Review of Swarm Intelligence Algorithms for Multi-objective Flowshop Scheduling

被引：4
|
作者：
He, Lijun [1 ]
Li, Wenfeng [1 ]
Zhang, Yu [1 ]
Cao, Jingjing [1 ]
机构：
[1] Wuhan Univ Technol, Sch Logist Engn, Wuhan, Peoples R China
来源：
INTERNET AND DISTRIBUTED COMPUTING SYSTEMS | 2018年 / 11226卷
基金：
中国国家自然科学基金;
关键词：
Swarm intelligence algorithm; Multi-objective flow shop scheduling; Machine learning; Big data; Multi-objective approach; DIFFERENTIAL EVOLUTION ALGORITHM; GENETIC ALGORITHM; TOTAL FLOWTIME; OPTIMIZATION; MAKESPAN; TARDINESS; MECHANISM; MOBILE;
D O I：
10.1007/978-3-030-02738-4_22
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Swarm intelligence algorithm (SIA) is an important artificial intelligence technology, which has been widely applied in various research fields. Recently, adopting various multi-objective SIAs (MOSIAs) to solve multi-objective flow shop scheduling problem (MOFSP) has attracted wide research attention. However, there are fewer review papers on the MOFSP. Many new MOSIAs have been proposed to solve MOFSP in the last decade. Therefore, in this study, MOSIAs of MOFSP over the past decade are briefly reviewed and analyzed. Based on the existing problems and new trend of Industry 4.0, several new promising future research directions are pointed out. These research directions are: (1) new hybrid MOSIA; (2) MOSIA with high computational efficiency; (3) MOSIA based on machine learning and big data; (4) multi-objective approach; (5) many-objective flowshop scheduling.
引用
收藏
页码：258 / 269
页数：12
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