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Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems

This paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous o... Full description

Journal Title: International journal of advanced manufacturing technology 2007-03-24, Vol.37 (1-2), p.157-165
Main Author: Lei, Deming
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_007_0945_8
title: Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems
format: Article
creator:
  • Lei, Deming
subjects:
  • Algorithms
  • Analysis
  • Archives & records
  • CAE) and Design
  • Chromosomes
  • Completion time
  • Computer-Aided Engineering (CAD
  • Decision making
  • Engineering
  • Industrial and Production Engineering
  • Job shop scheduling
  • Job shops
  • Mechanical Engineering
  • Media Management
  • Multiple objective analysis
  • Mutation
  • Original Article
  • Pareto optimization
  • Position measurement
  • Production scheduling
  • Representations
  • Swarm intelligence
ispartof: International journal of advanced manufacturing technology, 2007-03-24, Vol.37 (1-2), p.157-165
description: This paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous optimization ones to handle the problems by using particle swarm optimization. The conversion is implemented by constructing the corresponding relationship between real vector and the chromosome obtained with the new representation method. Pareto archive particle swarm optimization is proposed, in which the global best position selection is combined with the crowding measure-based archive maintenance, and the inclusion of mutation into the proposed algorithm is considered. The proposed algorithm is applied to eight benchmark problems for the following objectives: the minimum agreement index, the maximum fuzzy completion time and the mean fuzzy completion time. Computational results demonstrate that the proposal algorithm has a promising advantage in fuzzy job shop scheduling.
language: eng
source:
identifier: ISSN: 0268-3768
fulltext: no_fulltext
issn:
  • 0268-3768
  • 1433-3015
url: Link


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descriptionThis paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous optimization ones to handle the problems by using particle swarm optimization. The conversion is implemented by constructing the corresponding relationship between real vector and the chromosome obtained with the new representation method. Pareto archive particle swarm optimization is proposed, in which the global best position selection is combined with the crowding measure-based archive maintenance, and the inclusion of mutation into the proposed algorithm is considered. The proposed algorithm is applied to eight benchmark problems for the following objectives: the minimum agreement index, the maximum fuzzy completion time and the mean fuzzy completion time. Computational results demonstrate that the proposal algorithm has a promising advantage in fuzzy job shop scheduling.
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subjectAlgorithms ; Analysis ; Archives & records ; CAE) and Design ; Chromosomes ; Completion time ; Computer-Aided Engineering (CAD ; Decision making ; Engineering ; Industrial and Production Engineering ; Job shop scheduling ; Job shops ; Mechanical Engineering ; Media Management ; Multiple objective analysis ; Mutation ; Original Article ; Pareto optimization ; Position measurement ; Production scheduling ; Representations ; Swarm intelligence
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abstractThis paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous optimization ones to handle the problems by using particle swarm optimization. The conversion is implemented by constructing the corresponding relationship between real vector and the chromosome obtained with the new representation method. Pareto archive particle swarm optimization is proposed, in which the global best position selection is combined with the crowding measure-based archive maintenance, and the inclusion of mutation into the proposed algorithm is considered. The proposed algorithm is applied to eight benchmark problems for the following objectives: the minimum agreement index, the maximum fuzzy completion time and the mean fuzzy completion time. Computational results demonstrate that the proposal algorithm has a promising advantage in fuzzy job shop scheduling.
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