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Survey on Multi-objective Flexible Job Shop Scheduling Problem
多目标柔性Job Shop调度问题的技术现状和发展趋势

WU Xiu-li,SUN Shu-dong,YANG Zhan,ZHAI Ying-ni,
吴秀丽
,孙树栋,杨展,翟颖妮

计算机应用研究 , 2007,
Abstract: The paper reviewed literatures about MFJSP(Multi-objective Flexible Job Shop Scheduling Problem).First,it discussed MFJSP,including the scheduling task,assumptions,objectives,different types of the problem and the analysis of the computation complexity.Second,presented a literature review from the viewpoint of the scheduling modeling,optimizing procedures and prototype system.Additionally,discussed a more general multi-job and multi-objective flexible job shop scheduling.Third,summarized the problem and the deficiency of the reviewed literatures.Finally,proposed some future research.
Hybrid particle-swarm optimization for multi-objective flexible job-shop scheduling problem
混合粒子群算法求解多目标柔性作业车间调度问题

ZHANG Jing,WANG Wan-liang,XU Xin-li,JIE Jing,
张静
,王万良,徐新黎,介婧

控制理论与应用 , 2012,
Abstract: Flexible job-shop scheduling is a very important branch in both fields of production management and combinatorial optimization. A hybrid particle-swarm optimization algorithm is proposed to study the mutli-objective flexible job-shop scheduling problem based on Pareto-dominance. First, particles are represented based on job operation and machine assignment, and are updated directly in the discrete domain. Then, a multi-objective local search strategy including Baldwinian learning mechanism and simulated annealing technology is introduced to balance global exploration and local exploitation. Third, Pareto-dominance is applied to compare different solutions, and an external archive is employed to hold and update the obtained non-dominated solutions. Finally, the proposed algorithm is simulated on numerical classical benchmark examples and compared with existing methods. It is shown that the proposed method achieves better performance in both convergence and diversity.
Study on lot-splitting method for multi-objective flexible Job-Shop scheduling problem
多目标柔性车间作业调度分批方法研究

XU Xiu-lin,HU Ke-jin,
许秀林
,胡克瑾

计算机应用研究 , 2011,
Abstract: Flexible size lot-splitting method,for multi-objective flexible Job-Shop scheduling problem,may produce irregular lot-splitting scheme.To resolve this problem,proposed the idea of regular lot-splitting.According to the rule of maximum batch size,it analysed the main factors which determined the best batch size.It presented a lot-splitting scheme that determined batch size by process time of each job.Experiment shows that main indexes of the scheme with different batch size,including make span,are better tha...
Ant colony and particle swarm optimization algorithm-based solution to multi-objective flexible job-shop scheduling problems
基于蚁群粒子群算法求解多目标柔性调度问题

ZHANG Wei-cun,ZHENG Pi-e,WU Xiao-dan,
张维存
,郑丕谔,吴晓丹

计算机应用 , 2007,
Abstract: A hybrid of ant colony and particle swarm optimization algorithms was proposed to solve the multi-objective flexible job-shop scheduling problem based on the analysis of objectives and their relationship. The hybrid was formulated in a form of hierarchical structure. The ant colony algorithm was performed at the master level to minimize the total load and bottleneck load through selecting job-processing route, while the particle swarm optimization algorithm was carried out at the slave level to minimize the makespan through scheduling the operations with machines without violating the result from the master level. The transfer probabilities of ant between machines were designed by using heuristic information of processing time and machine load. The decoding method of particle vector was well designed in order to sequence operations of every machine based on the size relations of element priority values. The simulation and results from comparison with other algorithms demonstrate the effectiveness of the proposed algorithm.
Multi-objective flexible Job-shop scheduling problem in steel tubes production
钢管生产计划中的多目标柔性Job-shop调度问题

LI Lin,HUO Jia-zhen,
李琳
,霍佳震

系统工程理论与实践 , 2009,
Abstract: 基于国内大型钢铁企业中钢管生产的实际,将无缝钢管的生产计划调度抽象为多目标柔性Job-shop问题(MFJSSP).在考虑产线产能各不相同、产线定修、前置库存限制的情形下,构建了混合整数规划模型, 解决①多产线共存情形下的生产路径柔性选择;②以订单的按时完工、各订单的供料尽量连续、规格转换成本最小为目标的多目标生产调度优化.鉴于该问题的NP-hard性, 设计改进的遗传算法进行求解,该模型和算法已被用于无缝钢管冷区生产作业计划软件系统的开发,并在实际运用中取得了良好的效果,对各大钢管企业的生产调度均具有一定的实际指导意义.
求解多目标柔性作业车间调度问题的两阶段混合Pareto蚁群算法
Two??Stage Hybrid Pareto Ant Colony Algorithm for Multi??Objective Flexible Job Shop Scheduling
 [PDF]

