Abstract: Aiming at the Multi-objective Dual-resource Flexible Job shop Energy-efficient Scheduling Problem (MO-DFJESP) that considered transfer time between workers and jobs,a model by taking minimum makespan,total energy consumption,total worker costs and maximum worker workload as optimization objectives was constructed,which accounted for variations in workers'skills,proficiency and unit costs.To solve the MO-DFJESP model,a Multi-objective Hybrid Evolution Algorithm and Tabu Search (MO-HEATS) was proposed.A multi-dimensional encoding and decoding scheme based on the characteristics of MO-DFJESP model was devised to represent viable solutions.Utilizing the sigmoid function,an adaptive mechanism was 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 confirmed that the adaptive mechanism and TS component contributed 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),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,提出一种多目标混合进化算法(MO-HEATS)。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(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