Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

初始化 计算机科学 流水车间调度 模糊逻辑 选择(遗传算法) 趋同(经济学) 进化算法 作业车间调度 工作量 分解 调度(生产过程) 数学优化 人工智能 数学 地铁列车时刻表 生态学 经济 生物 程序设计语言 经济增长 操作系统
作者
Rui Li,Wenyin Gong,Chao Lu
出处
期刊:Computers & Industrial Engineering [Elsevier BV]
卷期号:168: 108099-108099 被引量:86
标识
DOI:10.1016/j.cie.2022.108099
摘要

• The multi-objective FFJSP with two objectives is considered. • Problem-specific initial heuristic and VNS are designed. • A self-adaptive MOEA/D is proposed. • The results indicate the superior performance of our approach. With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, there is a high level of uncertainty in practical processing procedure, particularly in flexible manufacturing systems. This study addresses the multi-objective flexible job shop scheduling problem with fuzzy processing time (MOFFJSP) to minimize the makespan and the total workload simultaneously. A mixed integer liner programming model is presented and a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) is proposed to handle this problem. HPEA has the following features: (i) two problem-specific initial rules considering triangular fuzzy number are presented for hybrid initialization to generate diverse solutions; (ii) five problem-specific local search methods are incorporated to enhance the exploitation; (iii) an effective solution selection method based on Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; and (iv) a parameter selection strategy is proposed to improve the quality of non-dominated solutions. To verify the effectiveness of HPEA, it is compared against other well-known multi-objective optimization algorithms. The results demonstrate that HPEA outperforms these five state-of-the-art multi-objective optimization algorithms in solving MOFFJSP.
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