Skip to main content
SpringerLink
Log in

A comparison of local search methods for flow shop scheduling

  • General And Hybrid Algorithms
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. E.H.L. Aarts, P.J.M. van Laarhoven, J.K. Lenstra and N.L.J. Ulder, A computational study of local search algorithms for job shop scheduling, ORSA J. Comp. 6(1994)118–125.

    Google Scholar 

  2. E.J. Anderson and M.C. Ferris, Genetic algorithms for combinatorial optimization: The assembly line balancing problem, ORSA J. Comp. 6(1994)161–173.

    Google Scholar 

  3. J.E. Baker, Reducing bias an inefficiency in the selection algorithm, in:Proc. 2nd Int. Conf. on Genetic Algorithms, ed. J.J. Grefenstette (Lawrence Erlbaum Associates, Hillside, NJ, 1987) pp. 14–21.

    Google Scholar 

  4. F. Della Croce, R. Tadei and G. Volta, A genetic algorithm for the job shop problem, Comp. Oper. Res. 22(1995)15–24.

    Article  Google Scholar 

  5. M. Dell'Amico and M. Trubian, Applying tabu-search to the job-shop scheduling problem, Ann. Oper. Res. 41(1992)231–252.

    Article  Google Scholar 

  6. U. Dorndorf and E. Pesch, Evolution based learning in a job shop scheduling environment, Comp. Oper. Res. 22(1995)25–40.

    Article  Google Scholar 

  7. G. Dueck and T. Scheuer, Threshold accepting: A general purpose optimization algorithm appearing superior to simulated annealing, J. Comp. Phys. 90(1988)161–175.

    Article  Google Scholar 

  8. R.W. Eglese, Simulated annealing: A tool for operational research, Euro. J. Oper. Res. 46(1990)271–281.

    Article  Google Scholar 

  9. H. Fisher and G.L. Thompson, Probabilistic learning combinations of local job-shop scheduling rules, in:Industrial Scheduling, ed. J.F. Muth and G.L. Thompson (Prentice-Hall, Englewood Cliffs, NJ, 1963) pp. 225–251.

    Google Scholar 

  10. M.R. Garey, D.S. Johnson and R. Sethi, The complexity of flowshop and jobshop scheduling, Math. Oper. Res. 1(1976)117–129.

    Google Scholar 

  11. C.A. Glass, C.N. Potts and P. Shade, Unrelated parallel machine scheduling using local search, Math. Comp. Mod. 20(1994)41–52.

    Article  Google Scholar 

  12. F. Glover, Future paths for integer programming and links to artificial intelligence, Comp. Oper. Res. 13(1986)533–549.

    Article  Google Scholar 

  13. F. Glover, Tabu search — Part I, ORSA J. Comp. 1(1989)190–206.

    Google Scholar 

  14. F. Glover, Tabu search — Part II, ORSA J. Comp. 2(1990)4–32.

    Google Scholar 

  15. F. Glover, Tabu search: A tutorial, Interfaces 20(1990)74–94.

    Google Scholar 

  16. D.E. Goldberg,Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, MA, 1989).

    Google Scholar 

  17. A.M.A. Hariri and C.N. Potts, Heuristics for scheduling unrelated parallel machine, Comp. Oper. Res. 18(1991)323–331.

    Article  Google Scholar 

  18. A. Hertz and D. de Werra, The tabu search metaheuristic: How we used it, Ann. Math. Art. Int. 1(1990)111–121.

    Article  Google Scholar 

  19. S. Kirkpatrick, C.D. Gelatt, Jr. and M.P. Vecchi, Optimization by simulated annealing, Science 220(1983)671–680.

    Google Scholar 

  20. W.H. Kohler and K. Steiglitz, Exact, approximate and guaranteed accuracy algorithms for the flow-shop problemn/2/F/\(\bar F\), J. ACM 22(1975)106–114.

    Article  Google Scholar 

  21. M.J. Krone and K. Steiglitz, Heuristic programming solution of a flowshop-scheduling problem, Oper. Res. 22(1974)629–638.

