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Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling

Multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed... Full description

Journal Title: International journal of advanced manufacturing technology 2006-08, Vol.30 (1), p.112-117
Main Author: Lei, Deming
Other Authors: Wu, Zhiming
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: London: Springer-Verlag
ID: ISSN: 0268-3768
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recordid: cdi_crossref_primary_10_1007_s00170_005_0029_6
title: Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling
format: Article
creator:
  • Lei, Deming
  • Wu, Zhiming
subjects:
  • Algorithms
  • Analysis
  • Computer-Aided Engineering (CAD, CAE) and Design
  • Crowding
  • Crowding measure
  • Engineering
  • Evolutionary algorithms
  • Fitness
  • Genetic algorithms
  • Industrial and Production Engineering
  • Job shop scheduling
  • Job shops
  • Mechanical Engineering
  • Multiobjective evolutionary algorithm
  • Multiple objective
  • Production scheduling
  • Production/Logistics
ispartof: International journal of advanced manufacturing technology, 2006-08, Vol.30 (1), p.112-117
description: Multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.
language: eng
source:
identifier: ISSN: 0268-3768
fulltext: no_fulltext
issn:
  • 0268-3768
  • 1433-3015
url: Link


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descriptionMultiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.
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subjectAlgorithms ; Analysis ; Computer-Aided Engineering (CAD, CAE) and Design ; Crowding ; Crowding measure ; Engineering ; Evolutionary algorithms ; Fitness ; Genetic algorithms ; Industrial and Production Engineering ; Job shop scheduling ; Job shops ; Mechanical Engineering ; Multiobjective evolutionary algorithm ; Multiple objective ; Production scheduling ; Production/Logistics
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abstractMultiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.
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