计算机应用 ›› 2017, Vol. 37 ›› Issue (2): 523-529.DOI: 10.11772/j.issn.1001-9081.2017.02.0523

• 人工智能 • 上一篇    下一篇

基于人工蜂群算法的柔性工艺与车间调度集成优化

宋栓军, 杨佩莉, 石雯丽   

  1. 西安工程大学 机电工程学院, 西安 710048
  • 收稿日期:2016-07-18 修回日期:2016-08-24 出版日期:2017-02-10 发布日期:2017-02-11
  • 通讯作者: 杨佩莉,447090664@qq.com
  • 作者简介:宋栓军(1974-),男,陕西西安人,副教授,博士,主要研究方向:生产系统优化、供应链管理;杨佩莉(1992-),女,陕西兴平人,硕士研究生,主要研究方向:生产系统优化;石雯丽(1991-),女,甘肃陇南人,硕士研究生,主要研究方向:供应链管理。
  • 基金资助:
    陕西省教育厅科研基金资助项目(15JK1311);西安工程大学博士科研启动基金资助项目(BS1301);西安工程大学研究生创新基金资助项目(CX201628)。

Optimization of integrated flexible process planning and job shop scheduling based on artificial bee colony

SONG Shuanjun, YANG Peili, SHI Wenli   

  1. College of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an Shaanxi 710048, China
  • Received:2016-07-18 Revised:2016-08-24 Online:2017-02-10 Published:2017-02-11
  • Supported by:
    This work is partially supported by the Scientific Research Fund by Shaanxi Provincial Education Department (15JK1311), Research Start-up Fund for Doctor of Xi'an Polytechnic University (BS1301), Innovation Fund for Graduate Students of Xi'an Polytechnic University (CX201628).

摘要: 为实现柔性工艺与车间调度集成优化,在考虑工件特征的加工工艺、次序及加工机器的柔性基础上,以最小化最大完工时间为优化目标,提出一种基于交叉变异的人工蜂群算法。该算法针对柔性工艺与车间调度集成问题的离散性特征,对工艺路线进行序列编码,工件调度采用基于工序的编码方式。通过工艺种群与调度种群的交叉变异操作,分别使采蜜蜂及观察蜂进行局部寻优,侦查蜂进行全局寻优,以此提高算法性能。在此基础上用两部分测试实例分别验证了集成研究的必要性及改进算法的有效性。

关键词: 柔性工艺规划, 车间调度, 人工蜂群算法

Abstract: To achieve the optimization of integrated flexible process planning and job shop scheduling, taking the flexibility of manufacturing process and order and manufacturing machine of the workpieces into account, for minimizing the maximum completion time of the product processing task, an artificial bee colony algorithm based on crossover and mutation was proposed. Aiming at the discrete characteristics of integrated flexible process and job shop scheduling, the process route was coded in sequence, and the job scheduling was based on the working procedure. To improve the performance of the algorithm, by means of crossover and mutation operation of process population and scheduling population, the employed foragers and onlookers bees seeked local optimality, and the scouts seeked global optimality. On this basis, the necessity of the integration research and the effectiveness of the improved algorithm were verified by two test cases.

Key words: flexible process planning, job shop scheduling, Artificial Bee Colony (ABC)

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