楚雄师范学院学报 ›› 2023, Vol. 38 ›› Issue (3): 76-82.

• 数学 • 上一篇    下一篇

多目标遗传算法求解模糊柔性作业车间调度问题

庄小叶1, 李轲2   

  1. 1.潍坊工程职业学院 信息工程学院,山东 青州 262500;
    2.火箭军士官学校 作战保障系,山东 青州 262500
  • 收稿日期:2022-12-12 出版日期:2023-05-20 发布日期:2023-08-07
  • 作者简介:庄小叶(1983–),女,副教授,研究方向为计算机网络、网络安全、计算机科学与技术。E-mail:50872285@qq.com,Tel:13780813656

Multi-objective Genetic Algorithm to Solve Fuzzy Flexible Job Shop Scheduling Problem

ZHUANG Xiaoye1, LI Ke2   

  1. 1. Department of Information Engineering, Weifang Engineering Vocational College, Qingzhou, Shandong Province 262500;
    2. Department of Operational Support, Rocket Sergeant Academy, Qingzhou, Shandong Province 262500
  • Received:2022-12-12 Online:2023-05-20 Published:2023-08-07

摘要: 建立了模糊数表示工件加工时间和交货期,以最大化平均客户满意度和最小化模糊完工时间为优化目标的多目标模糊柔性作业车间调度问题(Multi-objective fuzzy flexible job shop scheduling problem, MOFFJSP)模型,提出一种改进的多目标遗传算法(Multi-objective genetic algorithm,MOGA)。使用基于工序的编码方式表示调度解,并采用活动化解码方法改进解的质量。采用基于免疫和熵原理设计的交叉和变异算子来构造新解,并利用非支配排序和改进的精英保留策略来提升帕累托解集的多样性。通过仿真实验证明,改进的MOGA能够有效求解MOFFJSP问题模型。

关键词: 模糊柔性作业车间, 多目标遗传算法, 帕累托最优, 免疫和熵原理

Abstract: A multi-objective fuzzy flexible job shop scheduling problem (MOFFJSP) is established that uses fuzzy numbers to represent workpiece processing time and delivery date, and the optimization goal is to maximize average customer satisfaction and minimize fuzzy completion time. Process-based encoding is used to represent scheduling solutions, and active decoding methods to improve the quality of the solutions. The algorithm uses crossover and mutation operators designed based on the principles of immunity and entropy to construct new solutions, and uses non-dominated sorting and improved elite retention Strategies to improve the diversity of Pareto solution sets. Simulation experiments prove that the proposed MOGA can effectively solve the problem model.

Key words: fuzzy flexible job shop, multi-objective genetic algorithm, Pareto optimal, immune and entropy principle

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