Abstract: A model for the multi-objective dual-resource flexible job shop energy-efficient scheduling problem (MO-DFJESP),which considers transfer time between workers and jobs,is constructed.The model accounts for variations in workers' skills,proficiency,and unit costs.The optimization objectives involve minimizing makespan,total energy consumption,total worker costs,and maximum worker workload.To solve the MO-DFJESP model,a multi-objective hybrid evolution algorithm and tabu search (MO-HEATS) is proposed.A multidimensional encoding and decoding scheme,based on the characteristics of the MO-DFJESP model,is devised to represent viable solutions.Utilizing the sigmoid function,an adaptive mechanism is designed to balance the developmental and exploratory capabilities of the MO-HEATS algorithm,integrated with tabu search (TS) components to enhance local search abilities.Finally,comparison experiments and ablation experiments were carried out on simulation instances.The comparison experiments affirmed that the MO-HEATS algorithm exhibits significant advantages in addressing the MO-DFJESP model.The results of the ablation experiments confirm that the adaptive mechanism and TS component contribute to the enhancement of the MO-HEATS algorithm.

Key words: dual resource constrained flexible job shop green scheduling, multi-objective, hybrid evolution algorithm, tabu search, transfer time, energy-efficient scheduling

摘要： 针对考虑工人和工件在机器间转移时间的多目标双资源柔性作业车间节能调度问题（MO-DFJESP,many-objective double-flexible job-shop green scheduling problem with worker skill variance and transfer time）,构建了以最小化最大完工时间、总能耗、总工人成本和最大工人工作量为优化目标的数学模型。该模型同时还考虑了工人的技能、熟练度和单位成本差异。为了求解MO-DFJESP模型,提出了一种多目标混合进化算法MO-HEATS(multi-objectives hybrid evolution algorithm and tabu search )。根据MO-DFJESP模型特点,设计了一种多维编码和解码方案以表示问题的可行解。基于sigmoid函数设计了自适应机制,以兼顾MO-HEATS算法的开发和探索能力,并结合禁忌搜索(tabu search,TS)组件提升局部搜索能力。最后,在仿真算例上进行了消融实验和对比实验,实验结果验证了自适应机制和TS组件对MO-HEATS算法性能具有明显提升作用,且MO-HEATS算法对求解MO-DFJESP模型具有显著优势。

关键词: 双资源柔性作业车间调度, 多目标, 混合进化算法, 禁忌搜索, 转移时间, 节能调度