Journal of Heuristics

, Volume 4, Issue 4, pp 323–357 | Cite as

Constraint Handling in Genetic Algorithms: The Set Partitioning Problem

  • P.C. Chu
  • J.E. Beasley

Abstract

In this paper we present a genetic algorithm-based heuristic for solving the set partitioning problem (SPP). The SPP is an important combinatorial optimisation problem used by many airlines as a mathematical model for flight crew scheduling.

A key feature of the SPP is that it is a highly constrained problem, all constraints being equalities. New genetic algorithm (GA) components: separate fitness and unfitness scores, adaptive mutation, matching selection and ranking replacement, are introduced to enable a GA to effectively handle such constraints. These components are generalisable to any GA for constrained problems.

We present a steady-state GA in conjunction with a specialised heuristic improvement operator for solving the SPP. The performance of our algorithm is evaluated on a large set of real-world problems. Computational results show that the genetic algorithm-based heuristic is capable of producing high-quality solutions.

combinatorial optimisation crew scheduling genetic algorithms set partitioning 

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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • P.C. Chu
    • 1
  • J.E. Beasley
    • 1
  1. 1.The Management SchoolImperial CollegeLondonEngland

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