多目标柔性作业车间调度优化问题研究
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摘要
经典作业车间调度问题的研究取得了丰富的理论成果,但是由于所建模型与生产实践的现状相差甚远,很难用于生产实践。随着技术的进步,具有悠久历史的作业车间调度问题研究领域目前正朝着柔性化、多目标化等方向发展。多目标柔性作业车间调度问题是经典作业车间调度问题的重要扩展,它在体现生产柔性的同时,综合考虑企业各部门对调度决策的期望,能更好地适应现代制造系统的需求,其研究有着重要的理论意义和工程实践意义。
本文在综合国内外关于车间调度问题研究状况的基础上,考虑现行作业车间运作的实际情况,对多目标柔性作业车间调度问题进行了深入系统的研究。从车间生产调度理论与方法的研究入手,研究生产中以生产周期、总拖期、成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。本篇论文所做的主要工作有：
(1)针对经典作业车间调度问题的局限性,结合车间生产的实际情况,建立了包括时间、成本、交货期满意度和设备利用率在内的多目标优化模型。
(2)由于遗传算法和粒子群算法在求解多目标柔性作业车间调度问题上的局限性,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。对该算法在多目标柔性作业车间调度优化的应用进行了详细设计。
(3)仿真结果表明,提出的混合遗传算法可以有效解决一般模式下的多目标柔性作业车间调度问题。最后,用该算法求解现实生产实际中两个扩展的多目标生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。
The classical job shop scheduling Problem (JSP) has always being studied by researchers and therefore abundant theories are yielded. However, they are hard to put into practice because of the unrealistic assumptions in setting up the model of JSP. With technologies advance, flexible and multi-objective is the direction of job shop scheduling problem research field which has a long history. Multi-objective flexible job shop scheduling problem (FJSP) is an important extension of JSP, which takes into account not only the flexibility of machine availability but also the different expectations from different departments. Therefore, research on the multi-objective FJSP is significant both in theory and in practice.
On the basis of the technical review on the domestic and foreign research, combining the actual job shop operation, the multi-objective FJSP is studied thoroughly and systematically. The problem of multi-objective flexible job shop scheduling optimization of production is studied, where multi-objects of make span, total tardiness, cost and equipment utilization rate (total and maximum machine tool loads) are concerned, based on the research on the workshop scheduling theories and approaches. The main work of this paper is as follows:
(1) Directing against the limitation of classical job-shop scheduling and then combining the actual conditions of the workshop, the multi-objective flexible job shop scheduling problem optimization model was built, where time, cost, delivery satisfaction and equipment utilization rate were all concerned.
(2) Because the traditional genetic algorithm and particle swarm optimization have localizations in the solution to multi-objective flexible job shop scheduling problem, aiming at improving searching efficiency and searching quality, multi-objective hybrid algorithm combining both advantages of particle swarm optimization and genetic algorithm is presented. This paper designs application of the algorithm in optimizing multi-objective flexible job-shop scheduling.
(3) A simulation experiment is carried out to illustrate that the proposed hybrid genetic algorithm could solve the general multi-objective flexible job shop scheduling problem problem effectively. Finally, from the fact of production, two examples of the expanded multi-objective flexible job shop scheduling optimization in production are addressed. The experimental results can play a definite part in directing production.
本文在综合国内外关于车间调度问题研究状况的基础上,考虑现行作业车间运作的实际情况,对多目标柔性作业车间调度问题进行了深入系统的研究。从车间生产调度理论与方法的研究入手,研究生产中以生产周期、总拖期、成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。本篇论文所做的主要工作有：
(1)针对经典作业车间调度问题的局限性,结合车间生产的实际情况,建立了包括时间、成本、交货期满意度和设备利用率在内的多目标优化模型。
(2)由于遗传算法和粒子群算法在求解多目标柔性作业车间调度问题上的局限性,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。对该算法在多目标柔性作业车间调度优化的应用进行了详细设计。
(3)仿真结果表明,提出的混合遗传算法可以有效解决一般模式下的多目标柔性作业车间调度问题。最后,用该算法求解现实生产实际中两个扩展的多目标生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。
The classical job shop scheduling Problem (JSP) has always being studied by researchers and therefore abundant theories are yielded. However, they are hard to put into practice because of the unrealistic assumptions in setting up the model of JSP. With technologies advance, flexible and multi-objective is the direction of job shop scheduling problem research field which has a long history. Multi-objective flexible job shop scheduling problem (FJSP) is an important extension of JSP, which takes into account not only the flexibility of machine availability but also the different expectations from different departments. Therefore, research on the multi-objective FJSP is significant both in theory and in practice.