赵博选,高建民,陈琨
- , 2016, DOI: 10.7652/xjtuxb201607022
Abstract: 针对多目标柔性作业车间调度问题(FJSP)分解得到的作业分派、排序子问题仍是多目标优化问题的情况,提出了一种求解该问题的分层Pareto优化框架,并采用该框架构建了两阶段混合Pareto蚁群算法的求解算法,其中两个Pareto蚁群系统分别求解多目标作业分派、排序问题。结合GT算法、排产规则评估和过滤第一阶段的分派方案,将具有较好评估全局解的分派方案作为分派阶段的精英档案,并输入给排序蚁群系统获取其非支配调度解,进而获取问题全局非支配解。子问题算法混合了各目标相关的邻域搜索策略,与Pareto蚁群算法结合,以期提高解的质量。通过求解带有平均工件加权延迟时间指标的多个FJSP基准算例,验证了算法的有效性。计算结果表明,该分层Pareto优化框架对原问题进行分层分解,有利于降低原问题的复杂性,相比多数文献,算法能够获得各基准算例Pareto非支配解,从而为分解求解复杂多目标调度优化问题提供了一种途径。
Multi??objective flexible job shop scheduling can be divided into two sub??problems, namely job assignment and sorting, which are often multi??objective optimization problems. Aiming at this situation, this paper presents a layered Pareto optimization frame for multi??objective flexible job shop problem and proposes a two??stage hybrid Pareto ant colony algorithm for multi??objective operation assignment (OA) and operation sequencing (OS) sub??problems. Embedding multiple scheduling rules in GT algorithm is used to evaluate and filter the assignment solutions. The global optimal non??dominated front of the original problem is obtained by scheduling optimization as the elite archive of assignments. Each Pareto ant colony algorithm is combined with the neighborhood search strategies related to different objectives. The co??evolutionary can obtain high??quality solutions to multi??objective FJSP. Finally, by solving four benchmark instances considering minimizing the mean weighted tardiness time, the effectiveness of the method is testified. The simulation results show that the layered Pareto optimization frame helps to reduce complexity of the problem, and compared with other literatures, the proposed algorithm can obtain the Pareto non??dominant solutions of each instance, providing a new way for solving complex multi??objective scheduling problems
A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem  [PDF]
Sayedmohammadreza Vaghefinezhad,Kuan Yew Wong
Mathematics , 2012,
Abstract: Flexible job shop scheduling has been noticed as an effective manufacturing system to cope with rapid development in today's competitive environment. Flexible job shop scheduling problem (FJSSP) is known as a NP-hard problem in the field of optimization. Considering the dynamic state of the real world makes this problem more and more complicated. Most studies in the field of FJSSP have only focused on minimizing the total makespan. In this paper, a mathematical model for FJSSP has been developed. The objective function is maximizing the total profit while meeting some constraints. Time-varying raw material costs and selling prices and dissimilar demands for each period, have been considered to decrease gaps between reality and the model. A manufacturer that produces various parts of gas valves has been used as a case study. Its scheduling problem for multi-part, multi-period, and multi-operation with parallel machines has been solved by using genetic algorithm (GA). The best obtained answer determines the economic amount of production by different machines that belong to predefined operations for each part to satisfy customer demand in each period.
Solving the Flexible Job-Shop Scheduling Problem by a Genetic Algorithm  [PDF]
M. Zandieh,I. Mahdavi,A. Bagheri
Journal of Applied Sciences , 2008,
Abstract: A meta-heuristic approach for solving the flexible job-shop scheduling problem (FJSP) is presented in this study. This problem consists of two sub-problems, the routing problem and the sequencing problem and is among the hardest combinatorial optimization problems. We propose a Genetic Algorithm (GA) for the FJSP. Our algorithm uses several different rules for generating the initial population and several strategies for producing new population for next generation. Proposed GA is tested on benchmark problems and with due attention to the results of other meta-heuristics in this field, the results of GA show that our algorithm is effective and comparable to the other algorithms.
Optimizing combination of job shop scheduling and quadratic assignment problem through multi-objective decision making approach
Mostafa Kazemi,Saeed Poormoaied,Ghasem Eslami
Management Science Letters , 2012,
Abstract: In this paper, we consider job shop scheduling and machine location problem, simultaneously. Processing, transportation, and setup times are defined as deterministic parameters. The purpose of this paper is to determine machine location and job scheduling such that the make span and transportation cost is minimized. Therefore, the proposed model is a multi-objective problem one, where the first objective function minimizes make span and another minimizes the transportation cost. To solve the multi-objective problem, two methods are evaluated. Considering combination of job shop scheduling problem and machine location problem makes the proposed model more complex than job shop scheduling problem, which is an NP-hard problem. Therefore, to solve the proposed model, genetic algorithm as a meta-heuristic algorithm is implemented. To show the efficiency of the proposed genetic algorithm, 6×6 job shop scheduling problems are considered.
A Simulated Annealing Algorithm for Flexible Job-Shop Scheduling Problem  [PDF]
M. Yazdani,M. Gholami,M. Zandieh,M. Mousakhani
Journal of Applied Sciences , 2009,
Abstract: This study addresses the flexible job-shop scheduling problem to minimize makespan. In fact, the FJSP mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines and the second one is to sequence the assigned operations on all machines. Hence, to solve this NP-hard problem, a simulated annealing algorithm is proposed. The meta-heuristic algorithm explores the solution space using a stochastic local search while trying to avoid local optima through accepting probabilistic moves to the worse solutions. The neighborhood search structures of assignment and sequencing are used for generating neighboring solutions to search the solution space. To evaluate the performance of the algorithm, twenty benchmark problems adopted from the literature are used. Consequently, the computational results validate the quality of present approach.
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