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
下载
收藏
页码：258 / 269
页数：12
相关论文
共 50 条
  • [41] Swarm Intelligence for Multi-Objective Optimization of Synthesis Gas Production
    Ganesan, T.
    Vasant, P.
    Elamvazuthi, I.
    Shaari, Ku Zilati Ku
    [J]. PROCEEDINGS OF THE SIXTH GLOBAL CONFERENCE ON POWER CONTROL AND OPTIMIZATION, 2012, 1499 : 317 - 324
  • [42] Research on improved multi-objective particle swarm optimization algorithms
    Zhao, Duo
    Jin, Weidong
    [J]. APPLIED ARTIFICIAL INTELLIGENCE, 2006, : 231 - +
  • [43] CYBER SWARM ALGORITHMS FOR MULTI-OBJECTIVE NURSE ROSTERING PROBLEM
    Yin, Peng-Yeng
    Chiang, Ya-Tzu
    [J]. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2013, 9 (05): : 2043 - 2063
  • [44] Survey of multi-objective particle swarm optimization algorithms and their applications
    Ye Q.
    Wang W.
    Wang Z.
    [J]. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2024, 58 (06): : 1107 - 1120+1232
  • [45] Cultural particle swarm algorithms for constrained multi-objective optimization
    Gao, Fang
    Zhao, Qiang
    Liu, Hongwei
    Cui, Gang
    [J]. COMPUTATIONAL SCIENCE - ICCS 2007, PT 4, PROCEEDINGS, 2007, 4490 : 1021 - +
  • [46] Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem
    Seyed Habib A. Rahmati
    M. Zandieh
    M. Yazdani
    [J]. The International Journal of Advanced Manufacturing Technology, 2013, 64 : 915 - 932
  • [47] Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem
    Rahmati, Seyed Habib A.
    Zandieh, M.
    Yazdani, M.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2013, 64 (5-8): : 915 - 932
  • [48] Multi-objective optimization by genetic algorithms: A review
    Tamaki, H
    Kita, H
    Kobayashi, S
    [J]. 1996 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION (ICEC '96), PROCEEDINGS OF, 1996, : 517 - 522
  • [49] A multi-objective electromagnetism algorithm for a bi-objective hybrid no-wait flowshop scheduling problem
    Khalili, Majid
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 70 (9-12): : 1591 - 1601
  • [50] A multi-objective electromagnetism algorithm for a bi-objective hybrid no-wait flowshop scheduling problem
    Majid Khalili
    [J]. The International Journal of Advanced Manufacturing Technology, 2014, 70 : 1591 - 1601
12345