A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling

Tsung Che Chiang, Hsiao Jou Lin

Research output: Contribution to journalArticlepeer-review

87 Citations (Scopus)

Abstract

This paper addresses the multiobjective flexible job shop scheduling problem (MOFJSP) regarding minimizing the makespan, total workload, and maximum workload. The problem is solved in a Pareto manner, whose goal is to seek for the set of Pareto optimal solutions. We propose a multiobjective evolutionary algorithm, which utilizes effective genetic operators and maintains population diversity carefully. A main feature of the proposed algorithm is its simplicity - it needs only two parameters. Performance of our algorithm is compared with seven state-of-the-art algorithms on fifteen popular benchmark instances. Only our algorithm can find 70% or more non-dominated solutions for every instance.

Original languageEnglish
Pages (from-to)87-98
Number of pages12
JournalInternational Journal of Production Economics
Volume141
Issue number1
DOIs
Publication statusPublished - 2013 Jan 1

Keywords

  • Evolutionary algorithm
  • Flexible job shop scheduling
  • Multiobjective optimization
  • Pareto optimal

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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