An Improved Grey Wolf Optimizer for Flexible Job-shop Scheduling Problem

Ye Jieran, Wang Aimin, Ge Yan, Shen Xinyi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

For the flexible job-shop scheduling problem, an optimal scheduling model with the objective of minimum completion time is established, and an improved grey wolf optimizer is proposed. The algorithm improves the coding method based on Largest Order Value rule of the grey wolf optimizer. At the same time, a local search strategy is introduced to effectively balance the exploration ability and development ability of the algorithm. Finally, experiments verify the effectiveness of the algorithm.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE 11th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages213-217
Number of pages5
ISBN (Electronic)9781728153322
DOIs
Publication statusPublished - Jan 2020
Externally publishedYes
Event11th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2020 - Cape Town, South Africa
Duration: 20 Jan 202022 Jan 2020

Publication series

NameProceedings of 2020 IEEE 11th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2020

Conference

Conference11th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2020
Country/TerritorySouth Africa
CityCape Town
Period20/01/2022/01/20

Keywords

  • Flexible Job-shop Scheduling Problem
  • Grey Wolf Optimizer
  • Local Search
  • component

Fingerprint

Dive into the research topics of 'An Improved Grey Wolf Optimizer for Flexible Job-shop Scheduling Problem'. Together they form a unique fingerprint.

Cite this