On the basis of the technical review on the domestic and foreign research, combining the actual job shop operation, the multi-objective FJSP is studied thoroughly and systematically. The problem of multi-objective flexible job shop scheduling optimization of production is studied, where multi-objects of make span, total tardiness, cost and equipment utilization rate (total and maximum machine tool loads) are concerned, based on the research on the workshop scheduling theories and approaches. The main work of this paper is as follows:
(1) Directing against the limitation of classical job-shop scheduling and then combining the actual conditions of the workshop, the multi-objective flexible job shop scheduling problem optimization model was built, where time, cost, delivery satisfaction and equipment utilization rate were all concerned.
(2) Because the traditional genetic algorithm and particle swarm optimization have localizations in the solution to multi-objective flexible job shop scheduling problem, aiming at improving searching efficiency and searching quality, multi-objective hybrid algorithm combining both advantages of particle swarm optimization and genetic algorithm is presented. This paper designs application of the algorithm in optimizing multi-objective flexible job-shop scheduling.
(3) A simulation experiment is carried out to illustrate that the proposed hybrid genetic algorithm could solve the general multi-objective flexible job shop scheduling problem problem effectively. Finally, from the fact of production, two examples of the expanded multi-objective flexible job shop scheduling optimization in production are addressed. The experimental results can play a definite part in directing production.
引文
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2.苑丽红.基于遗传算法的FMS计划调度方法研究[D].长春：长春理工大学,2005,6.
3.范路桥,常会友,朱旭东.作业调度问题研究[J].现代计算机,2004(187)：21-25.
4.余建国.Job-shop调度优化方法及其应用研究[D].西安：西安理工大学,2005,3.
5. Qiao B, Sun Z J, Zhu J Y. Solving flexible job shop scheduling problem with genetic algorithm [J]. Transactions of Nanjing University of Aeronautics & Astronautics,2001, 18(1):108-113.
6.刘民,吴澄.解决并行多机提前／拖后调度问题的混合遗传算法方法[J].自动化学报,2000,26(2)：258-262.
7.李平,顾幸生.不确定条件下不同交货期窗口的Job Shop调度[J].管理科学学报,2004,7(2)：22-26.
8.刑文训,谢金星.现代优化计算方法[M].北京：清华大学出版社,1999,8.
9.宋庆克,汪希龄,胡铁牛.多属性评价方法及发展评述[J].决策与决策支持系统,1997,7(4)：128-138.
10.李敏强,寇纪松,林丹等.遗传算法的基本理论与应用[M].北京：科学出版社,2002：391-393.
11.陈志平,徐宗本.计算数学[M].北京：科学出版社,2001,68-74.
12.崔逊学.多目标进化算法及其应用[M].北京：国防工业出版社,2006：40-76.
13. BaykasogluA, Ozbakir L, Sonmez A. Using Multiple Objective Tabu Search and Grammars to Model and Solve Multiple-Objective Flexible Job Shop Scheduling Problems [J]. Journal of Intelligent Manufacturing,2004,15(16):777-785.
14. Rigao C. Tardiness Minimization in a Flexible Job Shop:a Tabu Search Approach [J]. Journal of Intelligent Manufacturing,2004,15(1):103-115.
15. Loukil T, Teghem J, Tuyttens D. Solving Multi— objective Production Scheduling Problems Using Metaheuristics [J]. European Journal of Operational Research,2005, 161(1):42-61.
16.Deb K. Non-linear Goal Programming Using Multi—Objective Genetic Algorithms [R]. Technical Report No Cl-60/98, Department of Computer Science/XI, University of Dortmund, Germany,1999.
17. Angel E, Bampis E, Gourves L. Approximation Results for a Bicriteria Job Scheduling Problem on a Single Machine without Preemption [J]. Information Processing Letters, 2005,94(1):19-27.
18. Torabi S A, Karimi B, Fatemi SMT. The Common Cycle Economic Lot Scheduling in Flexible Job Shops:The Finite Horizon Case [J]. International Journal of Production Economics,2005,97 (1):52-65.
19. Ho N B, Tay J C. GENACE:An Efficient Cultural Algorithm Solving the Flexible Job-Shop Problem [C]. The Proceedings of 2004 Congress on Evolutionary Computation, Piscataway:IEEE Press,2004,2:1759-1766.
20. AIvarez-Valdes R, Fuertes A, Tamarit JM, et al. A Heuristic to Schedule Flexible Job-shop in a Glass Factory [J]. European Journal of Operational Research,2005,165(2): 525-534.
21. Kacem I. Genetic Algorithm for the Flexible Job Shop Scheduling Problem [C]. The Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Piscataway: IEEE Press,2003,4:3464-3469.
22.余琦玮,赵亮,潘双夏.基于遗传算法的柔性作业车间调度优化[J].组合机床与自动化加工技术,2004,46(4)：32-34.
23.杨晓梅,曾建潮.遗传算法求解柔性Job-Shop调度问题[J].控制与决策,2004,19(10)：1197-1200.
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