Considering the uncertainty of the processing time in an actual production workshop,the processing time is expressed in the form of fuzzy numbers.A multi-objective Fuzzy Flexible Job-shop Scheduling Problem(FFJSP)model that minimizes the fuzzy maximum completion time and the fuzzy total cost is presented,and an Improved Multi-Objective Evolutionary Algorithm based on Decomposition(IMOE A/D)is proposed.In this algorithm,a two-layer encoding method based on operations and machines is used for encoding,and a mixed initialization strategy is used to improve the quality of the initial population.An insertion greedy decoding method is used to decode and minimize the total machine processing time.The selection operation based on neighborhood and external population combined with improved crossover and mutation operators are used to update the population to accelerate searching.The condition for neighborhood search is set,and a variable neighborhood search based on four neighborhood actions is performed to improve the local search ability of the algorithm.A Taguchi design of the experimental method is used to examine the effects of key parameters on the algorithm.Simultaneously,the optimal performance parameters of the algorithm are obtained.The proposed algorithm is compared with other algorithms based on the Xu 1-Xu 2,Lei 1-Lei 4,and Remanu 1-Remanu 4 datasets.The results show that the IMOEA/D algorithm obtains better solutions and objective function values than the other algorithms.The number of solutions obtained by the proposed algorithm based on Lei 2 is more than twice those obtained by the other algorithms.
关键词
模糊柔性作业车间调度问题/基于分解的多目标进化算法/混合初始化/选择策略/邻域搜索
Key words
Fuzzy Flexible Job-shop Scheduling Problem(FFJSP)/Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D)/hybrid initialization/selection strategy/neighborhood search
维普
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