Abstract: Distributed manufacturing has become a mainstream production mode in economic globalization.For the Distributed Mixed No-Idle Permutation Flowshop Scheduling Problem with Sequence-Dependent Setup Times（DMNIPFSP/SDST）,a mathematical model of the DMNIPFSP/SDST problem was constructed with the objective of minimizing the maximum makespan,total energy consumption and total tardiness.According to the characteristics of the problem,a Multi-objective Grey Wolf Optimizer（MOGWO）was proposed.To improve the quality and diversity of the initial solution,three improved initialization strategies and the random generation method were proposed.Based on the characteristics of the solving problem,a discrete population update mechanism was designed to balance the global exploration and local exploitation.To further improve the quality and accuracy of the solution,four neighborhood strategies were proposed for local search for different optimization objectives.Simulation results showed that the proposed method could achieve superior performance in solving the multi-objective DMNIPFSP/SDST.

Key words: distributed mixed no-idle permutation flowshop scheduling, sequence-dependent setup times, grey wolf optimizer, multi-objective optimization

摘要： 分布式制造已成为经济全球化背景下的一种主流生产模式,针对考虑序列相关准备时间的分布式混合零空闲置换流水车间调度问题（DMNIPFSP/SDST）,以最小化最大完工时间、总能耗以及总拖期为目标,构建了DMNIPFSP/SDST问题的数学模型,根据问题的特征,提出一种多目标灰狼优化算法（MOGWO）。为了提高初始解的质量和多样性,提出了3种改进的初始化策略以及随机生成相结合的初始化方法;基于所求解问题的特点,设计了一种离散化种群更新机制,以平衡算法的全局探索和局部开发能力;为了进一步提高解的质量和求解精度,针对不同优化目标各提出了4种邻域策略进行局部搜索。仿真结果表明,所提出的方法在解决多目标DMNIPFSP/SDST问题时具有优越的性能。

关键词: 分布式混合零空闲置换流水车间调度, 序列相关准备时间, 灰狼优化算法, 多目标优化