Many-Objective Job-Shop Scheduling : A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Si-Chen LIU, Zong-Gan CHEN, Zhi-Hui ZHAN, Sang-Woon JEON, Sam KWONG, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

32 Citations (Scopus)

Abstract

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.
Original languageEnglish
Pages (from-to)1460-1474
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume53
Issue number3
Early online date13 Sept 2021
DOIs
Publication statusPublished - Mar 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Archive sharing technique (AST)
  • archive update strategy (AUS)
  • genetic algorithm (GA)
  • many-objective job-shop scheduling problem (MaJSSP)
  • many-objective optimization
  • multiple populations for multiple objectives (MPMO)

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