Multiobjective job shop scheduling using rule-coded genetic local search

Tsung Che Chiang*, Li Chen Fu

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

The multiobjective job shop scheduling problem with mean tardiness and the maximum tardiness as the objectives is addressed. A genetic local search algorithm is proposed with several features. First, it uses a dispatching rule-based genome encoding scheme, and the encoded dispatching rules are chosen carefully. Second, its mating selection mechanism combines the advantages of two representative ones in the literature. Third, it enhances a recently proposed population-based local search procedure. The benefits of the proposed idea are verified through experiments on a public benchmark problem set. In the experiments, the proposed algorithm is also shown to significantly outperform a recent algorithm specific to the multiobjective job shop scheduling problem.

Original languageEnglish
Pages1764-1775
Number of pages12
Publication statusPublished - 2006 Dec 1
Event36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 - Taipei, Taiwan
Duration: 2006 Jun 202006 Jun 23

Other

Other36th International Conference on Computers and Industrial Engineering, ICC and IE 2006
Country/TerritoryTaiwan
CityTaipei
Period2006/06/202006/06/23

Keywords

  • Dispatching rules
  • Job shop scheduling
  • Multiobjective evolutionary algorithms

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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