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Metaheuristics Approach for Rule Acquisition in Flexible Shop Scheduling Problems

  • Kazutoshi Sakakibara
  • Hisashi Tamaki
  • Hajime Murao
  • Shinzo Kitamura
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 32)

Abstract

In this paper, we deal with an extended class of flexible shop scheduling problems. A solution is composed under the condition where information on jobs to be processed may not be given beforehand, i.e., under the framework of real-time scheduling. To realize a solution, we apply such a method where jobs are to be dispatched by applying a set of rules (a rule-set), and propose an approach in which rule-sets are generated and improved by using the genetics-based machine learning technique. Through some computational experiments, the effectiveness and the potential of the proposed approach are investigated.

Keywords

Rule acquisition genetics-based machine learning flexible shop scheduling problem simulation 

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Kazutoshi Sakakibara
    • 1
  • Hisashi Tamaki
    • 2
  • Hajime Murao
    • 3
  • Shinzo Kitamura
    • 4
  1. 1.College of Information Science and EngineeringRitsumeikan UniversityKusatsu City, ShigaJapan
  2. 2.Faculty of EngineeringKobe UniversityRokko-dai, Nada-ku, Kobe City, HyogoJapan
  3. 3.Faculty of Cross-Cultural StudiesKobe UniversityKobe City, HyogoJapan
  4. 4.Kobe UniversityRokko-dai, Nada-ku, Kobe City, HyogoJapan

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