Abstract: For the flexible job shop scheduling problem,considering the transportation time,machine preventive maintenance and energy consumption constraints simultaneously,a mixed-integer programming model that minimized makespan and total energy consumption was established,and a multi-objective discrete Jaya algorithm was proposed to solve this problem.According to the problem's characteristics,a two-layer encoding method based on operation and machine was designed,and the population initialization method that balanced the processing time and energy consumption was adopted to generate a high-quality initial population.To transform the solution into a feasible and effective scheduling scheme,a greedy insertion decoding method with the preventive maintenance dynamic adjustment strategy and transportation time was designed.According to the different situations of the solution,the individual was updated by different ways.The effectiveness of the proposed algorithm was verified by comparing with the commonly used multi-objective optimization algorithms through 18 datasets with different scales.Experimental results showed that the proposed algorithm could effectively solve the flexible job-shop green scheduling considering transportation time and machine preventive maintenance.

Key words: flexible job-shop scheduling, transportation time, preventive maintenance, energy consumption, multi-objective discrete Jaya algorithm

摘要： 针对柔性作业车间调度问题,在同时考虑运输时间、机器预维护和能耗等约束的情况下,建立了最小化完工时间和总能耗的混合整数规划模型,并提出一种多目标离散Jaya算法进行求解。根据问题的特点,设计了基于工序和机器的双层编码方式,并采用均衡加工时间和能耗的种群初始化方法产生高质量的初始种群。为了将解转化为可行有效的调度方案,设计了带有预维护动态调整策略和考虑运输时间的贪婪插入解码方法。根据解的不同情况,采用不同的方式更新个体。将本文设计的算法与常用的多目标优化算法在18组不同规模的测试算例下进行对比分析,验证了所提算法的有效性。实验结果表明,所提算法能够有效解决考虑运输时间和机器预维护的柔性作业车间绿色调度问题。

关键词: 柔性作业车间调度, 运输时间, 预维护, 能耗, 多目标离散Jaya算法