, Masahiro; Fuyuki, Masahiko; Inoue, Ichiro
Aiming at the elimination of tardy jobs in a job shop production schedule, an optimization-oriented simulation-based scheduling (OSBS) method incorporating capacity adjustment function is proposed. In order to determine the pertinent additional capacities and to control job allocations simultaneously the proposed method incorporates the parameter-space search improvement (PSSI) method into the scheduling procedure. In previous papers, we have introduced four parameters; two of them are used to control the upper limit to the additional capacity and the balance of the capacity distribution among machines, while the others are used to control the job allocation procedure. We found that a ‘direct' optimization procedure which uses the enumeration method produces a best solution with practical significance, but it takes too much computation time for practical use. In this paper, we propose a new method which adopts a pattern search method in the schedule generation procedure to obtain an approximate optimal solution. It is found that the computation time becomes short enough for a practical use. Moreover, the extension of the parameter domain yields an approximate optimal solution which is better than the best solution obtained by the ‘direct' optimization.
Yannibelli, Virginia; Amandi, Analía
2013-01-01
In this article, the project scheduling problem is addressed in order to assist project managers at the early stage of scheduling. Thus, as part of the problem, two priority optimization objectives for managers at that stage are considered. One of these objectives is to assign the most effective set of human resources to each project activity. The effectiveness of a human resource is considered to depend on its work context. The other objective is to minimize the project makespan. To solve the problem, a multi-objective evolutionary algorithm is proposed. This algorithm designs feasible schedules for a given project and evaluates the designed schedules in relation to each objective. The algorithm generates an approximation to the Pareto set as a solution to the problem. The computational experiments carried out on nine different instance sets are reported.
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. PMID:28263994
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.
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.
Kirti Nagpal; Vaishali Wadhwa
2012-01-01
In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is important for achieving a high performance. Task scheduling is essential for the suitable operation of multiprocessor systems. The aim of task scheduling is to determine an assignment of tasks to processors for shortening the length of schedules. A task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph), so the prob...
Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms
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Monica Alonso
2014-04-01
Full Text Available Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers’ behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA, where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.
Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems
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Whei-Min Lin
2015-09-01
Full Text Available This paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chilled water, the return temperature of ice storage water, and the supply temperature of ice storage water, are measured. The least-squares regression (LSR is used to obtain the input-output (I/O curve for the cooling load and power consumption of chillers and ice storage tank. The objective is to minimize overall cost in a daily schedule while satisfying all constraints, including cooling loading under the time-of-use (TOU rate. Based on the Radial Basis Function Network (RBFN and Ant Colony Optimization, an Ant-Based Radial Basis Function Network (ARBFN is constructed in the searching process. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the economic dispatch of ice storage air-conditioning systems, and offering greater energy efficiency in dispatching chillers.
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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.
组合优化调度问题求解方法%The Approach to Solving Combinatorial Optimization Schedule Problems
Institute of Scientific and Technical Information of China (English)
张居阳; 孙吉贵
2003-01-01
Optimization schedule problem is this kind of problem that people often meet in the field of industrial manufacture,transportation and traffic. A good schedule scheme can improve the efficiency of production and reduce the cost of production. So scholars in all of the related fields have high regard for schedule problem at all times. This paper describes the method and technology about combinatorial optimization schedule problems. The research state and advances in this field are reviewed and surveyed. At the end of the paper an approach to solving Job Shop problem,a representative paradigm in schedule problem ,is introduced and discussed concretely.
Tumor growth rate determines the timing of optimal chronomodulated treatment schedules.
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Samuel Bernard
2010-03-01
Full Text Available In host and cancer tissues, drug metabolism and susceptibility to drugs vary in a circadian (24 h manner. In particular, the efficacy of a cell cycle specific (CCS cytotoxic agent is affected by the daily modulation of cell cycle activity in the target tissues. Anti-cancer chronotherapy, in which treatments are administered at a particular time each day, aims at exploiting these biological rhythms to reduce toxicity and improve efficacy of the treatment. The circadian status, which is the timing of physiological and behavioral activity relative to daily environmental cues, largely determines the best timing of treatments. However, the influence of variations in tumor kinetics has not been considered in determining appropriate treatment schedules. We used a simple model for cell populations under chronomodulated treatment to identify which biological parameters are important for the successful design of a chronotherapy strategy. We show that the duration of the phase of the cell cycle targeted by the treatment and the cell proliferation rate are crucial in determining the best times to administer CCS drugs. Thus, optimal treatment times depend not only on the circadian status of the patient but also on the cell cycle kinetics of the tumor. Then, we developed a theoretical analysis of treatment outcome (TATO to relate the circadian status and cell cycle kinetic parameters to the treatment outcomes. We show that the best and the worst CCS drug administration schedules are those with 24 h intervals, implying that 24 h chronomodulated treatments can be ineffective or even harmful if administered at wrong circadian times. We show that for certain tumors, administration times at intervals different from 24 h may reduce these risks without compromising overall efficacy.
Small-scale farming optimization using frugal plant-based irrigation scheduling in Kenya
Samuel, L.; Ondula, E.; Wambua, M.; Fleming, K.
2015-12-01
Climate change is altering environmental conditions and impacting agriculture globally, especially in sub-Saharan Africa. Increased severity and duration of droughts coupled with decreased rainfall, mean that farmers have smaller inconsistent water supplies for crop production. Yields are negatively impacted by both deficiencies and excesses in key nutrients, as well as water in the surrounding environment. Small-scale farmers either overlook these nuances, make adjustments based on guesses as field conditions evolve or rely on confusing advice from agronomists and technical experts, who also lack insights in the farmer's unique operating situation. Thus, their crop yields are often below optimal quantities as farmers are unable to ensure that their crops experience limited stress during development. Precision irrigation scheduling was designed to address this need but is expensive and does not match the mode of operation of the average small-scale farmer. To this end, we have developed a frugal, cloud-integrated, cyber-physical sensing framework which relies on small networks of strategically placed soil moisture sensors together with tank-level sensors and utilizes plant growth models to determine if and how much irrigation is needed. By utilizing weather data to calculate both plant growth and evapotranspiration, we are able to monitor plant health and track development. Combining these calculations with cloud-enabled data management and analytics, we seek to provide small-scale farmers with a low-cost method for precision irrigation management. From both laboratory trials and a case study (a mixed-crop farm in Machakos), we illustrate the effectiveness of our approach for validating the accuracy of sensors and developing models and algorithms for tailored irrigation scheduling.
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Hao Qiang
2013-05-01
Full Text Available As an emerging research field, Wireless Power Transfer (WPT has attracted wide spread attention recently. In this study, the coil design of WPT system for optimal transmission efficiency is investigated. We deduce Tthe design criteria are deduced to meet various conditions of transmission distance and load. The results of simulation and experiment show the transmission efficiency from transmitting coil to receiving coil can keep high when the corresponding optimal condition is achieved. Thusen it can improve the overall transmission efficiency of the WPT system and it makes it suitable for the practical application of WPT system.
Exploring optimal combination of a file system and an I/O scheduler for underlying solid state disks
Institute of Scientific and Technical Information of China (English)
Hui SUN; Xiao QIN; Chang-sheng XIE
2014-01-01
Performance and energy consumption of a solid state disk (SSD) highly depend on fi le systems and I/O schedulers in operating systems. To fi nd an optimal combination of a fi le system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator (AI), which is the ratio of SSD performance value (e.g., data transfer rate in MB/s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a fi le system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD (SSD-I) when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to fi nd the combination of a fi le system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads (i.e., varmail, fi leserver, and webserver), and explore optimal combinations of fi le systems and I/O schedulers (i.e., optimal values of AI) for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a fi le system and an I/O scheduler for SSDs, compared with an individual metric.
Simulation and real-time optimal scheduling: a framework for integration
Energy Technology Data Exchange (ETDEWEB)
Macal, C.M.; Nevins, M.R. [Argonne National Lab., IL (United States); Williams, M.K.; Joines, J.C. [Military Traffic Management Command Transportation Engineering Agency, Newport News, VA (United States)
1997-02-01
Traditional scheduling and simulation models of the same system differ in several fundamental respects. These include the definition of a schedule, the existence of an objective function which orders schedules and indicates the performance of a given schedule according to specific criteria, and the level of fidelity at which the items are represented and processed through he system. This paper presents a conceptual, object-oriented, architecture for combining a traditional, high-level, scheduling system with a detailed, process- level, discrete-event simulation. A multi-echelon planning framework is established in the context of modeling end-to-end military deployments with the focus on detailed seaport operations.
Yang, Yahong; Zhao, Fuqing; Hong, Yi; Yu, Dongmei
2005-12-01
Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.
Optimizing Transmission Service Cost of Khuzestan Regional Grid Based on Nsga-Ii Algorithm
Shooshtari, Alireza Tavakoli; Joorabian, Mahmood; Milani, Armin Ebrahimi; Gholamzadeh, Arash
2011-06-01
Any plan for modeling the components of transmission service costs should be able to consider congestion as well as loss cost. Assessing the real value of congestion and loss costs in each network has a substantial contribution to analyze the grid's weaknesses in order to release capacity of power network. As much as the amount of congestion and loss costs in the transmission grid reduces the amount of power passing through transmission lines increases. Therefore, the transmission service cost will be optimized and revenues of the regional electricity company from transmission services will be increased. In this paper, a new power flow algorithm with congestion and loss considerations of a power network is presented. Thus, optimal power flow and a multi-objectives optimization algorithm, called NSGA-II, is used in this work. The real data of Khuzestan regional power grid is implemented to confirm the efficiency of proposed method.
Optimizing link efficiency for gated DPCCH transmission on HSUPA
DEFF Research Database (Denmark)
Zarco, Carlos Ruben Delgado; Wigard, Jeroen; Kolding, T. E.
2007-01-01
To minimize the terminal's transmission power in bursty uplink traffic conditions, the evolved High-Speed Uplink Packet Access (HSUPA) concept in 3GPP WCDMA includes a feature known as Dedicated Physical Control Channel (DPCCH) gating. We present here a detailed link level study of gating from...... a link efficiency (LE) perspective; LE being expressed in bits per second per Watt. While the overall gain mechanisms of gating are well known, we show how special challenges related to discontinuous Enhanced Dedicated Channel (E-DCH) transmission can be addressed for high link and system performance. We...... consider the E-DCH performance degradation caused by gating on other radio procedures relying on the DPCCH, such as inner and outer loop power control. Our studies show that gating is beneficial for both for 2 and 10 ms transmission time intervals. The gains in terms of LE with a Vehicular A 30 kmph...
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.
Optimal Rate Based Image Transmission Scheme in Multi-rate Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Mr. Jayachandran.A ,
2011-06-01
Full Text Available In image transmission application over WSN energy efficiency and image quality are both important factor for joint optimization. The large size image transmission cause bottleneck in WSN due to the limited energy resources and network capacity. Since some sensor are in similar viewing directions the images they are capture likely exhibit certain level of correlation among themselves. This optimization scheme allows each image sensor to transmit optimal functions of the overlapped images through appropriate multiple rate oriented routing paths. Moreover, we use unused segment loss protection with erasure codes of different strength to maximize the expected quality at the destination and propose a fast algorithm that find nearly optimal transmission strategies simulation results show that proposed the scheme achieves high energy efficiency in WSN enhancing the image transmission quality.
Islam, Md Rafiqul; Mahmud, Abdullah Al; Islam, Md Saiful; Babu, Hafiz Md Hasan
2010-01-01
This paper describes an integrated framework for SOC test automation. This framework is based on a new approach for Wrapper/TAM co-optimization based on rectangle packing considering the diagonal length of the rectangles to emphasize on both TAM widths required by a core and its corresponding testing time. In this paper, we propose an efficient algorithm to construct wrappers that reduce testing time for cores. We then use rectangle packing to develop an integrated scheduling algorithm that incorporates power constraints in the test schedule. The test power consumption is important to consider since exceeding the system's power limit might damage the system.
Efficiency-aware and fairness-aware joint-layer optimization for downlink data scheduling in OFDM
Institute of Scientific and Technical Information of China (English)
GUO KunQi; SUN LiXin; WANG Ping; JIA ShiLou
2008-01-01
Efficiency and fairness are two crucial issues to be considered for resource alloca-tion in multi-user wireless networks. Based on the joint optimization of physical layer and data link layer, an optimization model is derived to achieve efficient and fair downiink data scheduling in multi-user OFDM wireless networks by maximizing the total utility function with respect to the average waiting time of user queue. A dynamic sub-carrier allocation algorithm (DSAA) based on the optimization model is proposed in order to obtain the maximization of the total scheduling utility. Effi-ciency is improved by combining DSAA with time scale Interference predictor (TSIP) which at large time scales predict ON/OFF period of user data with temporal corre-lation structure across multiple time scales in multi-user interference environment. Simulation results verify the efficiency and fairness of the scheme.
Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng
2016-01-01
Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.
A Hybrid Search Algorithm for Midterm Optimal Scheduling of Thermal Power Plants
Directory of Open Access Journals (Sweden)
Shengli Liao
2015-01-01
Full Text Available A hybrid search algorithm consisting of three stages is presented to solve the midterm schedule for thermal power plants (MTSFTPP problem, where the primary objective is to achieve equal accumulated operating hours of installed capacity (EAOHIC for all thermal power plants during the selected period. First, feasible spaces are produced and narrowed based on constraints on the number of units and power load factors. Second, an initial feasible solution is obtained by a heuristic method that considers operating times and boundary conditions. Finally, the progressive optimality algorithm (POA, which we refer to as the vertical search algorithm (VSA, is used to solve the MTSFTPP problem. A method for avoiding convergence to a local minimum, called the lateral search algorithm (LSA, is presented. The LSA provides an updated solution that is used as a new feasible starting point for the next search in the VSA. The combination of the LSA and the VSA is referred to as the hybrid search algorithm (HSA, which is simple and converges quickly to the global minimum. The results of two case studies show that the algorithm is very effective in solving the MTSFTPP problem accurately and in real time.
