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Scheduling stochastic job shop subject to random breakdown to minimize makespan

The problem of scheduling stochastic job shop subject to breakdown is seldom considered. This paper proposes an efficient genetic algorithm (GA) for the problem with exponential processing time and non-resumable jobs. The objective is to minimize the stochastic makespan itself. In the proposed GA, a... Full description

Journal Title: International journal of advanced manufacturing technology 2011-01-12, Vol.55 (9-12), p.1183-1192
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_010_3151_z
title: Scheduling stochastic job shop subject to random breakdown to minimize makespan
format: Article
creator:
  • Lei, Deming
subjects:
  • Algorithms
  • Breakdown
  • CAE) and Design
  • Computer simulation
  • Computer-Aided Engineering (CAD
  • Computer-Aided Engineering (CAD, CAE) and Design
  • Decoding
  • Discrete event driven
  • Engineering
  • Genetic algorithms
  • Industrial and Production Engineering
  • Job shop scheduling
  • Job shops
  • Mathematical optimization
  • Mechanical Engineering
  • Media Management
  • Optimization
  • Original Article
  • Production scheduling
  • Production/Logistics/Supply Chain
  • Random key representation
  • Random variables
  • Schedules
  • Simulated annealing
  • Stochastic job shop scheduling
  • Stochastic order
  • Swarm intelligence
ispartof: International journal of advanced manufacturing technology, 2011-01-12, Vol.55 (9-12), p.1183-1192
description: The problem of scheduling stochastic job shop subject to breakdown is seldom considered. This paper proposes an efficient genetic algorithm (GA) for the problem with exponential processing time and non-resumable jobs. The objective is to minimize the stochastic makespan itself. In the proposed GA, a novel random key representation is suggested to represent the schedule of the problem and a discrete event-driven decoding method is applied to build the schedule and handle breakdown. Probability stochastic order and the addition operation of exponential random variables are also used to calculate the objective value. The proposed GA is applied to some test problems and compared with a simulated annealing and a particle swarm optimization . The computational results show the effectiveness of the GA and its promising advantage on stochastic scheduling.
language: eng
source:
identifier: ISSN: 0268-3768
fulltext: no_fulltext
issn:
  • 0268-3768
  • 1433-3015
url: Link


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descriptionThe problem of scheduling stochastic job shop subject to breakdown is seldom considered. This paper proposes an efficient genetic algorithm (GA) for the problem with exponential processing time and non-resumable jobs. The objective is to minimize the stochastic makespan itself. In the proposed GA, a novel random key representation is suggested to represent the schedule of the problem and a discrete event-driven decoding method is applied to build the schedule and handle breakdown. Probability stochastic order and the addition operation of exponential random variables are also used to calculate the objective value. The proposed GA is applied to some test problems and compared with a simulated annealing and a particle swarm optimization . The computational results show the effectiveness of the GA and its promising advantage on stochastic scheduling.
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subjectAlgorithms ; Breakdown ; CAE) and Design ; Computer simulation ; Computer-Aided Engineering (CAD ; Computer-Aided Engineering (CAD, CAE) and Design ; Decoding ; Discrete event driven ; Engineering ; Genetic algorithms ; Industrial and Production Engineering ; Job shop scheduling ; Job shops ; Mathematical optimization ; Mechanical Engineering ; Media Management ; Optimization ; Original Article ; Production scheduling ; Production/Logistics/Supply Chain ; Random key representation ; Random variables ; Schedules ; Simulated annealing ; Stochastic job shop scheduling ; Stochastic order ; Swarm intelligence
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0Springer-Verlag London Limited 2011
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abstractThe problem of scheduling stochastic job shop subject to breakdown is seldom considered. This paper proposes an efficient genetic algorithm (GA) for the problem with exponential processing time and non-resumable jobs. The objective is to minimize the stochastic makespan itself. In the proposed GA, a novel random key representation is suggested to represent the schedule of the problem and a discrete event-driven decoding method is applied to build the schedule and handle breakdown. Probability stochastic order and the addition operation of exponential random variables are also used to calculate the objective value. The proposed GA is applied to some test problems and compared with a simulated annealing and a particle swarm optimization . The computational results show the effectiveness of the GA and its promising advantage on stochastic scheduling.
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