Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops

In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until now, research on JIT scheduling problems has been mostly limited to high volume assembly lines rather than job-shop-like systems, due to their c...

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Vydané v:International journal of production research Ročník 52; číslo 12; s. 3688 - 3709
Hlavní autori: Zambrano Rey, Gabriel, Bekrar, Abdelghani, Prabhu, Vittaldas, Trentesaux, Damien
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 18.06.2014
Taylor & Francis LLC
Predmet:
Algorithms
arrival time control
Automatic
Combinatorial analysis
dynamic scheduling
Dynamical systems
Dynamics
Engineering Sciences
flexible job-shop
Gages
Genetic algorithms
Job shops
Joining
Just in time
manufacturing control
Mathematical problems
Optimization algorithms
Quadratic programming
Scheduling
Studies
manufacturing control
genetic algorithms
dynamic scheduling
arrival time control
Just-in-Time
flexible job-shop
ISSN:0020-7543, 1366-588X
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Abstract In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until now, research on JIT scheduling problems has been mostly limited to high volume assembly lines rather than job-shop-like systems, due to their combinatorial complexity. In this paper, we propose a generic strategy for dynamically controlling task schedules by coupling genetic algorithms and distributed arrival-time control to optimise JIT performance. We explore two such hybrid approaches: a sequential approach where the two algorithms work separately and an integrated approach where the distributed arrival time control is embedded into the genetic algorithm. Performance of these approaches is benchmarked with quadratic linear programme solutions to get a gauge of their relative strengths in a static environment. Results from applying these approaches to a job-shop-like automated cell verify their effectiveness for JIT manufacturing under realistic dynamically changing environment.
AbstractList In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until now, research on JIT scheduling problems has been mostly limited to high volume assembly lines rather than job-shop-like systems, due to their combinatorial complexity. In this paper, we propose a generic strategy for dynamically controlling task schedules by coupling genetic algorithms and distributed arrival-time control to optimise JIT performance. We explore two such hybrid approaches: a sequential approach where the two algorithms work separately and an integrated approach where the distributed arrival time control is embedded into the genetic algorithm. Performance of these approaches is benchmarked with quadratic linear programme solutions to get a gauge of their relative strengths in a static environment. Results from applying these approaches to a job-shop-like automated cell verify their effectiveness for JIT manufacturing under realistic dynamically changing environment.
In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until now, research on JIT scheduling problems has been mostly limited to high volume assembly lines rather than job-shop-like systems, due to their combinatorial complexity. In this paper, we propose a generic strategy for dynamically controlling task schedules by coupling genetic algorithms and distributed arrival-time control to optimise JIT performance. We explore two such hybrid approaches: a sequential approach where the two algorithms work separately and an integrated approach where the distributed arrival time control is embedded into the genetic algorithm. Performance of these approaches is benchmarked with quadratic linear programme solutions to get a gauge of their relative strengths in a static environment. Results from applying these approaches to a job-shop-like automated cell verify their effectiveness for JIT manufacturing under realistic dynamically changing environment. [PUBLICATION ABSTRACT]
Author Bekrar, Abdelghani
Zambrano Rey, Gabriel
Prabhu, Vittaldas
Trentesaux, Damien
Author_xml – sequence: 1
  givenname: Gabriel
  surname: Zambrano Rey
  fullname: Zambrano Rey, Gabriel
  email: gzambrano@javeriana.edu.co
  organization: Department of Industrial Engineering, Pontificia Universidad Javeriana
– sequence: 2
  givenname: Abdelghani
  surname: Bekrar
  fullname: Bekrar, Abdelghani
  organization: TEMPO Lab "Production, Services and Information" Team
– sequence: 3
  givenname: Vittaldas
  surname: Prabhu
  fullname: Prabhu, Vittaldas
  organization: Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University
– sequence: 4
  givenname: Damien
  surname: Trentesaux
  fullname: Trentesaux, Damien
  organization: TEMPO Lab "Production, Services and Information" Team
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Issue 12
Keywords manufacturing control
genetic algorithms
dynamic scheduling
arrival time control
Just-in-Time
flexible job-shop
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Snippet In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until...
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SubjectTerms Algorithms
arrival time control
Automatic
Combinatorial analysis
dynamic scheduling
Dynamical systems
Dynamics
Engineering Sciences
flexible job-shop
Gages
Genetic algorithms
Job shops
Joining
Just in time
manufacturing control
Mathematical problems
Optimization algorithms
Quadratic programming
Scheduling
Studies
Title Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops
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