关键词: 多工人协作, 柔性车间调度, 技能学习差异, 改进DeJong学习模型, 稀疏邻域带精英策略的快速非支配排序遗传算法

Abstract: On the basis of considering the difference of workers′ skill learning ability,in order to solve the scheduling problem of multi-worker cooperative flexible workshop,a scheduling method based on sparse neighborhood Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) was proposed. The multi-person cooperative flexible job shop scheduling problem under the premise of considering the difference of learning ability was described. The DeJong learning model was improved,and the multi-objective optimization model of multi-person cooperative flexible job shop scheduling was established. On the basis of NSGA-Ⅱ algorithm,the selection method of neighborhood sparsity was introduced,which effectively retained the chromosomes with rich information and diversity,and then the sparse neighborhood NSGA-Ⅱ was applied to solve the scheduling problem. The experimental results show that the Pareto solution set quality of the sparse neighborhood NSGA-Ⅱ is higher than that of the standard NSGA-Ⅱ and the adaptive Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and the time of the shortest completion time scheduling scheme is 127.1 min,which meets the constraints of logic and time. The experimental results verify the superiority of sparse neighborhood NSGA-Ⅱ in flexible job shop scheduling.

Key words: multi-worker collaboration, flexible job shop scheduling, differences in skill learning, improved DeJong learning model, sparse neighborhood Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ)