Multi-objective flexible job shop scheduling problem based on non-dominated genetic algorithm

Yang Yacong, Wang Aimin*, Ge Yan

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Aiming at the limitation of flexible job shop scheduling (JSP), a multi-objective optimization model of JSP is constructed, which takes the maximum completion time and delivery time as the objective function. Considering the actual inventory size of job shop, a non dominated genetic algorithm is proposed. Combined with the actual inventory and demand, the program is developed to verify the feasibility and effectiveness of the algorithm. The results show that the algorithm can meet the delivery time and the maximum completion time at the same time, considering the size of inventory and achieve satisfactory results.

Original languageEnglish
Title of host publicationProceedings - International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages260-263
Number of pages4
ISBN (Electronic)9781728182889
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes
Event2020 International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2020 - Tianjin, China
Duration: 26 Jun 202028 Jun 2020

Publication series

NameProceedings - International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2020

Conference

Conference2020 International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2020
Country/TerritoryChina
CityTianjin
Period26/06/2028/06/20

Keywords

  • Component
  • Flexible job shop scheduling
  • Multi-objective evolutionary algorithm
  • Non dominated sorting genetic algorithm

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