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Solving fuzzy job shop scheduling problems using random key genetic algorithm

This paper addresses job shop scheduling problems with fuzzy processing time and fuzzy trapezoid or doublet due date. An efficient random key genetic algorithm (RKGA) is suggested to maximize the minimum agreement index and to minimize the maximum fuzzy completion time. In RKGA, a random key represe... Full description

Journal Title: International journal of advanced manufacturing technology 2009-10-31, Vol.49 (1-4), p.253-262
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_009_2379_y
title: Solving fuzzy job shop scheduling problems using random key genetic algorithm
format: Article
creator:
  • Lei, Deming
subjects:
  • Algorithms
  • Analysis
  • CAE) and Design
  • Completion time
  • Computer-Aided Engineering (CAD
  • Computer-Aided Engineering (CAD, CAE) and Design
  • Crossovers
  • Decoding
  • Engineering
  • Fuzzy processing time
  • Genetic algorithm
  • Genetic algorithms
  • Industrial and Production Engineering
  • Job shop scheduling
  • Job shops
  • Mechanical Engineering
  • Media Management
  • Original Article
  • Production scheduling
  • Production/Logistics
  • Random key representation
  • Representations
ispartof: International journal of advanced manufacturing technology, 2009-10-31, Vol.49 (1-4), p.253-262
description: This paper addresses job shop scheduling problems with fuzzy processing time and fuzzy trapezoid or doublet due date. An efficient random key genetic algorithm (RKGA) is suggested to maximize the minimum agreement index and to minimize the maximum fuzzy completion time. In RKGA, a random key representation and a new decoding strategy are proposed and two-point crossover (TPX) and discrete crossover (DX) are considered. RKGA is applied to some fuzzy scheduling instances and performance analyses on random key representation, and the comparison between RKGA and other algorithms are done. Computation results validate the effectiveness of random key representation and the promising advantage of RKGA on fuzzy 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 job shop scheduling problems with fuzzy processing time and fuzzy trapezoid or doublet due date. An efficient random key genetic algorithm (RKGA) is suggested to maximize the minimum agreement index and to minimize the maximum fuzzy completion time. In RKGA, a random key representation and a new decoding strategy are proposed and two-point crossover (TPX) and discrete crossover (DX) are considered. RKGA is applied to some fuzzy scheduling instances and performance analyses on random key representation, and the comparison between RKGA and other algorithms are done. Computation results validate the effectiveness of random key representation and the promising advantage of RKGA on fuzzy scheduling.
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subjectAlgorithms ; Analysis ; CAE) and Design ; Completion time ; Computer-Aided Engineering (CAD ; Computer-Aided Engineering (CAD, CAE) and Design ; Crossovers ; Decoding ; Engineering ; Fuzzy processing time ; Genetic algorithm ; Genetic algorithms ; Industrial and Production Engineering ; Job shop scheduling ; Job shops ; Mechanical Engineering ; Media Management ; Original Article ; Production scheduling ; Production/Logistics ; Random key representation ; Representations
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abstractThis paper addresses job shop scheduling problems with fuzzy processing time and fuzzy trapezoid or doublet due date. An efficient random key genetic algorithm (RKGA) is suggested to maximize the minimum agreement index and to minimize the maximum fuzzy completion time. In RKGA, a random key representation and a new decoding strategy are proposed and two-point crossover (TPX) and discrete crossover (DX) are considered. RKGA is applied to some fuzzy scheduling instances and performance analyses on random key representation, and the comparison between RKGA and other algorithms are done. Computation results validate the effectiveness of random key representation and the promising advantage of RKGA on fuzzy scheduling.
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