Optimal RTP Based Power Scheduling for Residential Load in Smart Grid
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 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.
Optimization for vehicle scheduling in iron and steel works based on semi-trailer swap transport
Institute of Scientific and Technical Information of China (English)
CHENG Yao-rong; LIANG Bo; ZHOU Mei-hua
2010-01-01
In order to solve internal logistics problems of iron and steel works,such as low transportation efficiency of cehicles and high transportation cost,the production process and traditional transportation style of iron and steel work were introduced.The internal transport tasks of iron and steel works were grouped based on cluster analysis according to demand time of the transportation. An improved vehicle scheduling model of semi-trailer swap transport among loading nodes and unloading nodes in one task group was set up.The algorithm was designed to silve the vehicle routing problem with simultaneous pick-up and delivery (CRPSPD)problem based on semi-trailer swap transport.A solving program was written by MATLAB software and the method to figure out the optimal path of each grouping was obtainde.The dropping and pulling transportation plan of the tractor wan designed.And an decerase the numbers of semi-trailer swap transport in iron and steel works was given.The results indicate that semi-trailer swap transport can steel works,and the total distance traveled reduces by 43.5%.The semi-trailer swap transport can help the iron and steel works develop the productiong in intension.
Institute of Scientific and Technical Information of China (English)
Jian Qiu; Naiyuan Tian; Zhixin Lu; Guowei Sun
2003-01-01
The transfer of mass flow between ironmaking and steelmaking process at Baoshan Iron and Steel Co. Ltd. has been analyzed. The mathematic-physical models of transport scheduling for hot metal manufacturing have been researched combined with the practical problem in the metallurgical manufacture procedure. Taking into account these models, the scheduling software has been designed, programmed and tested on-line. The new automation system of production scheduling has been implemented successfully at Baosteel, which produces a great economic benefit.
Fuzzy Optimization of Construction Engineering Project Schedule Based on Critical Chain Management
Liu, Jianbing; Ren, Hong; Junshu DU
2013-01-01
Critical chain management was the very important theory innovation in the development of construction project schedule management field in recent years. Compared with the traditional methods of construction project schedule management, the methods of critical chain management considered resource constraints into construction project schedule management, the construction project cycle was shortened and the efficiency was enhanced by using the dynamic thinking and circulation pattern to manage ...
A Study on the optimal reclosing method of the transmission and distribution line
Energy Technology Data Exchange (ETDEWEB)
Kim, I.D.; Han, K.N. [Korea Electric Power Research Institute, Taejeon (Korea, Republic of)
1998-09-01
We studied Auto-Reclosing(AR) schemes of the transmission and distribution line in the various power system. Major results of this study are ; - Investigation on the overseas AR schemes - Analysis of existing AR schemes in KOREA - Optimal reclosing method of the transmission and distribution line - Study of 765kV AR scheme related in HSGS. (author). 142 refs., 113 figs., 37 tabs.
Optimal Dispersion of NZ-DSF in 40 Gbit/s DWDM Transmission
Institute of Scientific and Technical Information of China (English)
M. Tanaka; J. Kobayashi; T. Okuno; E. Sasaoka; Y. Araya; E. Tsumura; M. Shigematsu
2003-01-01
Numerical and experimental verification of optimal dispersion of NZ-DSF in 40Gb/s-based DWDM transmission are performed using 240km simple transmission line. The results show that dispersion of +8ps/nm/km is effective in 40Gb/s as well as 10Gb/s.
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
This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running...... 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...
Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm
Directory of Open Access Journals (Sweden)
Lingna He
2012-09-01
Full Text Available In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the specific implementation for cloud resources scheduling . And in CloudSim simulation environment and simulation experiments, the results show that the algorithm has better scheduling performance and load balance than general algorithm.
Optimal Encoding of Data in Data Transmission Channels
Directory of Open Access Journals (Sweden)
Silviu Draghici
2013-01-01
Full Text Available This paper aims to present the methods of achieving an optimal encoding in the data communication channels. After a short description of the communication channel and of the data communication channel types, follow briefly a few notions of the data channel enthropy, information, transinformation, with their properties, definitions and mathematical relations connecting them. Chapter 2 presents the concept of optimal code, following a detailed description (using two suggestive examples of the two main methods used to obtain an optimal code: Shannon-Fano and Huffman.
Directory of Open Access Journals (Sweden)
Haihua Zhu
2016-01-01
Full Text Available To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination relationships evaluation methodology are proposed for the purpose of reaching balance among kinematic performance, dynamic performance, and other performances of the multilink transmission mechanism during the conceptual design phase. Based on the traditional evaluation index of single target of multicriteria design optimization, the robust metrics of the mechanism system and preference metrics of decision-maker are taken into consideration in this preference polyhedron model and reflected by geometrical characteristic of the model. At last, two optimized multilink transmission mechanisms are designed based on the proposed preference polyhedron model with different evolutionary algorithms, and the result verifies the validity of the proposed optimization method.
Directory of Open Access Journals (Sweden)
M. Saravanan
2014-03-01
Full Text Available A Hybrid flow shop scheduling is characterized ‘n’ jobs ‘m’ machines with ‘M’ stages by unidirectional flow of work with a variety of jobs being processed sequentially in a single-pass manner. The paper addresses the multi-stage hybrid flow shop scheduling problems with missing operations. It occurs in many practical situations such as stainless steel manufacturing company. The essential complexity of the problem necessitates the application of meta-heuristics to solve hybrid flow shop scheduling. The proposed Simulated Annealing algorithm (SA compared with Particle Swarm Optimization (PSO with the objective of minimization of makespan. It is show that the SA algorithm is efficient in finding out good quality solutions for the hybrid flow shop problems with missing operations.
Trunfio, Roberto
2015-06-01
In a recent article, Guo, Cheng and Wang proposed a randomized search algorithm, called modified generalized extremal optimization (MGEO), to solve the quay crane scheduling problem for container groups under the assumption that schedules are unidirectional. The authors claim that the proposed algorithm is capable of finding new best solutions with respect to a well-known set of benchmark instances taken from the literature. However, as shown in this note, there are some errors in their work that can be detected by analysing the Gantt charts of two solutions provided by MGEO. In addition, some comments on the method used to evaluate the schedule corresponding to a task-to-quay crane assignment and on the search scheme of the proposed algorithm are provided. Finally, to assess the effectiveness of the proposed algorithm, the computational experiments are repeated and additional computational experiments are provided.
Magnetic shielding structure optimization design for wireless power transmission coil
Dai, Zhongyu; Wang, Junhua; Long, Mengjiao; Huang, Hong; Sun, Mingui
2017-09-01
In order to improve the performance of the wireless power transmission (WPT) system, a novel design scheme with magnetic shielding structure on the WPT coil is presented in this paper. This new type of shielding structure has great advantages on magnetic flux leakage reduction and magnetic field concentration. On the basis of theoretical calculation of coil magnetic flux linkage and characteristic analysis as well as practical application feasibility consideration, a complete magnetic shielding structure was designed and the whole design procedure was represented in detail. The simulation results show that the coil with the designed shielding structure has the maximum energy transmission efficiency. Compared with the traditional shielding structure, the weight of the new design is significantly decreased by about 41%. Finally, according to the designed shielding structure, the corresponding experiment platform is built to verify the correctness and superiority of the proposed scheme.
Institute of Scientific and Technical Information of China (English)
ZHAO Zhen-shan; XU Guo-zhi
2007-01-01
In real multiple-input multiple-output (MIMO) systems, the perfect channel state information (CSI) may be costly or impossible to acquire. But the channel statistical information can be considered relatively stationary during long-term transmission.The statistical information can be obtained at the receiver and fed back to the transmitter and do not require frequent update. By exploiting channel mean and covariance information at the transmitter simultaneously, this paper investigates the optimal transmission strategy for spatially correlated MIMO channels. An upper bound of ergodic capacity is derived and taken as the performance criterion. Simulation results are also given to show the performance improvement of the optimal transmission strategy.
Indian Academy of Sciences (India)
J Rakshit; P K Das
2000-08-01
A time–temperature schedule for formation of silicon–nitride by direct nitridation of silicon compact was optimized by kinetic study of the reaction, 3Si + 2N2 = Si3N4 at four different temperatures (1250°C, 1300°C, 1350°C and 1400°C). From kinetic study, three different temperature schedules were selected each of duration 20 h in the temperature range 1250°–1450°C, for complete nitridation. Theoretically full nitridation (100% i.e. 66.7% weight gain) was not achieved in the product having no unreacted silicon in the matrix, because impurities in Si powder and loss of material during nitridation would result in 5–10% reduction of weight gain. Green compact of density < 66% was fully nitrided by any one of the three schedules. For compact of density > 66%, the nitridation schedule was maneuvered for complete nitridation. Iron promotes nitridation reaction. Higher weight loss during nitridation of iron doped compact is the main cause of lower nitridation gain compared to undoped compact in the same firing schedule. Iron also enhances the amount of -Si3N4 phase by formation of low melting FeSi phase.
Institute of Scientific and Technical Information of China (English)
Wei-Neng Chen; Jun Zhang
2012-01-01
Project scheduling under uncertainty is a challenging field of research that has attracted increasing attention.While most existing studies only consider the single-mode project scheduling problem under uncertainty,this paper aims to deal with a more realistic model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows (S-MRCPSPDCF).In the model,activity durations and costs are given by random variables.The objective is to find an optimal baseline schedule so that the expected net present value (NPV) of cash flows is maximized.To solve the problem,an ant colony system (ACS) based approach is designed.The algorithm dispatches a group of ants to build baseline schedules iteratively using pheromones and an expected discounted cost (EDC) heuristic.Since it is impossible to evaluate the expected NPV directly due to the presence of random variables,the algorithm adopts the Monte Carlo (MC)simulation technique.As the ACS algorithm only uses the best-so-far solution to update pheromone values,it is found that a rough simulation with a small number of random scenarios is enough for evaluation.Thus the computational cost is reduced.Experimental results on 33 instances demonstrate the effectiveness of the proposed model and the ACS approach.
Sahelgozin, M.; Alimohammadi, A.
2015-12-01
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.
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.
Energy-Efficient Transmission Strategy by Using Optimal Stopping Approach for Mobile Networks
Directory of Open Access Journals (Sweden)
Ying Peng
2016-01-01
Full Text Available In mobile networks, transmission energy consumption dominates the major part of network energy consumption. To reduce energy consumption for data transmission is an important topic for constructing green mobile networks. According to Shannon formula, when the transmission power is constant, the better the channel quality is, the greater the transmission rate is. Then, more data will be delivered in a given period. And energy consumption per bit data transmitted will be reduced. Because channel quality varies with time randomly, it is a good opportunity for decreasing energy consumption to deliver data in the best channel quality. However, data has delay demand. The sending terminal cannot wait for the best channel quality unlimitedly. Actually, sending terminal has to select an optimal time to deliver data before data exceeds delay. For this, this paper obtains the optimal transmission rate threshold at each detection slot time by using optimal stopping approach. Then, sending terminal determines whether current time is the optimal time through comparing current transmission rate with the corresponding rate threshold, thus realizing energy-efficient transmission strategy, so as to decrease average energy consumption per bit data transmitted.
Directory of Open Access Journals (Sweden)
Mika Oki
2011-10-01
Full Text Available BACKGROUND: Dengue infection is endemic in many regions throughout the world. While insecticide fogging targeting the vector mosquito Aedes aegypti is a major control measure against dengue epidemics, the impact of this method remains controversial. A previous mathematical simulation study indicated that insecticide fogging minimized cases when conducted soon after peak disease prevalence, although the impact was minimal, possibly because seasonality and population immunity were not considered. Periodic outbreak patterns are also highly influenced by seasonal climatic conditions. Thus, these factors are important considerations when assessing the effect of vector control against dengue. We used mathematical simulations to identify the appropriate timing of insecticide fogging, considering seasonal change of vector populations, and to evaluate its impact on reducing dengue cases with various levels of transmission intensity. METHODOLOGY/PRINCIPAL FINDINGS: We created the Susceptible-Exposed-Infectious-Recovered (SEIR model of dengue virus transmission. Mosquito lifespan was assumed to change seasonally and the optimal timing of insecticide fogging to minimize dengue incidence under various lengths of the wet season was investigated. We also assessed whether insecticide fogging was equally effective at higher and lower endemic levels by running simulations over a 500-year period with various transmission intensities to produce an endemic state. In contrast to the previous study, the optimal application of insecticide fogging was between the onset of the wet season and the prevalence peak. Although it has less impact in areas that have higher endemicity and longer wet seasons, insecticide fogging can prevent a considerable number of dengue cases if applied at the optimal time. CONCLUSIONS/SIGNIFICANCE: The optimal timing of insecticide fogging and its impact on reducing dengue cases were greatly influenced by seasonality and the level of
Optimal Value of Series Capacitors for Uniform Field Distribution in Transmission Line MRI Coils
DEFF Research Database (Denmark)
Zhurbenko, Vitaliy
2016-01-01
Transmission lines are often used as coils in high field magnetic resonance imaging (MRI). Due to the distributed nature of transmission lines, coils based on them produce inhomogeneous field. This work investigates application of series capacitors to improve field homogeneity along the coil....... The equations for optimal values of evenly distributed capacitors are derived and expressed in terms of the implemented transmission line parameters.The achieved magnetic field homogeneity is estimated under quasistatic approximation and compared to the regular transmission line resonator. Finally, a more...... practical case of a microstrip line coil with two series capacitors is considered....
