A hybrid constraint programming/local search approach to the job-shop scheduling problem

  • Authors:

  • Jean-Paul Watson;J. Christopher Beck

  • Affiliations:

  • Discrete Math and Complex Systems Department, Sandia National Laboratories, Albuquerque, New Mexico;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada

  • Venue:
  • CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
  • Year:
  • 2008

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Abstract

Since their introduction, local search algorithms - and in particular tabu search algorithms - have consistently represented the state-of-the-art in solution techniques for the classical job-shop scheduling problem. This is despite the availability of powerful search and inference techniques for scheduling problems developed by the constraint programming community. In this paper, we introduce a simple hybrid algorithm for job-shop scheduling that leverages both the fast, broad search capabilities of modern tabu search and the scheduling-specific inference capabilities of constraint programming. The hybrid algorithm significantly improves the performance of a state-of-the-art tabu search for the job-shop problem, and represents the first instance in which a constraint programming algorithm obtains performance competitive with the best local search algorithms. Further, the variability in solution quality obtained by the hybrid is significantly lower than that of pure local search algorithms. As an illustrative example, we identify twelve new best-known solutions on Taillard's widely studied benchmark problems.