Flexible job shop scheduling using a multiobjective memetic algorithm

Tsung Che Chiang*, Hsiao Jou Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)


This paper addresses the flexible job shop scheduling problem with minimization of the makespan, maximum machine workload, and total machine workload as the objectives. A multiobjective memetic algorithm is proposed. It belongs to the integrated approach, which deals with the routing and sequencing sub-problems together. Dominance-based and aggregation-based fitness assignment methods are used in the parts of genetic algorithm and local search, respectively. The local search procedure follows the framework of variable neighborhood descent algorithm. The proposed algorithm is compared with three benchmark algorithms using fifteen classic problem instances. Its performance is better in terms of the number and quality of the obtained solutions.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers
Number of pages8
Publication statusPublished - 2011
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 2011 Aug 112011 Aug 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6839 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Intelligent Computing, ICIC 2011


  • Pareto optimal
  • flexible job shop scheduling
  • memetic algorithm
  • multiobjective
  • variable neighborhood descent

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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