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Interval job shop scheduling problems

Interval number theory has been applied to many fields; however, its applications to production scheduling are seldom investigated. In this paper, interval theory is used for its low cost in uncertainty modeling and novel interval job shop scheduling problem is proposed. To build the schedule of the... Full description

Journal Title: International journal of advanced manufacturing technology 2011-09-08, Vol.60 (1-4), p.291-301
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_springer_primary_2011_170_60_1_3600
title: Interval job shop scheduling problems
format: Article
creator:
  • Lei, Deming
subjects:
  • Algorithms
  • Analysis
  • CAE) and Design
  • Chromosomes
  • Computer-Aided Engineering (CAD
  • Computer-Aided Engineering (CAD, CAE) and Design
  • Decoding
  • Engineering
  • Genetic algorithm
  • Genetic algorithms
  • Industrial and Production Engineering
  • Interval number
  • Job shop scheduling
  • Job shops
  • Mechanical Engineering
  • Media Management
  • Number theory
  • Operation-based representation
  • Original Article
  • Production scheduling
  • Production/Logistics/Supply Chain
  • Schedules
  • Usage
ispartof: International journal of advanced manufacturing technology, 2011-09-08, Vol.60 (1-4), p.291-301
description: Interval number theory has been applied to many fields; however, its applications to production scheduling are seldom investigated. In this paper, interval theory is used for its low cost in uncertainty modeling and novel interval job shop scheduling problem is proposed. To build the schedule of the problem, the addition and comparison of two interval numbers are first introduced and then a decoding procedure is constructed by using the chromosome of operation-based representation. It is proved that the possible actual objective values are contained in interval objective. An effective genetic algorithm (GA) is presented and tested by using some randomly generated instances. Computational results show the effectiveness of the GA.
language: eng
source:
identifier: ISSN: 0268-3768
fulltext: no_fulltext
issn:
  • 0268-3768
  • 1433-3015
url: Link


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descriptionInterval number theory has been applied to many fields; however, its applications to production scheduling are seldom investigated. In this paper, interval theory is used for its low cost in uncertainty modeling and novel interval job shop scheduling problem is proposed. To build the schedule of the problem, the addition and comparison of two interval numbers are first introduced and then a decoding procedure is constructed by using the chromosome of operation-based representation. It is proved that the possible actual objective values are contained in interval objective. An effective genetic algorithm (GA) is presented and tested by using some randomly generated instances. Computational results show the effectiveness of the GA.
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subjectAlgorithms ; Analysis ; CAE) and Design ; Chromosomes ; Computer-Aided Engineering (CAD ; Computer-Aided Engineering (CAD, CAE) and Design ; Decoding ; Engineering ; Genetic algorithm ; Genetic algorithms ; Industrial and Production Engineering ; Interval number ; Job shop scheduling ; Job shops ; Mechanical Engineering ; Media Management ; Number theory ; Operation-based representation ; Original Article ; Production scheduling ; Production/Logistics/Supply Chain ; Schedules ; Usage
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descriptionInterval number theory has been applied to many fields; however, its applications to production scheduling are seldom investigated. In this paper, interval theory is used for its low cost in uncertainty modeling and novel interval job shop scheduling problem is proposed. To build the schedule of the problem, the addition and comparison of two interval numbers are first introduced and then a decoding procedure is constructed by using the chromosome of operation-based representation. It is proved that the possible actual objective values are contained in interval objective. An effective genetic algorithm (GA) is presented and tested by using some randomly generated instances. Computational results show the effectiveness of the GA.
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abstractInterval number theory has been applied to many fields; however, its applications to production scheduling are seldom investigated. In this paper, interval theory is used for its low cost in uncertainty modeling and novel interval job shop scheduling problem is proposed. To build the schedule of the problem, the addition and comparison of two interval numbers are first introduced and then a decoding procedure is constructed by using the chromosome of operation-based representation. It is proved that the possible actual objective values are contained in interval objective. An effective genetic algorithm (GA) is presented and tested by using some randomly generated instances. Computational results show the effectiveness of the GA.
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