Combining Neighbourhoods in Fuzzy Job Shop Problems

  • Jorge Puente
  • Camino R. Vela
  • Inés González-Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7023)

Abstract

In the sequel, we propose a new neighbourhood structure for local search for the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan of the resulting schedule. The new neighbourhood structure is based on changing the position of a task in a critical block. We provide feasibility conditions and a makespan estimate which allows to select only feasible and promising neighbours. The experimental results illustrate the success of our proposal in reducing expected makespan within a memetic algorithm. The experiments also show that combining the new structure with an existing neighbourhood from the literature considering both neighborhoods at the same time, provides the best results.

Keywords

Local Search Fuzzy Number Neighbourhood Structure Memetic Algorithm Local Search Procedure 
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 2011

Authors and Affiliations

  • Jorge Puente
    • 1
  • Camino R. Vela
    • 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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