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A Potts Neural Network Heuristic for the Class/Teacher Timetabling Problem

  • Marco P. Carrasco
  • Margarida V. Pato
Part of the Applied Optimization book series (APOP, volume 86)

Abstract

This paper describes the application of a neural network metaheuristic to a real timetabling problem, the Class/Teacher Timetabling Problem (CTTP). This problem is known to be a complex, highly constrained optimization problem, thus exhibiting limitations to be solved using classical optimization methods. For this reason, many metaheuristics have been proposed to tackle real CTTP instances. Artificial neural networks have, during the last decade, shown some interesting optimization capabilities supported mainly by the seminal work of Hopfield and Tank. By extending this approach, the current paper proposes a Potts neural network heuristic for the CTTP. Computational tests taken with real instances yield promising results, which suggest that this Potts neural heuristic is an effective method to the solving of this class of timetabling problem.

Keywords

School timetabling Metaheuristics Artificial neural networks Hopfield neural network. 

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Marco P. Carrasco
    • 1
    • 2
  • Margarida V. Pato
    • 3
    • 2
  1. 1.Escola Superior de Gestão, Hotelaria e TurismoUniversity of AlgarvePortimãoPortugal
  2. 2.Centro de Investigação OperacionalUniversity of LisbonLisboaPortugal
  3. 3.lnstituto Superior de Economia e GestãoTechnical University of LisbonLisboaPortugal

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