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
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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