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Genetic Algorithms — Constraint Logic Programming. Hybrid Method for Job Shop Scheduling

  • K. Mesghouni
  • P. Pesin
  • S. Hammadi
  • C. Tahon
  • P. Borne
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT)

Abstract

The job-shop scheduling problem is one of hardest problem (NP-complete problem). In lots of cases, the combination of goals and resources has exponentially increasing search space, the generation of consistently good scheduling is particularly difficult because we have very large combinatorial search space and precedence constraints between operations. So this paper shows the cooperation of two methods for the solving of job shop scheduling problems; Genetic Algorithms (GAs) and Constraint Logic Programming (CLP). CLP is a concept based on Operational Research (OR) and Artificial Intelligence (AI). It tends to rid itself of their drawbacks and to regroup their advantages. The GAs are searching algorithms based on the mechanics of natural selection, they employ a probabilistic search for locating the globally optimal solution. That starts with a population of randomly generated chromosomes, but the difficulty resides in the creation of initial population. This paper explains how to use the CLP to generate a first population and we apply the (GAs) to provide a job shop scheduling minimizing a makespan (Cmax) of the jobs.

Keywords

Genetic algorithms Initial population Assignment and scheduling problems Constraint Logic Programming 

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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • K. Mesghouni
    • 1
  • P. Pesin
    • 2
  • S. Hammadi
    • 1
  • C. Tahon
    • 2
  • P. Borne
    • 1
  1. 1.LAIL — URA CNRS D 1440Ecole Centrale de LilleVilleneuve d’Ascq CedexFrance
  2. 2.LAMIH — URA CNRS n° 1775Université de Valenciennes et du Hainaut CambresisValenciennes CedexFrance

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