Optimization model of vaccination strategy for dengue transmission
Widayani, H.; Kallista, M.; Nuraini, N.; Sari, M. Y.
2014-02-01
Dengue fever is emerging tropical and subtropical disease caused by dengue virus infection. The vaccination should be done as a prevention of epidemic in population. The host-vector model are modified with consider a vaccination factor to prevent the occurrence of epidemic dengue in a population. An optimal vaccination strategy using non-linear objective function was proposed. The genetic algorithm programming techniques are combined with fourth-order Runge-Kutta method to construct the optimal vaccination. In this paper, the appropriate vaccination strategy by using the optimal minimum cost function which can reduce the number of epidemic was analyzed. The numerical simulation for some specific cases of vaccination strategy is shown.
OPTIMIZATION OF TRANSIENT FEEDING TO PARALLEL-PLATE TRANSMISSION LINES FROM COAXIAL LINE
Institute of Scientific and Technical Information of China (English)
Tian Chunming; Wang Jianguo; Meng Fenxia; Zhang Maoyu; Ge Debiao
2001-01-01
The transient feeding to parallel-plate transmission lines from coaxial line is optimized by using the Finite-Difference Time-Domain (FDTD) method and a simple FDTD feed model. Observing the reflected voltages, this letter presents the optimal feeding position and ratio of width to height for a given input impedance of the coaxial line.
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.
Hybrid static/dynamic scheduling for already optimized dense matrix factorization
Donfack, Simplice; Gropp, William D; Kale, Vivek
2011-01-01
We present the use of a hybrid static/dynamic scheduling strategy of the task dependency graph for direct methods used in dense numerical linear algebra. This strategy provides a balance of data locality, load balance, and low dequeue overhead. We show that the usage of this scheduling in communication avoiding dense factorization leads to significant performance gains. On a 48 core AMD Opteron NUMA machine, our experiments show that we can achieve up to 64% improvement over a version of CALU that uses fully dynamic scheduling, and up to 30% improvement over the version of CALU that uses fully static scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic scheduling approach is up to 8% faster than the version of CALU that uses a fully static scheduling or fully dynamic scheduling. Our algorithm leads to speedups over the corresponding routines for computing LU factorization in well known libraries. On the 48 core AMD NUMA machine, our best implementation is up to 110% faster than MKL, while on...
Directory of Open Access Journals (Sweden)
S. T. Jaya Christa
2006-06-01
Full Text Available This paper deals with the optimal location and parameters of Unified Power Flow Controllers (UPFCs in electrical power systems, using particle swarm optimization (PSO. The objective is to maximize the transmission system loadability subject to the transmission line capacity limits and specified bus voltage levels. Using the proposed method, the location of UPFCs and their parameters are optimized simultaneously. PSO is used to solve the above non-linear programming problem for better accuracy. The proposed approach is examined and tested on IEEE 30-bus system and IEEE 118-bus system. The results obtained are quite promising for the power system operation environment
Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies
Ozel, Omur; Yang, Jing; Ulukus, Sennur; Yener, Aylin
2011-01-01
Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. ...
Improved Data Transmission Scheme of Network Coding Based on Access Point Optimization in VANET
Directory of Open Access Journals (Sweden)
Zhe Yang
2014-01-01
Full Text Available VANET is a hot spot of intelligent transportation researches. For vehicle users, the file sharing and content distribution through roadside access points (AP as well as the vehicular ad hoc networks (VANET have been an important complement to that cellular network. So the AP deployment is one of the key issues to improve the communication performance of VANET. In this paper, an access point optimization method is proposed based on particle swarm optimization algorithm. The transmission performances of the routing protocol with random linear network coding before and after the access point optimization are analyzed. The simulation results show the optimization model greatly affects the VANET transmission performances based on network coding, and it can enhance the delivery rate by 25% and 14% and reduce the average delay of transmission by 38% and 33%.
DPSK-3ASK transmission optimization by adapting modulation levels
Eiselt, Michael H.; Teipen, Brian T.
2008-11-01
For metro and regional 100-Gbps transmission, a transparent channel reach of 500-600 km is required and a 100-GHz channel grid is typically used. For these applications, a cost effective modulation format is introduced which can make use of electronic components designed for the already established 40-Gbps market, bypassing the requirements for novel electronic developments and therefore reducing the component cost. With this DPSK-3ASK modulation format, five information bits are transmitted in two consecutive symbols, leading to a symbol rate of 45 Gbaud, including overhead for framing and FEC. To minimize hardware requirements and to create a cost-effective solution, a single Mach-Zehnder modulator can be used to create the optical DPSK-3ASK signal after combining the phase and amplitude modulation signals into a 6-level modulator drive voltage. In this paper, it is demonstrated by numerical simulations that these voltage levels can be modified to adapt to varying signal distortions and thereby yield improved transmission performance. It is shown that by dynamically modifying the modulation levels based on the channel performance, dynamic signal impairments such as the non-linear effects from varying power levels, changes in chromatic dispersion, or varying PMD levels can be mitigated. Error-free performance (with FEC) can be obtained with 24 dB OSNR and 7ps DGD for a 112-Gbps (45-Gbaud) optical signal.
Optimal Placement and Sizing of FACTS Devices to Delay Transmission Expansion
Frolov, Vladimir; Backhaus, Scott; Bialek, Janusz; Chertkov, Michael
2016-01-01
The Transmission System Operators (TSOs) plan to reinforce their transmission grids based on the projected system congestion caused by the increase in future system load, amongst other factors. However, transmission expansion is severely limited in many countries, and especially in Europe, due to many factors such as clearance acquisitions. An alternative is to use Flexible Alternating Current Transmission System (FACTS) devices, thus allowing to delay or avoid much more expensive transmission expansion. These devices are capable of utilizing the existing transmission grid more flexibly. However, the locations and sizing of future FACTS devices must be carefully determined during the planning phase in order to avoid congestion under many representative scenarios of the projected economic growth (of loads). This paper proposes an optimization approach, based on AC Power Flows, to determine suitable locations and sizes of series and shunt FACTS devices. Non-linear, non-convex and multiple-scenario based optimiz...
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 and Fair Resource Allocation for Multiuser Wireless Multimedia Transmissions
Directory of Open Access Journals (Sweden)
Guan Zhangyu
2009-01-01
Full Text Available This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY and Application (APP layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.
The Design and Optimization of an Integrated Arrival/Departure Scheduler Project
National Aeronautics and Space Administration — Intelligent Automation, Inc. (IAI) proposes the design and validation of a dynamic integrated arrival/departure scheduler. In contrast to current approaches, we...
The Extension Of Torque Scheduler Allowing The Use Of Planning And Optimization In Grids
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Vaclav Chlumsky
2012-01-01
Full Text Available In this work we present a major extension of the open source TORQUE Resource Manager system. We have replaced a naive scheduler provided in the TORQUE distribution with complex scheduling system that allows to plan job execution ahead and predict the behavior of the system. It is based on the application of job schedule, which represents the jobs’ execution plan. Such a functionality is very useful as the plan can be used by the users to see when and where their jobs will be executed. Moreover, created plans can be easily evaluated in order to identify possible ineﬃciencies. Then, repair actions can be taken immediately and the ineﬃciencies can be ﬁxed, producing better schedules with respect to considered criteria.
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
This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running...... 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...... rescheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case...
Optimization of the transmission mechanism for wireless TCP
Institute of Scientific and Technical Information of China (English)
QU Zhao-wei; ZHANG Juan
2008-01-01
A novel method which can detect the possible handoffsfor mobile nodes is proposed in this article. The method can bedeployed on mobile nodes. So when a possible handoff occurs,mobile nodes can detect and take some necessary actions intime. In this study, the method is also used to optimize thetransmission mechanism of Q-TCP. Moreover, the simulationresults show that Q-TCP can provide better quality of service(QoS) for mobile nodes when they are moving among differentagents.
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
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
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
2014-09-01
Fire Rescue,” St. Anthony Hospitals, October 2006. 17 Leeanna Mims , “Overtime Cost Reduction with Alternative Work Schedules” Executive Fire Officer...Effectiveness: 24-hour schedules may lead to sleep deprivation when an employee’s cognitive skills are inhibited.50 Sleep deprivation may result 48 Mims ...Consecutive Hour Work Limit.” 56 Mims , “Overtime Cost Reduction with Alternative Work Schedules.” 37
Task scheduling of parallel programs to optimize communications for cluster of SMPs
Institute of Scientific and Technical Information of China (English)
郑纬民; 杨博; 林伟坚; 李志光
2001-01-01
This paper discusses the compile time task scheduling of parallel program running on cluster of SMP workstations. Firstly, the problem is stated formally and transformed into a graph partition problem and proved to be NP-Complete. A heuristic algorithm MMP-Solver is then proposed to solve the problem. Experiment result shows that the task scheduling can reduce communication overhead of parallel applications greatly and MMP-Solver outperforms the existing algorithms.
On the Optimal Scheduling of Independent, Symmetric and Time-Sensitive Tasks
Iannello, Fabio; Spagnolini, Umberto
2011-01-01
Consider a discrete-time system in which a centralized controller (CC) is tasked with assigning at each time interval (or slot) K resources (or servers) to K out of M>=K nodes. The M nodes execute tasks that are independently generated at each node by stochastically symmetric and memoryless random processes. The tasks are stored by each node in a finite-capacity task queue, and they are time-sensitive in the sense that within each slot there is a non-zero probability that a task expires before being scheduled. The scheduling problem is tackled with the aim of maximizing the number of tasks completed over time (or task-throughput) under the assumption that the CC has no direct access to the state of the task queues. The scheduling decisions at the CC are based on the outcomes of previous scheduling commands, and on the known statistical properties of the task generation and expiration processes. Overall, the scheduling problem considered herein is general. Practical applications include the scheduling of packe...
Optimal information transmission in organizations: search and congestion
Energy Technology Data Exchange (ETDEWEB)
Arenas, A.; Cabrales, A.; Danon, L.; Diaz-Guilera, A.; Guimera, R.; Vega-Redondo, F.
2008-01-01
We propose a stylized model of a problem-solving organization whose internal communication structure is given by a fixed network. Problems arrive randomly anywhere in this network and must find their way to their respective specialized solvers by relying on local information alone. The organization handles multiple problems simultaneously. For this reason, the process may be subject to congestion. We provide a characterization of the threshold of collapse of the network and of the stock of floating problems (or average delay) that prevails below that threshold. We build upon this characterization to address a design problem: the determination of what kind of network architecture optimizes performance for any given problem arrival rate. We conclude that, for low arrival rates, the optimal network is very polarized (i.e. star-like or centralized), whereas it is largely homogeneous (or decentralized) for high arrival rates. These observations are in line with a common transformation experienced by information-intensive organizations as their work flow has risen in recent years.
Scheduling optimization based on genetic algorithm%基于遗传算法的排产优化方法
Institute of Scientific and Technical Information of China (English)
韩志甲; 邓海峡; 李晓平
2014-01-01
For-the-single-and-small-batch-job-shop-scheduling-problem,-an-encoding-method-based-on-genetic-algorithm-is-proposed.-A-new-fitness-function-algorithm-that-can-deal-with-the-relationship-of-the-part-and-assembly-is-presented.-The-realization-method-and-results-of-genetic-algorithm-in-the-job-shop-optimization-scheduling-are-introduced,-mainly-focusing-on-the-encoding-method-and-the-design-of-fitness-function-algorithm.-Based-on-this-algorithm,-a-kind-of-software-for-production-plan-scheduling-is-developed-to-realize-intelligent-process-scheduling.-It-can-automatically-realize-the-forward-and-backward-plan.%针对单件小批量生产车间的优化排产问题，采用遗传算法进行研究，设计了一种分组编码方案，提出了可处理零部件间装配关系的适应度函数算法。介绍了遗传算法在车间优化排产中的实现方法及结果，重点讨论了编码方案及适应度函数设计。基于此算法，开发了生产作业计划排产软件，可完成顺排产与倒排产的自动排产，实现工艺排产的智能化。
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.
Directory of Open Access Journals (Sweden)
Bokyung Ko
2013-12-01
Full Text Available The increasing penetration of distributed generation (DG sources in low-voltage grid feeders causes problems concerning voltage regulation. The penetration of DG sources such as photovoltaics (PVs in the distribution system can significantly impact the power flow and voltage conditions on the customer side. As the DG sources are more commonly connected to low-voltage distribution systems, voltage fluctuations in the distribution system are experienced because of the DG fluctuation and uncertainty. Therefore, the penetration of DGs in distribution systems is often limited by the required operating voltage ranges. By using an energy storage system (ESS, voltage fluctuation can be compensated for, thus satisfying the voltage regulation requirements. This paper presents an ESS scheduling algorithm based on the power injection data obtained from a smart metering system. The proposed ESS scheduling algorithm is designed for use within a direct current (DC distribution grid, which comprises customers, each with a PV and an ESS system. The purpose of this ESS scheduling algorithm is to optimize the ESS scheduling by considering the complementary operation among all the ESSs.
