A Pruning Pattern List Approach to the Permutation Flowshop Scheduling Problem

  • Takeshi Yamada
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 15)

Abstract

This paper investigates an approach to the permutation flowshop scheduling problem based on Tabu Search with an additional memory structure called a ‘pruning pattern list’. The pruning pattern list approach allows a better use of the critical block information. A solution of the flowshop scheduling problem is represented by a permutation of job numbers. A pruning pattern is generated from a solution by replacing job numbers inside a critical block with ‘wild cards’ so that solutions that ‘match’ the pattern would be excluded from the search. A set of pruning patterns, which is called a ‘pruning pattern list’, is used to navigate the search by avoiding solutions that would match any pattern on the list. Computational experiments using benchmark problems demonstrate the effectiveness of the proposed approach.

Keywords

Recombination Suffix 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A.H.G. Rinnooy Kan. Machine Scheduling Problems: Classification, Complexity and Computations. Freeman, 1979.Google Scholar
  2. [2]
    H.G. Campbell, R.A. Dudek, and M.L. Smith. A Heuristic Algorithm for the n Job m Machine Sequencing Problem. Management Science, 16:630–637, 1970.CrossRefGoogle Scholar
  3. [3]
    D.G. Dannenbring. An Evaluation of Flow Shop Sequencing Heuristics. Management Science, 23:1174–1182, 1977.MATHCrossRefGoogle Scholar
  4. [4]
    M. Nawaz, E.E. Enscore Jr., and I. Ham. A Heuristic Algorithm for the m-Machine, n-Job Flow-Shop Sequencing Problem. OMEGA, 11:91–95, 1983.CrossRefGoogle Scholar
  5. [5]
    E. Nowicki and C. Smutnicki. A Fast Tabu Search Algorithm for the Permutation Flow-Shop Problem. European Journal of Operational Research, 91:160–175, 1996.MATHCrossRefGoogle Scholar
  6. [6]
    F.A. Ogbu and D.K. Smith. Simulated Annealing for the Permutation Flowshop Problem. OMEGA, 19:64–67, 1990.CrossRefGoogle Scholar
  7. [7]
    M. Widmer and A. Hertz. A New Heuristic Method for the Flow Shop Sequencing Problem. European Journal of Operational Research, 41:186–193, 1989.MATHCrossRefGoogle Scholar
  8. [8]
    I.H. Osman and C.N. Potts. Simulated Annealing for Permutation Flow-Shop Scheduling. OMEGA, 17:551–557, 1989.CrossRefGoogle Scholar
  9. [9]
    C.R. Reeves and T. Yamada. Genetic Algorithms, Path Relinking and the Flowshop Sequencing Problem. Evolutionary Computation Journal, 6:45–60, 1998.CrossRefGoogle Scholar
  10. [10]
    E. Taillard. Some Efficient Heuristic Methods for Flow Shop Sequencing. European Journal of Operational Research, 47:65–74, 1990.MathSciNetMATHCrossRefGoogle Scholar
  11. [11]
    E. Taillard. Benchmarks for Basic Scheduling Problems. European Journal of Operational Research, 64:278–285, 1993.MATHCrossRefGoogle Scholar
  12. [12]
    D. Whitley, T. Starkweather, and D. Fuquay. Scheduling Problems and Traveling Salesman: The Genetic Edge Recombination Operator. In: Proceedings of the Third International Conference on Genetic Algorithms, pages 133–140, Los Altos, Morgan Kaufmann, 1989.Google Scholar
  13. [13]
    T. Yamada and C.R. Reeves. Permutation Flowshop Scheduling by Genetic Local Search. In: Proceedings of the 2nd IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems, pages 232–238, Glasgow, 1997.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Takeshi Yamada
    • 1
  1. 1.NTT Communication Science LaboratoriesKyotoJapan

Personalised recommendations