Multiobjective job shop scheduling using rule-coded genetic local search

Tsung Che Chiang; Li Chen Fu

Multiobjective job shop scheduling using rule-coded genetic local search

Tsung Che Chiang, Li Chen Fu

Department of Computer Science and Information Engineering

Research output: Contribution to conference › Paper › peer-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 language	English
Pages	1764-1775
Number of pages	12
Publication status	Published - 2006 Dec 1
Event	36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 - Taipei, Taiwan Duration: 2006 Jun 20 → 2006 Jun 23

Other

Other	36th International Conference on Computers and Industrial Engineering, ICC and IE 2006
Country	Taiwan
City	Taipei
Period	2006/06/20 → 2006/06/23

Keywords

Dispatching rules
Job shop scheduling
Multiobjective evolutionary algorithms

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

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AB - 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.

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