Directory of Open Access Journals (Sweden)
Vinícius Correa Damaso
2009-12-01
Full Text Available The use of stochastic point processes to model the reliability of repairable systems has been a regular approach to establish survival measures in failure versus repair scenarios. However, the traditional processes do not consider the actual state in which an item returns to operational condition. The traditional renewal process considers an "as-good-as-new" philosophy, while a non-homogeneous Poisson process is based on the minimal repair concept. In this work, an approach based on the concept of Generalized Renewal Process (GRP is presented, which is a generalization of the renewal process and the non-homogeneous Poisson process. A stochastic modeling is presented for systems availability analysis, including testing and/or preventive maintenances scheduling. To validate the proposed approach, it was performed a case study of a hypothetical auxiliary feed-water system of a nuclear power plant, using genetic algorithm as optimization tool.O uso de processos estocásticos pontuais para modelar a confiabilidade de sistemas reparáveis tem sido constante para estabelecer medidas de sobrevivência em cenários de falha versus reparo. Entretanto, os processos tradicionais não consideram o real estado no qual um item retorna à condição operacional. O processo de renovação tradicional considera uma filosofia de "tão-bom-quanto-novo", enquanto um processo não-homogêneo de Poisson é baseado no conceito de reparo mínimo. Neste trabalho, é apresentada uma abordagem baseada no conceito de Processo de Renovação Generalizado (PRG, que é uma generalização de processo de renovação e de processo não-homogêneo de Poisson. Uma modelagem estocástica será apresentada para análise de disponibilidade de sistemas, incluindo planejamento de testes e/ou manutenção preventiva. Para validar a abordagem proposta, um estudo de caso foi desenvolvido para um hipotético sistema auxiliar de água de alimentação de uma usina nuclear, usando algoritmo
Modeling and optimization of transmission and processing of data in an information computer network
Nekrasova, A.; Boriev, Z.; Nyrkov, A.; Sokolov, S.
2016-04-01
The paper presents a comparative analysis of the routing algorithms that allows optimizing the process of transmission and processing of data in information computer networks. A special attention is paid to multipath methods of data transmission coupled with the number of operations necessary for their performance. In addition the authors have raised the question of a linear programming method for the purpose of the solution of the above-mentioned problem.
Granja, C; Almada-Lobo, B; Janela, F; Seabra, J; Mendes, A
2014-12-01
As patient's length of stay in waiting lists increases, governments are looking for strategies to control the problem. Agreements were created with private providers to diminish the workload in the public sector. However, the growth of the private sector is not following the demand for care. Given this context, new management strategies have to be considered in order to minimize patient length of stay in waiting lists while reducing the costs and increasing (or at least maintaining) the quality of care. Appointment scheduling systems are today known to be proficient in the optimization of health care services. Their utilization is focused on increasing the usage of human resources, medical equipment and reducing the patient waiting times. In this paper, a simulation-based optimization approach to the Patient Admission Scheduling Problem is presented. Modeling tools and simulation techniques are used in the optimization of a diagnostic imaging department. The proposed techniques have demonstrated to be effective in the evaluation of diagnostic imaging workflows. A simulated annealing algorithm was used to optimize the patient admission sequence towards minimizing the total completion and total waiting of patients. The obtained results showed average reductions of 5% on the total completion and 38% on the patients' total waiting time. Copyright © 2014 Elsevier Inc. All rights reserved.
Optimal control of an influenza model with seasonal forcing and age-dependent transmission rates.
Lee, Jeehyun; Kim, Jungeun; Kwon, Hee-Dae
2013-01-21
This study considers an optimal intervention strategy for influenza outbreaks. Variations in the SEIAR model are considered to include seasonal forcing and age structure, and control strategies include vaccination, antiviral treatment, and social distancing such as school closures. We formulate an optimal control problem by minimizing the incidence of influenza outbreaks while considering intervention costs. We examine the effects of delays in vaccine production, seasonal forcing, and age-dependent transmission rates on the optimal control and suggest some optimal strategies through numerical simulations.
Sadek, Mirette
2011-05-01
In MIMO-OFDM multiuser systems, user scheduling is employed as a means of multiple access. In a downlink scenario, users that share the same subcarriers of an OFDM symbol are separated through precoding in order to achieve space division multiple access (SDMA). User scheduling techniques rely on channel knowledge at the transmitter, namely, the so-called channel quality indicator (CQI). In this paper, we implement a leakage-based precoding algorithm whose purpose is twofold. First, it is used to compute a reliable CQI based on a group of precoding vectors that are adapted to the channel. Then, it implements user scheduling through using the optimum vectors for precoding, thus minimizing interference among users. We also introduce the concept of resource block size adaptivity. The resource block (RB) is defined as the least unit in an OFDM symbol that a user can be assigned to. We propose a variable RB size that adapts to the channel conditions. © 2011 IEEE.
Karumbu, Premkumar; Leith, Douglas J
2011-01-01
We consider multi--hop networks comprising Binary Symmetric Channels ($\\mathsf{BSC}$s). The network carries unicast flows for multiple users. The utility of the network is the sum of the utilities of the flows, where the utility of each flow is a concave function of its throughput. Given that the network capacity is shared by the flows, there is a contention for network resources like coding rate (at the physical layer), scheduling time (at the MAC layer), etc., among the flows. We propose a proportional fair transmission scheme that maximises the sum utility of flow throughputs subject to the rate and the scheduling constraints. This is achieved by {\\em jointly optimising the packet coding rates of all the flows through the network}.
Elrazek, Abd; Amer, Mohamed; El-Hawary, Bahaa; Salah, Altaher; Bhagavathula, Akshaya S; Alboraie, M; Saab, Samy
2017-04-01
Neonates born to hepatitis C virus (HCV)-positive mothers are usually not screened for HCV. Unscreened children may act as active sources for social HCV transmission, and factors contributing for vertical HCV transmitting still remained controversial and needed optimization. We aimed to investigate the factors contributing for vertical HCV transmission in Egypt; the highest HCV prevalence worldwide. We prospectively followed the neonates born to HCV-positive mother in the child-bearing period, to identify mother-to-child transmission (MTCT) factors from January 2015 to March 2016. Data mining computational analysis was used to quantify the findings. Among 3000 randomized pregnant women, prevalence of HCV was 46/3000 (1.53%). HCV vertical transmission was identified in eight neonates (17.39%). Only high viral load identified at 975.000 IU was the predictor risk for MTCT. Hepatitis C virus in pregnancy has substantial risk for vertical HCV transmission: High viral load in HCV-positive women increases the risk of HCV transmission to neonates. Screening pregnant women during early stage of pregnancy and optimizing the HCV viral load in HCV-positive women might prevent vertical HCV transmission to neonates. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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%.
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....
Directory of Open Access Journals (Sweden)
Mahesh S. Narkhede
2015-01-01
Full Text Available An attempt has been made in this article to compare the performances of two multiobjective evolutionary algorithms namely ev-MOGA and GODLIKE. The performances of both are evaluated on risk based optimal power scheduling of virtual power plant. The risk based scheduling is proposed as a conflicting bi objective optimization problem with increased number of durations of day. Both the algorithms are elaborated in detail. Results based on the performance analysis are depicted at the end.
DEFF Research Database (Denmark)
Li, Rui; Roberti, Roberto
2017-01-01
This paper addresses the railway track possession scheduling problem (RTPSP), where a large-scale railway infrastructure project consisting of multiple construction works is to be planned. The RTPSP is to determine when to perform the construction works and in which track possessions while satisf...
DEFF Research Database (Denmark)
Pop, Paul; Eles, Petru; Peng, Zebo
2003-01-01
An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...
Sel, C.; Bilgen, B.; Bloemhof, J.M.; Vorst, van der J.G.A.J.
2015-01-01
This paper considers a dairy industry problem on integrated planning and scheduling of set yoghurt production. A mixed integer linear programming formulation is introduced to integrate tactical and operational decisions and a heuristic approach is proposed to decompose time buckets of the decisions.
Sel, C.; Bilgen, B.; Bloemhof, J.M.; Vorst, van der J.G.A.J.
2015-01-01
This paper considers a dairy industry problem on integrated planning and scheduling of set yoghurt production. A mixed integer linear programming formulation is introduced to integrate tactical and operational decisions and a heuristic approach is proposed to decompose time buckets of the decisions.
Designing robust liner shipping schedules: Optimizing recovery actions and buffer times
J. Mulder (Judith); R. Dekker (Rommert); M. Sharifyazdi (Mehdi)
2012-01-01
textabstractMaritime transport is an important mode of transport in international trade. It is important for liner shipping companies to maintain cost efficient and robust liner shipping networks. Regularly, they set up pro-forma schedules, yet it is difficult to stay on time and we consider the
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 m...
DEFF Research Database (Denmark)
Morais, Hugo; Sousa, Tiago; Perez, Angel
2016-01-01
The need for developing new methodologies in order to improve power system stability has increased due to the recent growth of distributed energy resources. In this paper, the inclusion of a voltage stability index in distributed energy resources scheduling is proposed. Two techniques were used...... resources is presented to analyse the proposed methodology. Additionally, the methodology is tested in a real distribution network....
Optimization-based manufacturing scheduling with multiple resources and setup requirements
Chen, Dong; Luh, Peter B.; Thakur, Lakshman S.; Moreno, Jack, Jr.
1998-10-01
The increasing demand for on-time delivery and low price forces manufacturer to seek effective schedules to improve coordination of multiple resources and to reduce product internal costs associated with labor, setup and inventory. This study describes the design and implementation of a scheduling system for J. M. Product Inc. whose manufacturing is characterized by the need to simultaneously consider machines and operators while an operator may attend several operations at the same time, and the presence of machines requiring significant setup times. The scheduling problem with these characteristics are typical for many manufacturers, very difficult to be handled, and have not been adequately addressed in the literature. In this study, both machine and operators are modeled as resources with finite capacities to obtain efficient coordination between them, and an operator's time can be shared by several operations at the same time to make full use of the operator. Setups are explicitly modeled following our previous work, with additional penalties on excessive setups to reduce setup costs and avoid possible scraps. An integer formulation with a separable structure is developed to maximize on-time delivery of products, low inventory and small number of setups. Within the Lagrangian relaxation framework, the problem is decomposed into individual subproblems that are effectively solved by using dynamic programming with additional penalties embedded in state transitions. Heuristics is then developed to obtain a feasible schedule following on our previous work with new mechanism to satisfy operator capacity constraints. The method has been implemented using the object-oriented programming language C++ with a user-friendly interface, and numerical testing shows that the method generates high quality schedules in a timely fashion. Through simultaneous consideration of machines and operators, machines and operators are well coordinated to facilitate the smooth flow of
Dai, Wanyang
2011-01-01
We design a dynamic rate scheduling policy of Markov type via the solution (a social optimal Nash equilibrium point) to a utility-maximization problem over a randomly evolving capacity set for a class of generalized processor-sharing queues living in a random environment, whose job arrivals to each queue follow a doubly stochastic renewal process (DSRP). Both the random environment and the random arrival rate of each DSRP are driven by a finite state continuous time Markov chain (FS-CTMC). Whereas the scheduling policy optimizes in a greedy fashion with respect to each queue and environmental state and since the closed-form solution for the performance of such a queueing system under the policy is difficult to obtain, we establish a reflecting diffusion with regime-switching (RDRS) model for its measures of performance and justify its asymptotic optimality through deriving the stochastic fluid and diffusion limits for the corresponding system under heavy traffic and identifying a cost function related to the ...
Royston, T. J.; Singh, R.
1996-07-01
While significant non-linear behavior has been observed in many vibration mounting applications, most design studies are typically based on the concept of linear system theory in terms of force or motion transmissibility. In this paper, an improved analytical strategy is presented for the design optimization of complex, active of passive, non-linear mounting systems. This strategy is built upon the computational Galerkin method of weighted residuals, and incorporates order reduction and numerical continuation in an iterative optimization scheme. The overall dynamic characteristics of the mounting system are considered and vibratory power transmission is minimized via adjustment of mount parameters by using both passive and active means. The method is first applied through a computational example case to the optimization of basic passive and active, non-linear isolation configurations. It is found that either active control or intentionally introduced non-linearity can improve the mount's performance; but a combination of both produces the greatest benefit. Next, a novel experimental, active, non-linear isolation system is studied. The effect of non-linearity on vibratory power transmission and active control are assessed via experimental measurements and the enhanced Galerkin method. Results show how harmonic excitation can result in multiharmonic vibratory power transmission. The proposed optimization strategy offers designers some flexibility in utilizing both passive and active means in combination with linear and non-linear components for improved vibration mounts.
Energy Technology Data Exchange (ETDEWEB)
A.Badri; Jadid, S. [Department of Electrical Engineering, Iran University of Science and Technology (Iran); Rashidinejad, M. [Shahid Bahonar University, Kerman (Iran); Moghaddam, M.P. [Tarbiat Modarres University, Tehran (Iran)
2008-06-15
In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)
Robust Secure Transmission in MISO Channels Based on Worst-Case Optimization
Huang, Jing
2011-01-01
This paper studies robust transmission schemes for multiple-input single-output (MISO) wiretap channels. Both the cases of direct transmission and cooperative jamming with a helper are investigated with imperfect channel state information (CSI) for the eavesdropper links. Robust transmit covariance matrices are obtained based on worst-case secrecy rate maximization, under both individual and global power constraints. For the case of an individual power constraint, we show that the non-convex maximin optimization problem can be transformed into a quasiconvex problem that can be efficiently solved with existing methods. For a global power constraint, the joint optimization of the transmit covariance matrices and power allocation between the source and the helper is studied via geometric programming. We also study the robust wiretap transmission problem for the case with a quality-of-service constraint at the legitimate receiver. Numerical results show the advantage of the proposed robust design. In particular, ...
