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Crossover Operators for the Car Sequencing Problem

  • Arnaud Zinflou
  • Caroline Gagné
  • Marc Gravel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4446)

Abstract

The car sequencing problem involves scheduling cars along an assembly line while satisfying as many assembly line requirements as possible. The car sequencing problem is NP-hard and is applied in industry as shown by the 2005 ROADEF Challenge. In this paper, we introduce three new crossover operators for solving this problem efficiently using a genetic algorithm. A computational experiment compares these three operators on standard car sequencing benchmark problems. The best operator is then compared with state of the art approach for this problem. The results show that the proposed operator consistently produces competitive solutions for most instances.

Keywords

Genetic Algorithm Local Search Utilization Rate Travel Salesman Problem Travel Salesman Problem 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Arnaud Zinflou
    • 1
  • Caroline Gagné
    • 1
  • Marc Gravel
    • 1
  1. 1.Université du Québec à Chicoutimi, 555 boulevard de l’université, Chicoutimi, Qc, G7H2B1Canada

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