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Improving Local Search for the Fuzzy Job Shop Using a Lower Bound

  • Jorge Puente
  • Camino R. Vela
  • Alejandro Hernández-Arauzo
  • Inés González-Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5988)

Abstract

We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results illustrate the success of our proposals in reducing both CPU time and number of evaluated neighbours.

Keywords

Local Search Fuzzy Number Critical Path Neighbourhood Structure Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jorge Puente
    • 1
  • Camino R. Vela
    • 1
  • Alejandro Hernández-Arauzo
    • 1
  • Inés González-Rodríguez
    • 2
  1. 1.A.I. Centre and Department of Computer ScienceUniversity of OviedoSpain
  2. 2.Department of Mathematics, Statistics and ComputingUniversity of CantabriaSpain

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