Energy-Constrained Quality Optimization for Secure Image Transmission in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Wei Wang
2007-01-01
Full Text Available Resource allocation for multimedia selective encryption and energy efficient transmission has not been fully investigated in literature for wireless sensor networks (WSNs. In this article, we propose a new cross-layer approach to optimize selectively encrypted image transmission quality in WSNs with strict energy constraint. A new selective image encryption approach favorable for unequal error protection (UEP is proposed, which reduces encryption overhead considerably by controlling the structure of image bitstreams. Also, a novel cross-layer UEP scheme based on cipher-plain-text diversity is studied. In this UEP scheme, resources are unequally and optimally allocated in the encrypted bitstream structure, including data position information and magnitude value information. Simulation studies demonstrate that the proposed approach can simultaneously achieve improved image quality and assured energy efficiency with secure transmissions over WSNs.
A New Approach to Online Scheduling: Approximating the Optimal Competitive Ratio
Günther, Elisabeth; Megow, Nicole; Wiese, Andreas
2012-01-01
We propose a new approach to competitive analysis in online scheduling by introducing the novel concept of online approximation schemes. Such a scheme algorithmically constructs an online algorithm with a competitive ratio arbitrarily close to the best possible competitive ratio for any online algorithm. We study the problem of scheduling jobs online to minimize the weighted sum of completion times on parallel, related, and unrelated machines, and we derive both deterministic and randomized algorithms which are almost best possible among all online algorithms of the respective settings. We also general- ize our techniques to arbitrary monomial cost functions and apply them to the makespan objective. Our method relies on an abstract characterization of online algorithms combined with various simplifications and transformations. We also contribute algorithmic means to compute the actual value of the best possi- ble competitive ratio up to an arbitrary accuracy. This strongly contrasts all previous manually obta...
Directory of Open Access Journals (Sweden)
Jinwei Gu
2015-01-01
Full Text Available A mutualism quantum genetic algorithm (MQGA is proposed for an integrated supply chain scheduling with the materials pickup, flow shop scheduling, and the finished products delivery. The objective is to minimize the makespan, that is, the arrival time of the last finished product to the customer. In MQGA, a new symbiosis strategy named mutualism is proposed to adjust the size of each population dynamically by regarding the mutual influence relation of the two subpopulations. A hybrid Q-bit coding method and a local speeding-up method are designed to increase the diversity of genes, and a checking routine is carried out to ensure the feasibility of each solution; that is, the total physical space of each delivery batch could not exceed the capacity of the vehicle. Compared with the modified genetic algorithm (MGA and the quantum-inspired genetic algorithm (QGA, the effectiveness and efficiency of the MQGA are validated by numerical experiments.
Directory of Open Access Journals (Sweden)
Yin Aiwei
2016-01-01
Full Text Available To reduce the influence of wind power random on system operation, energy storage systems (ESSs and demand response (DR are introduced to the traditional scheduling model of wind power and thermal power with carbon emission trading (CET. Firstly, a joint optimization scheduling model for wind power, thermal power, and ESSs is constructed. Secondly, DR and CET are integrated into the joint scheduling model. Finally, 10 thermal power units, a wind farm with 2800 MW of installed capacity, and 3×80 MW ESSs are taken as the simulation system for verifying the proposed models. The results show backup service for integrating wind power into the grid is provided by ESSs based on their charge-discharge characteristics. However, system profit reduces due to ESSs’ high cost. Demand responses smooth the load curve, increase profit from power generation, and expand the wind power integration space. After introducing CET, the generation cost of thermal power units and the generation of wind power are both increased; however, the positive effect of DR on the system profit is also weakened. The simulation results reach the optimum when both DR and CET are introduced.
Directory of Open Access Journals (Sweden)
Hui Du
2016-01-01
Full Text Available To produce the final product, parts need to be fabricated in the process stages and thereafter several parts are joined under the assembly operations based on the predefined bill of materials. But assembly relationship between the assembly parts and components has not been considered in general job shop scheduling problem model. The aim of this research is to find the schedule which minimizes completion time of Assembly Job Shop Scheduling Problem (AJSSP. Since the complexity of AJSSP is NP-hard, a hybrid particle swarm optimization (HPSO algorithm integrated PSO with Artificial Immune is proposed and developed to solve AJSSP. The selection strategy based on antibody density makes the particles of HPSO maintain the diversity during the iterative process, thus overcoming the defect of premature convergence. Then HPSO algorithm is applied into a case study development from classical FT06. Finally, the effect of key parameters on the proposed algorithm is analyzed and discussed regarding how to select the parameters. The experiment result confirmed its practice and effectiveness.
Odegaard, Fredrik; Chen, Li; Quee, Ryan; Puterman, Martin L
2007-01-01
This article is the second of a 2-part series on a study of porter operations at Vancouver General Hospital, Vancouver, British Columbia, Canada. Part 1 describes the importance of efficient porter services, the system's operation at the time of the study, the challenges faced in carrying out the study, the performance measures developed, the recommendations, and the outcomes. Part 2 describes the simulation model that measured the impact of system changes and the linear programming model developed to improve porter schedules.
Institute of Scientific and Technical Information of China (English)
Zhang Rui; Song Rongfang
2011-01-01
An optimal linear precoding scheme based on Particle Swarm Optimization (PSO),which aims to maximize the system capacity of the cooperative transmission in the downlink channel,is proposed for a multicell multiuser single input single output system.With such a scheme,the optimal precoding vector could be easily searched for each user according to a simplified objective function.Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better Bit Error Rate (BER) performance than Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) algorithm.
STDP in adaptive neurons gives close-to-optimal information transmission
Directory of Open Access Journals (Sweden)
Guillaume Hennequin
2010-12-01
Full Text Available Spike-frequency adaptation is known to enhance the transmission of information in sensory spiking neurons, by rescaling the dynamic range for input processing, matching it to the temporal statistics of the sensory stimulus. Achieving maximal information transmission has also been recently postulated as a role for Spike-Timing Dependent Plasticity (STDP. However, the link between optimal plasticity and STDP in cortex remains loose, and so does the relationship between STDP and adaptation processes. We investigate how STDP, as described by recent minimal models derived from experimental data, influences the quality of information transmission in an adapting neuron. We show that a phenomenological model based on triplets of spikes yields almost the same information rate as an optimal model specially designed to this end. In contrast, the standard pair-based model of STDP does not improve information transmission as much. This result holds not only for additive STDP with hard weight bounds, known to produce bimodal distributions of synaptic weights, but also for weight-dependent STDP in the context of unimodal but skewed weight distributions. We analyze the similarities between the triplet model and the optimal learning rule, and find that the triplet effect is an important feature of the optimal model when the neuron is adaptive. If STDP is optimized for information transmission, it must take into account the dynamical properties of the postsynaptic cell, which might explain the target-cell specificity of STDP. In particular, it accounts for the differences found in vitro between STDP at excitatory synapses onto principal cells and those onto fast-spiking interneurons.
Optimal Control of Malaria Transmission using Insecticide Treated Nets and Spraying
Athina, D.; Bakhtiar, T.; Jaharuddin
2017-03-01
In this paper, we consider a model of the transmission of malaria which was developed by Silva and Torres equipped with two control variables, namely the use of insecticide treated nets (ITN) to reduce the number of human beings infected and spraying to reduce the number of mosquitoes. Pontryagin maximum principle was applied to derive the differential equation system as optimality conditions which must be satisfied by optimal control variables. The Mangasarian sufficiency theorem shows that Pontryagin maximum principle is necessary as well as sufficient conditions for optimization problem. The 4th-order Runge Kutta method was then performed to solve the differential equations system. The numerical results show that both controls given at once can reduce the number of infected individuals as well as the number of mosquitoes which reduce the impact of malaria transmission.
Downlink scheduling of multiuser MIMO systems with transmit beamforming
Institute of Scientific and Technical Information of China (English)
SONG Xing-hua; WU Wei-ling
2008-01-01
This article deals with downlink scheduling for multiuser multiple-input multiple-output (MIMO) systems, where the base station communicates with multiple users simultaneously through transmit beamforming. Most of the existing transmission schemes for multiuser MIMO systems focus on optimizing sum rate performance of the system. The individual quality of service (QoS) requirements (such as packet delay and minimum transmission rate for the data traffic) are rarely considered. In this article, a novel scheduling strategy is proposed, where we try to optimize the global system performance under individual QoS constraints. By performing scheduling into two steps, namely successive user selection and power allocation, the scheduler can achieve efficient resource utilization while maintaining the QoS requirements of all users. Extensive simulations and analysis are given to show the effectiveness of the proposed scheduler.
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.
Directory of Open Access Journals (Sweden)
Huan-huan Li
2015-01-01
Full Text Available Wind power has the characteristics of randomness and intermittence, which influences power system safety and stable operation. To alleviate the effect of wind power grid connection and improve power system’s wind power consumptive capability, this paper took emission trading and energy storage system into consideration and built an optimization model for thermal-wind power system and energy storage systems collaborative scheduling. A simulation based on 10 thermal units and wind farms with 2800 MW installed capacity verified the correctness of the models put forward by this paper. According to the simulation results, the introduction of carbon emission trading can improve wind power consumptive capability and cut down the average coal consumption per unit of power. The introduction of energy storage system can smooth wind power output curve and suppress power fluctuations. The optimization effects achieve the best when both of carbon emission trading and energy storage system work at the same time.
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...
Directory of Open Access Journals (Sweden)
Rong Chai
2017-01-01
Full Text Available In recent years, heterogeneous radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets. In this paper, we consider the downlink secure transmission of HetNets where the information transmission from base stations (BSs to legitimate users is subject to the interception of eavesdroppers. In particular, we stress the problem of joint user association and power allocation of the BSs. To achieve data transmission in a secure and energy efficient manner, we introduce the concept of secrecy energy efficiency which is defined as the ratio of the secrecy transmission rate and power consumption of the BSs and formulate the problem of joint user association and power allocation as an optimization problem which maximizes the joint secrecy energy efficiency of all the BSs under the power constraint of the BSs and the minimum data rate constraint of user equipment (UE. By equivalently transforming the optimization problem into two subproblems, that is, power allocation subproblem and user association subproblem of the BSs, and applying iterative method and Kuhn-Munkres (K-M algorithm to solve the two subproblems, respectively, the optimal user association and power allocation strategies can be obtained. Numerical results demonstrate that the proposed algorithm outperforms previously proposed algorithms.
Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling
DEFF Research Database (Denmark)
Soares, Joao; Vale, Zita; Canizes, Bruno
2013-01-01
This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aimi...... calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method....
An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem
Institute of Scientific and Technical Information of China (English)
常俊林; 邵惠鹤
2004-01-01
This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem's scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.
Huang, Yuxin; Xin, Qinkun; Yin, Huabing
2010-06-01
The torsional vibration of vehicle transmission system is heavily concerned with the increase of vehicle speed. The whole powertrain system has to be matched according to the torsional vibration characteristics, especially in developing a new vehicle. The selection of proper elastic coupling has to be made for the torsional vibration match and some frequencies have to be moved out of engine's range . Thus the torsional vibration model of powertrain needs to be built. In the paper a new torsional vibration model is built, which is programmed in the form of a platform. The whole powertrain system torsional vibration model of a vehicle is built firstly with consideration of gear mesh stiffness and engine's excitation in it. The free torsional vibration mode analysis is made and the resonant torques of each lumped inertia in the transmission system are obtained. Secondly the forced vibration of transmission system with the engine's excitation is made and the dynamic torques of each lumped inertias are obtained. Thirdly the process for the torsional vibration analysis is integrated into the optimization process and the selection of elastic coupling for the transmission system is made according the optimization and match results. Fourthly in order to modify the design parameters in the structural design, the sensitivities of inertia and torsional stiffness with reference to eigenvalues are obtained. At last the evaluations of analysis results are made and some suggestions for structural modification for engineers are presented. According to the above study, the conclusion can be made that the new torsional modelling method, the elastic coupling selection method and integration optimization method in the paper are practical and reliabl and these methods play very important roles in torsional vibration analyzing, match and optimization of vehicle transmission system.
Rate-aware optimal transmission power analysis in wireless ad hoc networks
Institute of Scientific and Technical Information of China (English)
Chen Lin; Li Minglu; Yu Jiadi
2008-01-01
The problem of transmission power control in a rate-aware way is investigated to improve the through put of wireless ad hoe network.The behavior of basic IEEE 802.11 DCF is approximated by the p-persistent CSMA through a Markov chain model.The throughput model takes hidden terminals, multi-hop flow and concurrent interference into account.Numerical results show that the optimal transmission power derived from this model could balance the tradeoff between spatial reuse and data rate and hence yield maximum throughput.
Implementation of transmission functions for an optimized three-terminal quantum dot heat engine
Schiegg, Christian H.; Dzierzawa, Michael; Eckern, Ulrich
2017-03-01
We consider two modifications of a recently proposed three-terminal quantum dot heat engine. First, we investigate the necessity of the thermalization assumption, namely that electrons are always thermalized by inelastic processes when traveling across the cavity where the heat is supplied. Second, we analyze various arrangements of tunneling-coupled quantum dots in order to implement a transmission function that is superior to the Lorentzian transmission function of a single quantum dot. We show that the maximum power of the heat engine can be improved by about a factor of two, even for a small number of dots, by choosing an optimal structure.
