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

Tsung Che Chiang, Hsiao Jou Lin

Research output: Contribution to journalArticle

72 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

Fingerprint

Evolutionary algorithms
Mathematical operators
Job shop scheduling
Workload

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

Cite this

A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. / Chiang, Tsung Che; Lin, Hsiao Jou.

In: International Journal of Production Economics, Vol. 141, No. 1, 01.01.2013, p. 87-98.

Research output: Contribution to journalArticle

@article{f5711d78a576419c96b20223d3001bf2,
title = "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling",
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.",
keywords = "Evolutionary algorithm, Flexible job shop scheduling, Multiobjective optimization, Pareto optimal",
author = "Chiang, {Tsung Che} and Lin, {Hsiao Jou}",
year = "2013",
month = "1",
day = "1",
doi = "10.1016/j.ijpe.2012.03.034",
language = "English",
volume = "141",
pages = "87--98",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier",
number = "1",

}

TY - JOUR

T1 - A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling

AU - Chiang, Tsung Che

AU - Lin, Hsiao Jou

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

KW - Evolutionary algorithm

KW - Flexible job shop scheduling

KW - Multiobjective optimization

KW - Pareto optimal

UR - http://www.scopus.com/inward/record.url?scp=84869495945&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84869495945&partnerID=8YFLogxK

U2 - 10.1016/j.ijpe.2012.03.034

DO - 10.1016/j.ijpe.2012.03.034

M3 - Article

VL - 141

SP - 87

EP - 98

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - 1

ER -