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A Performance Comparison of Alternative Heuristics for the Flow Shop Scheduling Problem

  • Susana Esquivel
  • Guillermo Leguizamón
  • Federico Zuppa
  • Raúl Gallard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2279)

Abstract

Determining an optimal schedule to minimise the completion time of the last job abandoning the system (makespan) become a very difficult problem when there are more than two machines in the flow shop. Due, both to its economical impact and complexity, attention to solve this problem has been paid by many researchers. Starting with the Johnson’s exact algorithm for the twomachine makespan problem [1], over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem and providing a single acceptable solution. Current trends to solve the FSSP involve Evolutionary Computation and Ant Colony paradigms. This work shows different bio-inspired heuristics for the FSSP, including hybrid versions of enhanced multirecombined evolutionary algorithms and ant colony algorithms [2], on a set of flow shop scheduling instances. A discussion on implementation details, analysis and a comparison of different approaches to the problem is shown.

Keywords

Flow Shop Flow Shop Schedule Problem Travelling Salesperson Problem Flow Shop Problem Multiple Knapsack 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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References

  1. 1.
    Johnson S.: Optimal Two and Three Stage Production Schedule with Setup Times Included. Naval Research Logistic Quarterly, Vol. 1, pp 61–68, 1954.CrossRefGoogle Scholar
  2. 2.
    Dorigo M., V. Maniezzo & A. Colorni: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1), pp 29–41, 1996.CrossRefGoogle Scholar
  3. 3.
    Esquivel S., Zuppa F., Gallard R.: Contrasting Conventional and Evolutionary Approaches for the Flow Shop Scheduling Problem, Second International ICSC Symposium of Intelligent Systems, University of Paisley, Scotland U.K, pp 340–345, 2000.Google Scholar
  4. 4.
    Palmer D.: Sequencing Jobs through a Multistage Process in the Minimun Total Time. A Quick Method of Obtaining a near Optimun., Operational Research Quarterly 16, pp 101–107, 1965.Google Scholar
  5. 5.
    Gupta J.: A Functional Heuristic Algorithm for the Flow Shop Scheduling Problem, Operational Research Quarterly 22, pp 39–48, 1971.zbMATHGoogle Scholar
  6. 6.
    Campbell H., Dudek R., Smith M.: A Heuristic Algorithm for the n Job m Machines Sequencing Problem. Management Science 16, pp 630–637, 1970.CrossRefGoogle Scholar
  7. 7.
    Nawaz M., Enscore E., Ham I.: A Heuristic Algorithm for the m-machine n-job Flow Shop Sequencing Problem. Omeea, Vol. II, pp 11–95, 1983.Google Scholar
  8. 8.
    Esquivel S., Zuppa F., Gallard R.: Using the Stud in Multiple Parents for the Flow Shop Scheduling Problem, invited session on Current Trends in Evolutionary Computation to Face Scheduling Problems, 4th. International ICSC Symposium on Soft Computing and Intelligent Systems for Industry, presentado y publicado, Paisley, Scotland, United Kingdom,pp133, 2001.Google Scholar
  9. 9.
    Esquivel S., Zuppa F., Gallard R.: Multiple Crossover, Multiple Parents and the Stud for Optimization in the Flow Shop Scheduling Problem, World Multiconference on Systemics, Cybernetics and Informatics, Vol III: Emergent Computing and Virtual Engineering, pp 388–392, Orlando, Florida, USA. 2001.Google Scholar
  10. 10.
    Corne D, Dorigo M,and Glover F.,, editors: New Ideas in Optimization. Advanced topics in computer science series. McGraw-Hill, 1999.Google Scholar
  11. 11.
    Colorni A., Dorigo M., Maniezzo V. and Trubian M.: Ant system for Job-shop Scheduling. JORBEL-Belgian Journal of Operations Research, Statistics and Computer Science, 34(1), pp 39–53, 1994.zbMATHGoogle Scholar
  12. 12.
    Dorigo M., Maniezzo V. and Colorni A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1), pp 29, 1996.CrossRefGoogle Scholar
  13. 13.
    Marques C., Zwaan S. Ant Colony Optimization for Job Shop Scheduling, Procedings 3rd Workshop of Genetic Algorithms & Artificial Life, GAAL 99, Lisboa, 1999.Google Scholar
  14. 14.
    Stützle T., den Besten M. and Dorigo M.: Ant Colony Optimization for the Total Weighted Tardiness Problem. In Deb et al, editors, Proceedings of PPSN-VI, Sixth International Conference on Parallel Problem Solving from Nature, volume 1917 of LNCS, pages 611–620, 2000.CrossRefGoogle Scholar
  15. 15.
    Stützle T: An Ant Approach to the Flow Shop Problem, Proceedings of EUFIT’98, Aachen, pp 1560–1564, 1998.Google Scholar
  16. 16.
    Eiben A.E., Raué P-E., and Ruttkay Zs., Genetic algorithms with multi-parent recombination. In Davidor, H.-P. Schwefel, and R. Männer, editors, Proceedings of the 3rd Conference on Parallel Problem Solving from Nature, number 866 in LNCS, pages 78–87. Springer-Verlag, 1994Google Scholar
  17. 17.
    Taillard E.: Benchmarks for Basic Scheduling Problems, European Journal of Operational Research, Vol. 64, pp 278–285, 1993.zbMATHCrossRefGoogle Scholar
  18. 18.
    Chen S. and Smith S.: Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-99, USA, Morgan Kauffman 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Susana Esquivel
    • 1
  • Guillermo Leguizamón
    • 1
  • Federico Zuppa
    • 1
  • Raúl Gallard
    • 1
  1. 1.Laboratorio de Investigación y Desarrollo en Inteligencia ComputacionalUniversidad Nacional de San LuisArgentina

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