Energy Technology Data Exchange (ETDEWEB)
Kim, M; Saberian, F; Ghate, A [University of Washington, Seattle, WA (United States)
2014-06-15
Purpose: Past efforts to improve the therapeutic ratio have focused on a spatial approach where highly conformal radiation dose is given to tumors while minimizing dose to normal tissues, e.g., IMRT, VMAT, and IGRT. However, the fractionation schedule, i.e., a temporal approach to radiotherapy, has been largely overlooked so far in maximizing the therapeutic ratio. We establish a rigorous mathematical spatio-temporal approach to systematically investigate the feasibility and potential benefits of simultaneously optimizing radiation dose distribution in space and time. Methods: Stochastic control formalism is constructed to maximize the average tumor BED by choosing an optimal radiation dose distribution for an optimal number of fractions subject to normal tissue BED constraints. Three separate simulations are run on two groups of phantom cases; 5 cases with prostate cancer and 5 cases with head-and-neck cancer. (1) Conventional IMRT with 70Gy/35fx for head-and-neck, and 81Gy/45fx for prostate, (2) IMRT is done independently from the fractionation schedule optimization (S-model), (3) integrated spatio-temporal approach (I-model) where radiation intensities are simultaneously optimized for the first time ever in space and time. Final tumor BEDs from the three trials are compared in prostate and head-and-neck cases. Results: Numerical simulations show that final tumor BED from I-model is 20–90% larger than conventional IMRT, and 20-50% larger than S-model for head-and-neck cancer with α/β=10 and Tdouble=2–50 days. Final tumor BED from I-model is also 90–140% larger than conventional IMRT, and 20–30% larger than S-model for prostate cancer with α/β=2 and Tdouble=5–80 days. Conclusion: Our spatio-temporal optimization of radiotherapy allows an expansion of search space for the optimal treatment plans to include the temporal distribution of radiation dose in addition to the spatial distribution. Such spatio-temporal approach shows great potential to improve
Institute of Scientific and Technical Information of China (English)
ZHOU Yunshan; LIU Jin'gang; CAI Yuanchun; ZOU Naiwei
2008-01-01
Associated dynamic performance of the clamping force control valve used in continuously variable transmission (CVT) is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm (GA) with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.Key words: Dynamic modeling Optimal design Genetic algorithm Clamping force control valve Continuously variable transmission (CVT)
Ugarte, Marta
2015-01-01
This study was designed guided by the Model for Improvement framework to reduce waiting times and visit duration in the intravitreal therapy clinic, while improving patient and staff experience. In our aim to provide good quality, patient-centred care and constantly improve, we optimised the appointment profile and patient flow. We involved a multidisciplinary team (one consultant, junior doctors, staff nurses, technicians, and receptionist), as well as patients and relatives, to try to understand the main delays in the clinic. Process mapping, a fishbone diagram, run charts, together with feedback from patients and staff, provided an insight on the possible roots of the delays experienced by our patients. The results of the inquiry led us to take actions focused on optimising appointment scheduling. After implementing the new scheduling profile (with a gap in the middle of the session), various cycles of plan-do-study-act and a comparative, qualitative study by interviewing 10 patients demonstrated that the waiting times decreased, and patients and staff experience improved.
Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization
Directory of Open Access Journals (Sweden)
Qi Xu
2012-01-01
Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.
Directory of Open Access Journals (Sweden)
Anupama Tasneem
2015-11-01
Full Text Available Long Term Evolution (LTE is the new standard specified by Third Generation Partnership Project (3GPP on the way towards the 4G mobile network. The LTE introduces enhance data link mechanisms to support successful implementation of new data services across the network. The incorporated scheduling mechanisms can significantly contribute to this goal. In this paper, we have compared the performance of Best Channel Quality Indicator (Best CQI and Proportional Fair (PF which are the two most popular scheduling algorithms used in LTE. The performance was compared in rich multipath environment using Transmit Diversity (TxD and Open Loop Spatial Multiplexing (OLSM. In recent past similar comparison was carried out for rural environments [1]. In the rural environment the multipath effect was not significant. The results of simulation showed that both the Best CQI and PF perform fairly well for TxD in comparison with the OLSM technique. So here in this paper we have considered the urban environment which demonstrates significant effect due to multipath. The simulation results shows improvement in total throughput is not so significant using OLSM technique for the both the scheduling without much deterioration in the BLER. When the throughput is increased in OLSM, the BLER gets deteriorated drastically. Thus TxD is found to be working efficiently in rich multipath environments as it had been found previously for the rural environment.
Directory of Open Access Journals (Sweden)
Ali Nouri
2014-01-01
Full Text Available The maximizing of sound transmission loss (TL across a functionally graded material (FGM cylindrical shell has been conducted using a genetic algorithm (GA. To prevent the softening effect from occurring due to optimization, the objective function is modified based on the first resonant frequency. Optimization is performed over the frequency range 1000–4000 Hz, where the ear is the most sensitive. The weighting constants are chosen here to correspond to an A-weighting scale. Since the weight of the shell structure is an important concern in most applications, the weight of the optimized structure is constrained. Several traditional materials are used and the result shows that optimized shells with aluminum-nickel and aluminum-steel FGM are the most effective at maximizing TL at both stiffness and mass control region, while they have minimum weight.
Institute of Scientific and Technical Information of China (English)
GU Yanchun; YIN Chengliang; ZHANG Jianwu
2007-01-01
In parallel hybrid electrical vehicle (PHEV) equipped with automatic mechanical transmission (AMT), the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving sinoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.
Optimization of top polymer gratings to improve GaN LEDs light transmission
Institute of Scientific and Technical Information of China (English)
Xiaomin Jin; Bei Zhang; Tao Dai; Wei Wei; Xiangning; Guoyi Zhang; Simeon Trieu; Fei Wang
2008-01-01
@@ We present a grating model of two-dimensional (2D) rigorous coupled wave analysis (RCWA) to study top diffraction gratings on light-emitting diodes(LEDs). We compare the integrated-transmission of the non-grating,rectangular-grating,and triangular-grating cases for the same grating period of 6μm,and show that the triangular grating has the best performance. For the triangular grating with 6-μmperiod, the LED achieves the highest light transmission at 6-μ gratingbottom width and 2.9-μm grating depth. Compared with the non-grating case, the optimized light transmission improvement is about 74.6%.The simulation agrees with the experimental data of the thin ploymer grating encapsulated flip-chip(FC) GaN-based LEDs for the light extraction improvement.
Optimization of Single-Sensor Two-State Hot-Wire Anemometer Transmission Bandwidth.
Ligęza, Paweł
2008-10-28
Hot-wire anemometric measurements of non-isothermal flows require the use of thermal compensation or correction circuitry. One possible solution is a two-state hot-wire anemometer that uses the cyclically changing heating level of a single sensor. The area in which flow velocity and fluid temperature can be measured is limited by the dimensions of the sensor's active element. The system is designed to measure flows characterized by high velocity and temperature gradients, although its transmission bandwidth is very limited. In this study, we propose a method to optimize the two-state hot-wire anemometer transmission bandwidth. The method is based on the use of a specialized constanttemperature system together with variable dynamic parameters. It is also based on a suitable measurement cycle paradigm. Analysis of the method was undertaken using model testing. Our results reveal a possible significant broadening of the two-state hot-wire anemometer's transmission bandwidth.
Institute of Scientific and Technical Information of China (English)
刘民; 李法朝; 吴澄
2003-01-01
Measuring the difference between fuzzy numbers is often needed in many fuzzy optimizationproblems such as manufacturing system production line scheduling with uncertainty environments. In thispaper, based on the distance function of plane R2 and the level importance function, we establish theUID-metric and LPID-metric of measuring the difference between fuzzy numbers, and discuss the basicproperties of UID-metric and LPID-metric, and prove that fuzzy number spaces are metric spaces aboutUID-metric and LPID-metric if and only if the level importance function /(λ) ≠ 0 almost everywhere on [0,1]. Further, we discuss the convergence, separability and completeness of UID-metric and LPID-metricbased on the norms of plane R2. Finally, we analyze the characteristics of UID-metric and LPID-metric bysome application examples.
Capacity optimization and scheduling of a multiproduct manufacturing facility for biotech products.
Shaik, Munawar A; Dhakre, Ankita; Rathore, Anurag S; Patil, Nitin
2014-01-01
A general mathematical framework has been proposed in this work for scheduling of a multiproduct and multipurpose facility involving manufacturing of biotech products. The specific problem involves several batch operations occurring in multiple units involving fixed processing time, unlimited storage policy, transition times, shared units, and deterministic and fixed data in the given time horizon. The different batch operations are modeled using state-task network representation. Two different mathematical formulations are proposed based on discrete- and continuous-time representations leading to a mixed-integer linear programming model which is solved using General Algebraic Modeling System software. A case study based on a real facility is presented to illustrate the potential and applicability of the proposed models. The continuous-time model required less number of events and has a smaller problem size compared to the discrete-time model.
Directory of Open Access Journals (Sweden)
Yu-Chi Wu
2015-01-01
Full Text Available A modeling and computational framework is presented for the determination of optimal carbon taxes that apply to electric power plants in the context of electric power supply chain with consideration of transmission constraints and losses. In order to achieve this goal, a generalized electric power supply chain network equilibrium model is used. Under deregulation, there are several players in electrical market: generation companies, power suppliers, transmission service providers, and consumers. Each player in this model tries to maximize its own profit and competes with others in a noncooperative manner. The Nash equilibrium conditions of these players in this model form a finite-dimensional variational inequality problem (VIP. By solving this VIP via an extragradient method based on an interior point algorithm, the optimal carbon taxes of power plants can be determined. Numerical examples are provided to analyze the results of the presented modeling.
Song, Yi
2011-01-01
In a cognitive radio (CR) network, CR users intend to operate over the same spectrum band licensed to legacy networks. A tradeoff exists between protecting the communications in legacy networks and maximizing the throughput of CR transmissions, especially when CR links are unstable due to the mobility of CR users. Because of the non-zero probability of false detection and implementation complexity of spectrum sensing, in this paper, we investigate a sensing-free spectrum sharing scenario for mobile CR ad hoc networks to improve the frequency reuse by incorporating the location awareness capability in CR networks. We propose an optimal power control algorithm for the CR transmitter to maximize the concurrent transmission region of CR users especially in mobile scenarios. Under the proposed power control algorithm, the mobile CR network achieves maximized throughput without causing harmful interference to primary users in the legacy network. Simulation results show that the proposed optimal power control algori...
Optimization of Coding of AR Sources for Transmission Across Channels with Loss
DEFF Research Database (Denmark)
Arildsen, Thomas
, and quantization. On this background we propose a new algorithm for optimization of predictive coding of AR sources for transmission across channels with loss. The optimization algorithm takes as its starting point a re-thinking of the source coding operation as an operation producing linear measurements....... Channel coding is usually applied in combination with source coding to ensure reliable transmission of the (source coded) information at the maximal rate across a channel given the properties of this channel. In this thesis, we consider the coding of auto-regressive (AR) sources which are sources that can...... be modeled as auto-regressive processes. The coding of AR sources lends itself to linear predictive coding. We address the problem of joint source/channel coding in the setting of linear predictive coding of AR sources. We consider channels in which individual source coded signal samples can be lost during...
分布式卫星系统数传调度研究%Research on Transmission Task Scheduling for Distributed Satellite Systems
Institute of Scientific and Technical Information of China (English)
国晓博; 刘金灿; 周红彬
2016-01-01
With the increase of in⁃orbit sensing satellite,the current relay satellite system is facing a major challenge in terms of data transmission performance.While the payload capability is restricted by satellite platform,the distributed satellite system is a practi⁃cal solution to tremendously improve the performance of relay satellite system. The transmission task scheduling problem of distributed satellite system is studied, a multi⁃satellites multi⁃tasks transmission task scheduling model is proposed based on PSO algorithm by jointly considering constraint conditions such as the visible time windows between distributed satellite systems and user satellites in MEO and LEO, the residual resources of satellite systems and the property of user transmission tasks. Considering the number of scheduling tasks in practice,the typical PSO algorithm is modified and the proposed algorithm is verified by computer simulations.%近年来随着我国在轨遥感卫星数量的不断增加，对当前中继星系统的数传能力提出了更高的要求。在卫星平台载荷能力受限的情况下，分布式卫星系统成为大幅提升中继星系统性能的可行方案。针对分布式卫星系统的数传任务调度问题，综合考虑系统与中低轨航天器用户之间的可见时间窗口、卫星剩余资源及数传任务属性等约束条件，提出了基于粒子群优化（ PSO）的多星多任务数传调度方法。根据实际中继系统任务量级对经典 PSO 算法进行了改进，并通过计算机仿真验证了改进算法的有效性。
A guideline for optimizing outage management of Eskom's transmission network / Michelle de Haan
de Haan, Michelle
2012-01-01
A streamlined process is needed to optimize the outage management of the Eskom transmission power system, as well as a ranking system in order to determine the best window of opportunity for an outage to occur thus positively impacting on Eskom‘s asset management. The outage data captured between 2007 and 2011 was analysed for all cancelled, turned down and completed outages. This data indicated that there were 19 902 completed outages, 5 312 cancelled outages and 1 889 turned down outages...
Application of Optimal Control to the Epidemiology of Fowl Pox Transmission Dynamics in Poultry
Directory of Open Access Journals (Sweden)
Inyama Simeon Chioma
2012-01-01
Full Text Available Problem statement: In this study, we present the mathematical model of the transmission dynamics of fowl pox infection in poultry. Approach: It describes the interaction between the susceptible and the infected birds which results in a system of ordinary differential equation. Introducing the control which represents the effort in applying chemoprophylaxis control u1 and treatment control u2 in birds with fowl pox, the system becomes a system of ordinary differential equations with control. Results: Our optimal control problem involves that in which the number of birds with latent and active fowl pox infections and the cost of treatment controls u1 (t and u2 (t were minimized subject to the differential Eq. 5-8. This involves the number of birds with active and latent fowl pox respectively as well as the cost of applying chemoprophylaxis control u1 and treatment u2 in birds with fowl pox. Conclusion: Analysing the model using Pontryaginâs Maximum Principle and optimality conditions, optimal effort necessary to reduce the transmission rate of fowl pox in the poultry has been determined. Hence, it is possible to reduce to reduce the rate of transmission.