    Google Scholar 

  22. S. Lawrence, Resource constrained scheduling: An experimental investigation of heuristic scheduling techniques, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA (1984).

    Google Scholar 

  23. G.E. Liepins and M.R. Hilliard, Genetic algorithms: Foundations and applications, Ann. Oper. Res. 21(1989)31–58.

    Article  Google Scholar 

  24. M. Lundy and A. Mees, Convergence of an annealing algorithm, Math. Progr. 34(1986)111–124.

    Article  Google Scholar 

  25. H. Matsuo, C.J. Suh and R.S. Sullivan, A controlled search simulated annealing method for the general jobshop scheduling problem, Working Paper 03-44-88, Department of Management, University of Texas, Austin, TX (1988).

    Google Scholar 

  26. H. Mühenbein, M. Gorges-Schleuter and O. Krämer, Evolution algorithms in combinatorial optimization, Parallel Comp. 7(1988)65–85.

    Article  Google Scholar 

  27. R. Nakano and T. Yamada, Conventional genetic algorithms for job shop problems, in:Proc. 4th Int. Conf. on Genetic Algorithms, ed. R.K. Belew and L.B. Booker (Morgan Kaufmann, San Mateo, CA, 1991) pp. 474–479.

    Google Scholar 

  28. E. Nowicki and C. Smutnicki, A fast taboo search algorithm for the job shop problem, Preprint 8/93, Instytut Cybernetyki Technicznej, Politnechniki Wroclawskiej, Wroclaw (1993).

    Google Scholar 

  29. F.A. Ogbu and D.K. Smith, The application of the simulated annealing algorithm to the solution of then|m|C max flowshop problem, Comp. Oper. Res. 17(1990)243–253.

    Article  Google Scholar 

  30. F.A. Ogbu and D.K. Smith, Simulated annealing for the permutation flowshop problem, Omega 19(1991)64–67.

    Article  Google Scholar 

  31. I.H. Osman and C.N. Potts, Simulated annealing for permutation flow-shop scheduling, Omega 17(1989)551–557.

    Article  Google Scholar 

  32. C.R. Reeves, Improving the efficiency of tabu search for machine sequencing problems, J. Oper. Res. Soc. 44(1993)375–382.

    Google Scholar 

  33. C.R. Reeves, A genetic algorithm for flowshop sequencing, Comp. Oper. Res. (1994), to appear.

  34. E. Taillard, Some efficient heuristic methods for the flow shop sequencing problem, Euro. J. Oper. Res. 47(1990)65–74.

    Article  MathSciNet  Google Scholar 

  35. E. Taillard, Benchmarks for basic scheduling problems, Euro. J. Oper. Res. 64(1993)278–285.

    Article  Google Scholar 

  36. E. Taillard, Parallel taboo search techniques for the job shop scheduling problem, ORSA J. Comp. 6(1994)108–117.

    Google Scholar 

  37. P.J.M. van Laarhoven and E.H.L. Aarts,Simulated Annealing: Theory and Applications (Kluwer, Dordrecht, 1987).

    Google Scholar 

  38. P.J.M. van Laarhoven, E.H.L. Aarts and J.K. Lenstra, Job shop scheduling by simulated annealing, Oper. Res. 40(1992)113–125.

    Google Scholar 

  39. F. Werner, On the heuristic solution of the permutation flow shop problem by path algorithms, Comp. Oper. Res. 20(1993)707–722.

    Article  Google Scholar 

  40. M. Widner and A. Hertz, A new heuristic method for the flow shop sequencing problem, Euro. J. Oper. Res. 41(1989)186–193.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematical Studies, University of Southampton, SO17 1BJ, Southampton, U.K.

    Celia A. Glass & Chris N. Potts

Authors
  1. Celia A. Glass
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chris N. Potts
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Glass, C.A., Potts, C.N. A comparison of local search methods for flow shop scheduling. Ann Oper Res 63, 489–509 (1996). https://doi.org/10.1007/BF02156631

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02156631

Keywords

Search

Navigation