A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

被引：5
|
作者：
Kaizhou Gao [1 ,2 ,3 ]
Zhiguang Cao [4 ,5 ]
Le Zhang [6 ]
Zhenghua Chen [6 ]
Yuyan Han [7 ]
Quanke Pan [8 ]
机构：
[1] IEEE
[2] the Macau Institute of Systems Engineering at Macau University of Science and Technology
[3] School of Computer at Liaocheng Univeristy
[4] the Department of Industrial Systems Engineering and Management, National University of Singapore
[5] Centre for Maritime Studies, National University of Singapore
[6] the Institute for Infocomm Research(I2R), the Agency for Science, Technology and Research (ASTAR)
[7] the School of Computer at Liaocheng Univeristy
[8] School of Mechatronic Engineering and Automation,Shanghai University
来源：
IEEE/CAA Journal of Automatica Sinica | 2019年 / 6卷 / 04期
基金：
新加坡国家研究基金会; 中国国家自然科学基金;
关键词：
Evolutionary algorithm; flexible job shop scheduling; review; swarm intelligence;
D O I：
暂无
中图分类号：
TP18 [人工智能理论]; TH165 [柔性制造系统及柔性制造单元];
学科分类号：
080202 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Flexible job shop scheduling problems(FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence(SI) and evolutionary algorithms(EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First,the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm(GA) and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS) for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.
引用
收藏
页码：904 / 916
页数：13
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