Optimal control of an electric vehicle’s charging schedule under electricity markets
DEFF Research Database (Denmark)
Lan, Tian; Hu, Junjie; Kang, Qi
2013-01-01
As increasing numbers of electric vehicles (EVs) enter into the society, the charging behavior of EVs has got lots of attention due to its economical difference within the electricity market. The charging cost for EVs generally differ from each other in choosing the charging time interval (hourly......), since the hourly electricity prices are different in the market. In this paper, the problem is formulated into an optimal control one and solved by dynamic programming. Optimization aims to find the economically optimal charging solution for each vehicle. In this paper, a nonlinear battery model...... is characterized and presented, and a given future electricity prices is assumed and utilized. Simulation results indicate that daily charing cost is reduced by smart charing....
Optimization of Radiation Therapy Fractionation Schedules in the Presence of Tumor Repopulation
Bortfeld, Thomas; Tsitsiklis, John N; Unkelbach, Jan
2013-01-01
We analyze the effect of tumor repopulation on optimal dose delivery in radiation therapy. We are primarily motivated by accelerated tumor repopulation towards the end of radiation treatment, which is believed to play a role in treatment failure for some tumor sites. A dynamic programming framework is developed to determine an optimal fractionation scheme based on a model of cell kill due to radiation and tumor growth in between treatment days. We find that faster tumor growth suggests shorter overall treatment duration. In addition, the presence of accelerated repopulation suggests larger dose fractions later in the treatment to compensate for the increased tumor proliferation. We prove that the optimal dose fractions are increasing over time. Numerical simulations indicate potential for improvement in treatment effectiveness.
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.
Wakano, Joe Yuichiro; Miura, Chiaki
2014-02-01
Inheritance of culture is achieved by social learning and improvement is achieved by individual learning. To realize cumulative cultural evolution, social and individual learning should be performed in this order in one's life. However, it is not clear whether such a learning schedule can evolve by the maximization of individual fitness. Here we study optimal allocation of lifetime to learning and exploitation in a two-stage life history model under a constant environment. We show that the learning schedule by which high cultural level is achieved through cumulative cultural evolution is unlikely to evolve as a result of the maximization of individual fitness, if there exists a trade-off between the time spent in learning and the time spent in exploiting the knowledge that has been learned in earlier stages of one's life. Collapse of a fully developed culture is predicted by a game-theoretical analysis where individuals behave selfishly, e.g., less learning and more exploiting. The present study suggests that such factors as group selection, the ability of learning-while-working ("on the job training"), or environmental fluctuation might be important in the realization of rapid and cumulative cultural evolution that is observed in humans.
Directory of Open Access Journals (Sweden)
Song Huang
2016-01-01
Full Text Available The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF. In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.
Statistical Mechanics of On-Line Learning Using Correlated Examples and Its Optimal Scheduling
Fujii, Takashi; Ito, Hidetaka; Miyoshi, Seiji
2017-08-01
We theoretically study the generalization capability of on-line learning using several correlated input vectors in each update in a statistical-mechanical manner. We consider a model organized with linear perceptrons with Gaussian noise. First, in a noiseless case, we analytically derive the optimal learning rate as a function of the number of examples used in one update and their correlation. Next, we analytically show that the use of correlated examples is effective if the optimal learning rate is used, even when there is some noise. Furthermore, we propose a novel algorithm that raises the generalization capability by increasing the number of examples used in one update with time.
Institute of Scientific and Technical Information of China (English)
GE Xiaolin; SHU Jun; ZHANG Lizi
2012-01-01
Mid-long term hydro-thermal optimal dispatching plays an important role in mid-long term electric power and energy balance, and it also can bring significant economic benefits. This topic has been discussed in many literatures and some progress has been achieved, but there are still two problems that need to be solved. First, the modeling approach needs to be improved. When a multi-scenario model is adopted in hydro-thermal optimal dispatching, the existing modeling approaches will probably suffer from the dimensionality problem. Second, the construction of the mathematical model is not comprehensive. Generally, the existing model only considers the power balance;
Directory of Open Access Journals (Sweden)
A.D. Falehi
2012-07-01
Full Text Available This study inspects the optimum location of STATCOM device in long transmission line to acquire the maximum power system transient stability improvement. STATCOM is a kind of prominent and effective shunt FACTS device which is used in power system to enhance the power system stability and to regulate the line voltage. When it has been placed at the center point of a transmission line, play a key role in controlling the reactive power flow and enhancing the power system transient stability. The active power losses caused by transmission line resistance alter the neutral position or optimum location of STATCOM in transmission line. RCGA optimization due to have high ability to solve non-linear objective function has been implanted to identify the optimum location of STATCOM. The results of non-linear simulation under severe disturbance approve that the optimum location of STATCOM in order to access the maximum power system transient stability by reducing the active power losses approaches to midpoint of transmission line.
Institute of Scientific and Technical Information of China (English)
武善玉; 张平; 李方; 古锋; 潘毅
2016-01-01
To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems (SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm (HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.
Airport Ground Services Scheduling Simulation and Optimization%机场地面作业仿真与优化
Institute of Scientific and Technical Information of China (English)
黄鹂诗; 杨文东
2014-01-01
In view of airport ground services scheduling, which is a mixed process optimization of fixed site and job-shop, a model is built which employs the object-oriented simulation software SIMIO to simulate the all-day operation of the shuttle bus and fuel truck. This article proposes a two-objective optimi-zation aiming at minimizing flight delays and reducing equipment wastage. And then it analyzes the results of vehicle assignment plan to find out the existing problems. Through changing the logic condition of the system, an optimization solution has been put forward to improve equipment utilization.%针对机场地面作业调度这一定位型流程（Fixed Site)和JOBSHOP二者混合的流程优化问题，运用面向对象的SIMIO仿真软件实现机坪保障设备中摆渡车、加油车的全天运行。提出基于平衡设备工作量差和航班延误最少的双优化目标，对仿真所得的车辆指派计划进行统计分析，找出存在的问题，通过更改系统逻辑条件建立优化模型，根据优化目标给出最终优化方案。
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.
Rana, Javed; Gadre, Bhooshan; Bhalerao, Varun; Bose, Sukanta
2016-01-01
The discovery and subsequent study of optical counterparts to transient sources is crucial for their complete astrophysical understanding. Various gamma ray burst (GRB) detectors, and more notably the ground--based gravitational wave detectors, typically have large uncertainties in the sky positions of detected sources. Searching these large sky regions spanning hundreds of square degrees is a formidable challenge for most ground--based optical telescopes, which can usually image less than tens of square degrees of the sky in a single night. We present algorithms for optimal scheduling of such follow--up observations in order to maximize the probability of imaging the optical counterpart, based on the all--sky probability distribution of the source position. We incorporate realistic observing constraints like the diurnal cycle, telescope pointing limitations, available observing time, and the rising/setting of the target at the observatory location. We use simulations to demonstrate that our proposed algorith...
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury [Federal Univ. of Itajuba, Minas Gerais (Brazil)
2011-01-15
Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)
Optimal resource allocation and load scheduling for a multi-commodity smart energy system
Blaauwbroek, N.; Nguyen, P.H.; Shi, H.; Kamphuis, I.G.; Kling, W.L.; Konsman, M.J.
2015-01-01
The increasing introduction of district heating systems together with hybrid energy appliances as heat pumps and micro-combined heat and power installations, results in new opportunities for optimizing the available resources in multi-commodity smart energy systems, including electricity, heat and
Vinnikov, Volodymyr A.
2017-01-01
The methodology of cytogenetic triage can be improved by optimizing a schedule of microscopy for different exposure scenarios. Chromosome aberrations were quantified by microscopy in human blood lymphocytes irradiated in vitro to ~2, 4, and 12 Gy acute 60Co γ-rays mixed with the unirradiated blood simulating 10%, 50%, 90%, and 100% exposure and in along with a sample from a homogeneous exposure to ~20 Gy. Biodosimetry workload was statistically modeled assuming that 0.5, 1, 5, or 25 h was available for scoring one case or for analysis of up to 1000 cells or 100 dicentrics plus centric rings by one operator. A strong negative correlation was established between the rates of aberration acquisition and cell recording. Calculations showed that the workload of 1 case per operator per·day (5 h of scoring by microscopy) allows dose estimates with high accuracy for either 90%–100% irradiations of 2 Gy or 50%–90% irradiations of 4–12 Gy; lethal homogeneous (100%) exposures of 12 and 20 Gy can be evaluated with just 1 h of microscopy. Triage analysis of 0.5 h scoring per case results in the minimum tolerable accuracy only for partial- and total-body exposure of 4–20 Gy. Time-related efficacy of conventional biodosimetry depends primarily on the aberration yield in the sample, which is dependent on the radiation dose and its distribution in the patient's body. An optimized schedule of microscopy scoring should be developed for different exposure scenarios in each laboratory to increase their preparedness to radiological emergencies. PMID:28250910
Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization.
Chung, Sai Ho; Ma, Hoi Lam; Chan, Hing Kai
2017-08-01
This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era. © 2016 Society for Risk Analysis.
Directory of Open Access Journals (Sweden)
Yin Luo
2012-01-01
Full Text Available Traditional pump scheduling models neglect the operation reliability which directly relates with the unscheduled maintenance cost and the wear cost during the operation. Just for this, based on the assumption that the vibration directly relates with the operation reliability and the degree of wear, it could express the operation reliability as the normalization of the vibration level. The characteristic of the vibration with the operation point was studied, it could be concluded that idealized flow versus vibration plot should be a distinct bathtub shape. There is a narrow sweet spot (80 to 100 percent BEP to obtain low vibration levels in this shape, and the vibration also follows similar law with the square of the rotation speed without resonance phenomena. Then, the operation reliability could be modeled as the function of the capacity and rotation speed of the pump and add this function to the traditional model to form the new. And contrast with the tradition method, the result shown that the new model could fix the result produced by the traditional, make the pump operate in low vibration, then the operation reliability could increase and the maintenance cost could decrease.
Directory of Open Access Journals (Sweden)
Leyzgold D.Yu.
2015-04-01
Full Text Available This article studies the problem of the transmission line conductor heating effect on the active power flows optimization in the local segment of industrial power supply. The purpose is to determine the optimal generation rating of the distributed power sources, in which the power flow values will correspond to the minimum active power losses in the power supply. The timeliness is the need to define the most appropriate rated power values of distributed sources which will be connected to current industrial power supply. Basing on the model of active power flow optimization, authors formulate the description of the nonlinear transportation problem considering the active power losses depending on the transmission line conductor heating. Authors proposed a new approach to the heating model parameters definition based on allowable current loads and nominal parameters of conductors as part of the optimization problem. Analysis of study results showed that, despite the relatively small active power losses reduction to the tune 0,45% due to accounting of the conductors heating effect for the present configuration of power supply, there are significant fluctuations in the required generation rating in nodes of the network to 9,32% within seasonal changes in the outer air temperature. This fact should be taken into account when selecting the optimum power of distributed generation systems, as exemplified by an arbitrary network configuration.
Moro, Erik A; Todd, Michael D; Puckett, Anthony D
2011-12-10
A variety of intensity-modulated optical displacement sensor architectures have been proposed for use in noncontacting sensing applications, with one of the most widely implemented architectures being the bundled displacement sensor. To the best of the authors' knowledge, the arrangement of measurement fibers in previously reported bundled displacement sensors has not been configured with the use of a validated optical transmission model. Such a model has utility in accurately describing the sensor's performance a priori and thereby guides the arrangement of the fibers within the bundle to meet application-specific performance needs. In this paper, a recently validated transmission model is used for these purposes, and an optimization approach that employs a genetic algorithm efficiently explores the design space of the proposed bundle sensor architecture. From the converged output of the optimization routine, a bundled displacement sensor configuration is designed and experimentally tested, offering linear performance with a sensitivity of -0.066 μm(-1) and displacement measurement error of 223 μm over the axial displacement range of 6-8 mm. It is shown that this optimization approach may be generalized to determine optimized bundle configurations that offer high-sensitivity performance, with an acceptable error level, over a variety of axial displacement ranges. This document has been approved by Los Alamos National Laboratory for unlimited public release (LA-UR 11-03413). © 2011 Optical Society of America
Directory of Open Access Journals (Sweden)
Rodrigo Palma-Behnke
2013-01-01
Full Text Available A novel optimization methodology consisting of finding the near optimal location of wind turbines (WTs on a planned transmission network in a secure and cost-effective way is presented on this paper. While minimizing the investment costs of WTs, the algorithm allocates the turbines so that a desired wind power energy-penetration level is reached. The optimization considers both transmission security and power system stability constraints. The results of the optimization provide regulators with a support instrument to give proper signals to WT investors, in order to achieve secure and cost effective wind power network integration. The proposal is especially aimed at countries in the initial stage of wind power development, where the WT network integration process can still be influenced by policy-makers. The proposed methodology is validated with a real power system. Obtained results are compared with those generated from a business-as-usual (BAU scenario, in which the WT network allocation is made according to existing WT projects. The proposed WT network allocation scheme not only reduces the total investment costs associated with a determined wind power energy target, but also improves power system stability.
