Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling

被引：37
|
作者：
Lei, Deming [1 ]
Wu, Zhiming [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Inst Automat, Shanghai, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2006年 / 30卷 / 1-2期
关键词：
crowding measure; job shop scheduling; multiobjective evolutionary algorithm;
D O I：
10.1007/s00170-005-0029-6
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.
引用
收藏
页码：112 / 117
页数：6
相关论文
共 50 条
  • [21] Enhanced evolutionary algorithms for single and multiobjective optimization in the job shop scheduling problem
    Esquivel, S
    Ferrero, S
    Gallard, R
    Salto, C
    Alfonso, H
    Schütz, M
    KNOWLEDGE-BASED SYSTEMS, 2002, 15 (1-2) : 13 - 25
  • [22] A Novel Quantum Immune Algorithm for Multiobjective Flexible Job Shop Scheduling
    Davarzani, Zohreh
    Akbarzadeh-T, Mohammad-R.
    INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2014, 23 (05)
  • [23] A hybrid evolutionary algorithm to solve the job shop scheduling problem
    T. C. E. Cheng
    Bo Peng
    Zhipeng Lü
    Annals of Operations Research, 2016, 242 : 223 - 237
  • [24] A hybrid evolutionary algorithm to solve the job shop scheduling problem
    Cheng, T. C. E.
    Peng, Bo
    Lu, Zhipeng
    ANNALS OF OPERATIONS RESEARCH, 2016, 242 (02) : 223 - 237
  • [25] A Hybrid Evolutionary Algorithm for Flexible Job Shop Scheduling Problems
    Chun, Wang
    Na, Tian
    Chen, Ji Zhi
    Yan, Wang
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 2690 - 2696
  • [26] A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling
    Deng, Qianwang
    Gong, Guiliang
    Gong, Xuran
    Zhang, Like
    Liu, Wei
    Ren, Qinghua
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2017, 2017
  • [27] A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem
    Ding, Junwen
    Lu, Zhipeng
    Li, Chu-Min
    Shen, Liji
    Xu, Liping
    Glover, Fred
    THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, : 2262 - 2271
  • [28] Multi-objective Job Shop Scheduling Based on Hybrid Evolutionary Algorithm and Knowledge
    Qiu Y.
    Ji W.
    Zhang C.
    Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2020, 31 (24): : 2979 - 2987
  • [29] Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
    Tan, Choo Jun
    Neoh, Siew Chin
    Lim, Chee Peng
    Hanoun, Samer
    Wong, Wai Peng
    Loo, Chu Kong
    Zhang, Li
    Nahavandi, Saeid
    JOURNAL OF INTELLIGENT MANUFACTURING, 2019, 30 (02) : 879 - 890
  • [30] Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
    Choo Jun Tan
    Siew Chin Neoh
    Chee Peng Lim
    Samer Hanoun
    Wai Peng Wong
    Chu Kong Loo
    Li Zhang
    Saeid Nahavandi
    Journal of Intelligent Manufacturing, 2019, 30 : 879 - 890
12345