作业车间调度
强化学习
计算机科学
工作车间
模糊逻辑
人工智能
机器学习
工业工程
运筹学
流水车间调度
数学
操作系统
工程类
地铁列车时刻表
作者
Rui Li,Wenyin Gong,Chao Lu
标识
DOI:10.1016/j.eswa.2022.117380
摘要
The flexible job shop scheduling problem (FJSP) is significant for realistic manufacturing. However, the job processing time usually is uncertain and changeable during manufacturing. This paper presents a multi-objective FJSP with fuzzy processing time (MOFFJSP) for optimizing the makespan and total machine workload as objectives. To solve the MOFFJSP, a MOEA/D based on reinforcement learning named RMOEA/D is proposed. RMOEA/D can be featured as: (i) an initial strategy with three rules is used to get a high-quality initial population; (ii) a parameter adaption strategy based on Q-learning is proposed to guide the population choose the best parameter to increase diversity; (iii) a variable neighborhood search based on reinforcement learning is designed to lead the solution to choose the right local search method; and (iv) an elite archive is used to improve the usage rate of the abandoned historical solution. RMOEA/D is compared with five well-known realted methods, i.e., MOEA/D, NSGA-II, MOEA/D-M2M, NSGA-III and IAIS on three benchmark suites. The results show that RMOEA/D outperforms these five state-of-art algorithms. • The bi-objective FFJSP with two objectives is considered. • An adaptive MOEA/D with VNS is proposed. • The results indicate the superior performance of our approach.
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