Energy Technology Data Exchange (ETDEWEB)
Felizari, Luiz Carlos; Lueders, Ricardo [Centro Federal de Educacao Tecnologica do Parana (CEFET-PR), Curitiba, PR (Brazil)
2004-07-01
Among important factors concerning control and management of industrial production, the programming of operations should be considered. The scheduling process takes in account the processing time of each operation, which is inherently uncertainty. This way, decisions in this activity should be supported by decision systems, especially those that use optimization techniques. This paper proposes a scheduling of transfer and storage operations in a refinery, considering tanks and pipelines. It aims to consider possible operation time delays by using fuzzy optimization techniques. The purpose is to consider characteristics such as soft constraints, not found in traditional models. In the mathematical model development, mixed integer linear programming (MILP) is used with continuous time approach. (author)
On Multiple Users Scheduling Using Superposition Coding over Rayleigh Fading Channels
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
Optimal Scheduling of Industrial Task-Continuous Load Management for Smart Power Utilization
Directory of Open Access Journals (Sweden)
Jidong Wang
2017-03-01
Full Text Available In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG is increasing rapidly on the user’s side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes.
Institute of Scientific and Technical Information of China (English)
王倩
2015-01-01
In the management of power transmission and transformation project, project schedule management is one of the most important parts. Currently, methods frequently applied in the schedule management of power transmission and transformation project include Gantt chart,CPM,earned value management,S curve, PERT and etc..However, we often come across all kinds of problems when applying these methods in power transmission and transformation project, for instance, the project safety time is lost or the duration program is too long. To solve the problems mentioned above ,this paper introduces the Critical Chain theory to optimize the schedule management of power transmission and transformation project. The advantage of the Critical Chain theory is that it can start from the whole project, giving full consideration to the project's human power, material resource and financial resource, and additionally, it can meanwhile consider the sequence constraint of activities and the resource constraint between the activities as well. By taking 500kV power transmission and transformation project as an example to conduct the empirical analysis, it shows that this theory can effectively solve the resource conflict in the construction of power transmission and transformation project, therefore it can both improve the work efficiency and reduce the cost at the same time.%项目进度管理是输变电项目管理中最重要的一个环节,目前常用于输变电工程项目进度管理的方法包括甘特图法、关键路径法、挣得值法、S曲线比较法和计划评审技术法等,然而在输变电工程项目中采用这些方法时,存在项目安全时间丢失、工期计划过长等问题.为解决上述问题,本文引入关键链理论来对输变电项目进度管理进行优化处理,关键链理论的优势是它可以从工程的整体出发,充分考虑项目的人、物和财等资源,并考虑项目活动的前后约束和活动间的资源约束.以500kV输变
Optimal Scheduling of Time-Shiftable Electric Loads in Expeditionary Power Grids
2015-09-01
ter cold and blazing heat by expeditionary shelter systems consisting of structures with heating, ventilation, and air conditioning (HVAC) systems...storage, shower /shaves, latrines, kitchen and dining, and other functions. Tent modules come in 8 foot by 20-foot sections that fasten together; the...Parameters for rolling horizon optimization. 85 THIS PAGE INTENTIONALLY LEFT BLANK 86 List of References [1] “Environmental medicine: heat, cold , and
Directory of Open Access Journals (Sweden)
Yongkai Zhou
2013-01-01
Full Text Available The paper considers the secure transmission in a wireless environment in which both the transmitter (Alice and the legitimate receiver (Bob send artificial noise (AN to interfere with the eavesdropper (Eve. Optimal design is analyzed in detail for this AN-by-both-side model to deal with Eve’s stochastic channel condition and random spatial distribution. Bipolar-beamforming is first proposed to jointly design Alice and Bob’s transmitting signals. By optimally assigning the transmitting antenna for Bob and allocating the power ratio between Alice’s information and the AN signal, maximum secrecy capacity can be achieved. Simulation is done to illustrate the process of bipolar-beamforming optimization. Results show that the AN-by-both-side model has good secrecy performance on both average and extreme conditions as Eve approaches Alice or Bob.
Energy transmission modes based on Tabu search and particle swarm hybrid optimization algorithm
Institute of Scientific and Technical Information of China (English)
LI xiang; CUI Ji-feng; QI Jian-xun; YANG Shang-dong
2007-01-01
In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage, To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency Was established, and then, a new Tabu search and power transmission was gained.Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methodsthat are based on regional price differences. freight rates and annual cost witll the proposed method, the result indicates that the economic efficiency of the energy transfer Can be enhanced by 3.14%, 5.78% and 6.01%, respectively.
Zimmermann, Karl-Heinz; Achtziger, Wolfgang
2001-09-01
The size of a systolic array synthesized from a uniform recurrence equation, whose computations are mapped by a linear function to the processors, matches the problem size. In practice, however, there exist several limiting factors on the array size. There are two dual schemes available to derive arrays of smaller size from large-size systolic arrays based on the partitioning of the large-size arrays into subarrays. In LSGP, the subarrays are clustered one-to-one into the processors of a small-size array, while in LPGS, the subarrays are serially assigned to a reduced-size array. In this paper, we propose a common methodology for both LSGP and LPGS based on polyhedral partitionings of large-size k-dimensional systolic arrays which are synthesized from n-dimensional uniform recurrences by linear mappings for allocation and timing. In particular, we address the optimization problem of finding optimal piecewise linear timing functions for small-size arrays. These are mappings composed of linear timing functions for the computations of the subarrays. We study a continuous approximation of this problem by passing from piecewise linear to piecewise quasi-linear timing functions. The resultant problem formulation is then a quadratic programming problem which can be solved by standard algorithms for nonlinear optimization problems.
Optimal vaccine schedules to maintain measles elimination with a two-dose routine policy.
McKEE, A; Shea, K; Ferrari, M J
2017-01-01
Measles was eliminated in the Americas in 2002 by a combination of routine immunizations and supplementary immunization activities. Recent outbreaks underscore the importance of reconsidering vaccine policy in order to maintain elimination. We constructed an age-structured dynamical model for the distribution of immunity in a population with routine immunization and without disease, and analysed the steady state for an idealized age structure and for real age structures of countries in the Americas. We compared the level of immunity maintained by current policy in these countries to the level maintainable by an optimal policy. The optimal age target for the first routine dose of measles vaccine depends on the timing and coverage of both doses. Similarly, the optimal age target for the second dose of measles vaccine depends on the timing and coverage of the first dose. The age targets for the first and second doses of measles vaccine should be adjusted for the post-elimination era, by specifically accounting for current context, including realized coverage of both doses, and altered maternal immunity. Doing so can greatly improve the proportion immune within a population, and therefore the chances of maintaining measles elimination, without changing coverage.
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Meng, Miao; Kiani, Mehdi
2017-02-01
Ultrasound has been recently proposed as an alternative modality for efficient wireless power transmission (WPT) to biomedical implants with millimeter (mm) dimensions. This paper presents the theory and design methodology of ultrasonic WPT links that involve mm-sized receivers (Rx). For given load (RL) and powering distance (d), the optimal geometries of transmitter (Tx) and Rx ultrasonic transducers, including their diameter and thickness, as well as the optimal operation frequency (fc) are found through a recursive design procedure to maximize the power transmission efficiency (PTE). First, a range of realistic fcs is found based on the Rx thickness constrain. For a chosen fc within the range, the diameter and thickness of the Rx transducer are then swept together to maximize PTE. Then, the diameter and thickness of the Tx transducer are optimized to maximize PTE. Finally, this procedure is repeated for different fcs to find the optimal fc and its corresponding transducer geometries that maximize PTE. A design example of ultrasonic link has been presented and optimized for WPT to a 1 mm(3) implant, including a disk-shaped piezoelectric transducer on a silicon die. In simulations, a PTE of 2.11% at fc of 1.8 MHz was achieved for RL of 2.5 [Formula: see text] at [Formula: see text]. In order to validate our simulations, an ultrasonic link was optimized for a 1 mm(3) piezoelectric transducer mounted on a printed circuit board (PCB), which led to simulated and measured PTEs of 0.65% and 0.66% at fc of 1.1 MHz for RL of 2.5 [Formula: see text] at [Formula: see text], respectively.
An optimal control strategies using vaccination and fogging in dengue fever transmission model
Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan
2017-08-01
This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging optimally, the number of vector can be minimized. In this case, we considered the giving of vaccination as a control variable because it is one of the efforts that are being developed to reduce the spreading of dengue fever. We used Pontryagin Minimum Principle to solve the optimal control problem. Furthermore, the numerical simulation results are given to show the effect of the optimal control strategies in order to minimize the epidemic of dengue fever.
Anatomy-based transmission factors for technique optimization in portable chest x-ray
Liptak, Christopher L.; Tovey, Deborah; Segars, William P.; Dong, Frank D.; Li, Xiang
2015-03-01
Portable x-ray examinations often account for a large percentage of all radiographic examinations. Currently, portable examinations do not employ automatic exposure control (AEC). To aid in the design of a size-specific technique chart, acrylic slabs of various thicknesses are often used to estimate x-ray transmission for patients of various body thicknesses. This approach, while simple, does not account for patient anatomy, tissue heterogeneity, and the attenuation properties of the human body. To better account for these factors, in this work, we determined x-ray transmission factors using computational patient models that are anatomically realistic. A Monte Carlo program was developed to model a portable x-ray system. Detailed modeling was done of the x-ray spectrum, detector positioning, collimation, and source-to-detector distance. Simulations were performed using 18 computational patient models from the extended cardiac-torso (XCAT) family (9 males, 9 females; age range: 2-58 years; weight range: 12-117 kg). The ratio of air kerma at the detector with and without a patient model was calculated as the transmission factor. Our study showed that the transmission factor decreased exponentially with increasing patient thickness. For the range of patient thicknesses examined (12-28 cm), the transmission factor ranged from approximately 21% to 1.9% when the air kerma used in the calculation represented an average over the entire imaging field of view. The transmission factor ranged from approximately 21% to 3.6% when the air kerma used in the calculation represented the average signals from two discrete AEC cells behind the lung fields. These exponential relationships may be used to optimize imaging techniques for patients of various body thicknesses to aid in the design of clinical technique charts.
Power Distortion Optimization for Uncoded Linear Transformed Transmission of Images and Videos.
Xiong, Ruiqin; Zhang, Jian; Wu, Feng; Xu, Jizheng; Gao, Wen
2017-01-01
Recently, there is a resurgence of interest in uncoded transmission for wireless visual communication. While conventional coded systems suffer from cliff effect as the channel condition varies dynamically, uncoded linear-transformed transmission (ULT) provides elegant quality degradation for wide channel SNR range. ULT skips non-linear operations, such as quantization and entropy coding. Instead, it utilizes linear decorrelation transform and linear scaling power allocation to achieve optimized transmission. This paper presents a theoretical analysis for power-distortion optimization of ULT. In addition to the observation in our previous work that a decorrelation transform can bring significant performance gain, this paper reveals that exploiting the energy diversity in transformed signal is the key to achieve the full potential of decorrelation transform. In particular, we investigated the efficiency of ULT with exact or inexact signal statistics, highlighting the impact of signal energy modeling accuracy. Based on that, we further proposed two practical energy modeling schemes for ULT of visual signals. Experimental results show that the proposed schemes improve the quality of reconstructed images by 3~5 dB, while reducing the signal modeling overhead from hundreds or thousands of meta data to only a few meta data. The perceptual quality of reconstruction is significantly improved.
Power-Distortion Optimization for Uncoded Linear-Transformed Transmission of Images and Videos.
Xiong, Ruiqin; Zhang, Jian; Wu, Feng; Xu, Jizheng; Gao, Wen
2016-10-26
Recently there is a resurgence of interest in uncoded transmission for wireless visual communication. While conventional coded systems suffer from cliff effect as the channel condition varies dynamically, uncoded linear-transformed transmission (ULT) provides elegant quality degradation for wide channel SNR range. ULT skips non-linear operations such as quantization and entropy coding. Instead, it utilizes linear decorrelation transform and linear scaling power allocation to achieve optimized transmission. This paper presents a theoretical analysis for power-distortion optimization of ULT. In addition to the observation in our previous work that a decorrelation transform can bring significant performance gain, this work reveals that exploiting the energy diversity in transformed signal is the key to achieve the full potential of decorrelation transform. In particular, we investigated the efficiency of ULT with exact or inexact signal statistics, highlighting the impact of signal energy modeling accuracy. Based on that, we further proposed two practical energy modeling schemes for ULT of visual signals. Experimental results show that the proposed schemes improve the quality of reconstructed images by 3 5dB, while reducing the signal modeling overhead from hundreds or thousands of meta data to only a few meta data. The perceptual quality of reconstruction is significantly improved.
Cross-layer optimization for video transmission over multirate GMC-CDMA wireless links.
Bandyopadhyay, Saurav K; Partasides, George; Kondi, Lisimachos P
2008-06-01
In this paper, we consider the problem of video transmission over wireless generalized multicarrier code division multiple access (GMC-CDMA) systems. Such systems offer deterministic elimination of multiple access interference. A scalable video source codec is used and a multirate setup is assumed, i.e., each video user is allowed to occupy more than one GMC-CDMA channels. Furthermore, each of these channels can utilize a different number of subcarriers. We propose a cross-layer optimization method to select the source coding rate, channel coding rate, number of subcarriers per GMC-CDMA channel and transmission power per GMC-CDMA channel given a maximum transmission power for each video user and an available chip rate. Universal rate distortion characteristics (URDC) are used to approximate the expected distortion at the receiver. The proposed algorithm is optimal in the operational rate distortion sense, subject to the specific setup used and the approximation caused by the use of the URDC. Experimental results are presented and conclusions are drawn.