柔性路径下基于混合粒子群算法的跨单元调度方法

发布时间：2018-01-13 21:27

本文关键词：柔性路径下基于混合粒子群算法的跨单元调度方法　出处：《北京理工大学》2011年硕士论文　论文类型：学位论文

【摘要】：跨单元调度是在单元制造的基础上发展起来的,通常在单元制造系统中包含两个或多个生产单元。在实际生产过程中,由于单元内生产工艺的局限性,存在某些零件需要两个或多个单元相互协作才能加工完成的情况,本文将那些需要跨单元生产的零件称为异常零件。并根据生产调度的实际情况,对存在柔性路径的跨单元调度问题进行了研究与实现。本文首先介绍了单元制造系统和跨单元调度问题,并且通过分析单元调度问题国内外研究现状,提出了本文研究的重点柔性路径下的跨单元调度问题。然后对柔性路径下的跨单元调度问题进行分析和建模,与目前研究的跨单元调度问题相比,本文提出了异常零件在跨单元生产时存在多条工艺路线,从而产生了路径选择的问题,本文针对跨单元调度问题中柔性路径进行了描述。接下来本文详细介绍了基于禁忌搜索的混合粒子群调度算法,介绍了用混合粒子群算法求解该调度问题的原因,并提出了通过关键路径来求解禁忌搜索中的邻域解。通过仿真实验比较混合粒子群算法和基本粒子群算法在求解该调度问题时的最优值偏差率、CPU时间和机器负载,实验结果表明混合粒子群算法能够有效的解决柔性路径下的跨单元调度问题,与基本粒子群算法相比,效率显著提高。最后,介绍了车间智能排产系统软件的开发,详细介绍了该系统中的分厂月计划设置模块的功能及详细设计方案。
[Abstract]:Cross-cell scheduling is developed on the basis of cell manufacturing, usually contains two or more production units in the cell manufacturing system. In the actual production process, due to the limitations of the production process within the unit. Some parts need two or more units to cooperate with each other to complete the processing. In this paper, the parts that need cross-unit production are called abnormal parts, and according to the actual situation of production scheduling. The cross-cell scheduling problem with flexible paths is studied and implemented. This paper first introduces the cell manufacturing system and the cross-cell scheduling problem, and by analyzing the status quo of the cell scheduling problem at home and abroad, puts forward the cross-cell scheduling problem under the flexible path. Then, the cross-cell scheduling problem under flexible path is analyzed and modeled. Compared with the cross-cell scheduling problem studied at present, this paper proposes that there are many process routes in cross-cell production of abnormal parts. This paper describes the flexible path in cross-cell scheduling problem. Then, this paper introduces the Tabu search based hybrid particle swarm optimization algorithm in detail, and introduces the reasons why the hybrid particle swarm optimization algorithm is used to solve the scheduling problem. A critical path is proposed to solve the neighborhood solution in Tabu search. The optimal deviation rate of the hybrid particle swarm optimization algorithm and the basic particle swarm optimization algorithm in solving the scheduling problem is compared by simulation experiments. The experimental results show that the hybrid particle swarm optimization algorithm can effectively solve the cross-cell scheduling problem in flexible paths. Compared with the basic particle swarm optimization algorithm, the efficiency of the hybrid particle swarm optimization algorithm is significantly improved. Finally, the software development of workshop intelligent production scheduling system is introduced, and the function and detailed design scheme of the monthly planning module in the system are introduced in detail.
【学位授予单位】：北京理工大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH165

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