工业工程

工业工程 ›› 2024, Vol. 27 ›› Issue (3): 78-86.doi: 10.3969/j.issn.1007-7375.230102

• 智能制造系统与车间调度优化 • 上一篇    下一篇

考虑序列相关准备时间的分布式柔性作业车间调度研究

王有远1,2, 董博文3   

  1. 1. 南昌航空大学 工业工程研究所，江西 南昌 330063;
    2. 南昌市航空复杂系统与智能科学重点实验室，江西 南昌 330063;
    3. 南昌航空大学 飞行器工程学院，江西 南昌 330063
  • 收稿日期:2023-05-21 发布日期:2024-07-12
  • 作者简介:王有远 (1965—)，男，江西省人，教授，博士，主要研究方向为智能制造与制造业信息化
  • 基金资助:
    国家自然科学基金资助项目 (71761028)；航空基金资助项目 (2022Z069056002)

Distributed Flexible Job Shop Scheduling with Sequence-Dependent Setup Times

WANG Youyuan1,2, DONG Bowen3   

  1. 1. Institute of Industrial Engineering, Nanchang Hangkong University, Nanchang 330063, China;
    2. Nanchang Key Laboratory of Aviation Complex Systems and Intelligence Science, Nanchang 330063, China;
    3. School of Aircraft Engineering, Nanchang Hangkong University, Nanchang 330063, China
  • Received:2023-05-21 Published:2024-07-12

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摘要： 针对考虑序列相关准备时间的分布式柔性作业车间调度问题，提出以最小化最大完工时间为优化目标的混合整数线性规划模型，并提出一种改进遗传算法。采用基于负荷均衡的种群初始化方法提高初始种群质量，根据问题特性构造6个局部扰动算子，设计多重局部扰动策略提高算法的局部搜索能力。通过扩展柔性作业车间调度基准生成测试算例，使用正交实验确定算法参数。实验结果表明，所提改进策略能够有效提高算法性能，求解结果优于对比算法，验证了调度模型和所提算法的可行性和有效性。

关键词: 分布式柔性作业车间调度, 序列相关准备时间, 遗传算法, 最大完工时间

Abstract: For the distributed flexible job shop scheduling problem considering sequence-dependent setup times, a mixed-integer linear programming model with the optimization objective of minimizing the makespan is proposed. Also, an improved genetic algorithm is developed. A load-balanced population initialization method is used to improve the quality of the initial population. Six local perturbation operators are constructed according to problem characteristics, and a multiple local perturbation strategy is designed to improve the local search capability of the algorithm. Test cases are generated by extending the flexible job shop scheduling benchmark, and the algorithm parameters are determined by orthogonal experiments. Experimental results show that the proposed strategy can effectively improve the performance of the algorithm, with solutions superior to those obtained by the comparison algorithms, thus verifying the feasibility and effectiveness of the scheduling model and the proposed algorithm.

Key words: distributed flexible job shop scheduling, sequence-dependent setup times, genetic algorithm, makespan

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