期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 6, 期 4, 页码 904-916出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2019.1911540
关键词
Evolutionary algorithm; flexible job shop scheduling; review; swarm intelligence
资金
- National Natural Science Foundation of China [61603169, 61773192, 61803192]
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
- Singapore National Research Foundation [NRF-RSS2016-004]
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.
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