Multi-objective Evolutionary Algorithm to Solve Fuzzy Flexible Job Shop Scheduling Problem

被引：0

作者：

Wang C. ^{[1
]}

Tian N. ^{[2
]}

Ji Z.-C. ^{[1
]}

Wang Y. ^{[1
]}

机构：

[1] Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi, 214122, Jiangsu

[2] School of Humanities, Jiangnan University, Wuxi, 214122, Jiangsu

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2017年 / 45卷 / 12期

关键词：

Fuzzy critical operation; Fuzzy flexible job shop scheduling; Local search; Multi-objective evolutionary algorithm; Possibility degree;

D O I：

10.3969/j.issn.0372-2112.2017.12.012

中图分类号：

学科分类号：

摘要：

Seeing that the processing time is uncertain in the actual manufacturing workshop, a multi-objective fuzzy flexible job shop scheduling model is established, and then an effective multi-objective evolutionary algorithm (MOEA) is proposed to solve this model. First, a method of mixing different machine allocation and operation sequencing strategies is adopted to generate initial population and a well-designed greedy inserting algorithm is adopted for chromosome decoding. Second, a Pareto dominant relation based on possibility degree and a modified crowding distance measure in decision space are defined and further employed to improve the fast nondominated sorting. Moreover, a problem-specific local search based on fuzzy critical path theory is incorporated into MOEA. Afterwards, the influence of key parameters is investigated based on the Taguchi method of experiment. Finally, extensive comparison with three existing algorithms is carried out, and the results demonstrate the effectiveness of the proposed algorithm. © 2017, Chinese Institute of Electronics. All right reserved.

引用

页码：2909 / 2916

页数：7

共 18 条

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