Skip to main content
SpringerLink
Log in

Performance evaluation of the scatter search method for permutation flowshop sequencing problems

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Many optimization problems from the industrial engineering world, in particular manufacturing systems, are very complex in nature and are quite hard to solve by conventional optimization techniques. There has been increasing interest to apply metaheuristic methods to solve such kinds of hard optimization problems. In this work, a novel metaheuristic approach called scatter search (SS) is applied for the n/m/P/C max problem, an NP-hard sequencing problem, which is used to find a processing order of n different jobs to be processed on m machines in the same sequence with minimizing the makespan. SS contrasts with other evolutionary procedures by providing a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions and they exploit the adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. In this paper, various metaheuristic methods and best heuristics from the literature are used for solving the well-known benchmark problem set of Taillard (Eur J Oper Res 64:278–285, 1993). The results available for the various existing metaheuristic and heuristic methods are compared with the results obtained by the SS method. The proposed framework achieves better results for 4 of 12 benchmark problems and also achieves an average deviation of 1.003% from the benchmark problem set of Taillard (Eur J Oper Res 64:278–285, 1993). The computational results show that SS is a more effective metaheuristic for the n/m/P/C max problem.

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. Glover F, Laguna M, Marti R (2000) Fundamentals of scatter search and path relinking. Control Cybern 29(3):653–684

    MATH  MathSciNet  Google Scholar 

  2. Widmer M, Hertz A (1989) A new heuristic method for the flow shop sequencing problem. Eur J Oper Res 41(2):186–193

    Article  MATH  MathSciNet  Google Scholar 

  3. Pinedo M (1995) Scheduling: theory, algorithm, and systems. Prentice-Hall, Englewood Cliffs, New Jersey

    Google Scholar 

  4. Johnson SM (1954) Optimal two- and three-stage production schedules with setup times included. Nav Res Logist Q 1(1):61–68

    Article  Google Scholar 

  5. Lomnicki ZA (1965) A “branch-and-bound” algorithm for the exact solution of the three-machine scheduling problem. Oper Res Q 16(1):89–100

    Google Scholar 

  6. Osman IH, Potts CN (1989) Simulated annealing for permutation flow-shop scheduling. OMEGA Int J Manag Sci 17(6):551–557

    Article  Google Scholar 

  7. Palmer DS (1965) Sequencing jobs through a multi-stage process in the minimum total time—a quick method of obtaining a near optimum. Oper Res Q 16(1):101–107

    MathSciNet  Google Scholar 

  8. Campbell HG, Dudek RA, Smith ML (1970) A heuristic algorithm for the n-job, m-machine sequencing problem. Manage Sci 16(10):630–637

    Article  Google Scholar 

  9. Gupta JND (1971) A functional heuristic algorithm for the flowshop scheduling problem. Oper Res Q 22(1):39–47

    MATH  Google Scholar 

  10. Dannenbring DG (1977) An evaluation of flow shop sequencing heuristics. Manage Sci 23(11):1174–1182

    MATH  Google Scholar 

  11. Rock H, Schmidt G (1982) Machine aggregation heuristics in shop scheduling. Methods Oper Res 45:303–314

    Google Scholar 

  12. Nawaz M, Enscore EE Jr, Ham I (1983) A heuristic algorithm for the m-machine, n-job flow shop sequencing problem. OMEGA Int J Manag Sci 11(1):91–95

    Article  Google Scholar 

  13. Turner S, Booth D (1987) Comparison of heuristics for flow shop sequencing. OMEGA Int J Manag Sci 15(1):75–78

    Article  Google Scholar 

  14. Taillard E (1990) Some efficient heuristic methods for the flow shop sequencing problem. Eur J Oper Res 47(1):65–74

    Article  MATH  MathSciNet  Google Scholar 

  15. Ho JC, Chang Y-L (1990) A new heuristic for the n-job, m-machine flow shop problem. Eur J Oper Res 52(2):194–206

    Article  MathSciNet  Google Scholar 

  16. Ogbu FA, Smith DK (1990) The application of the simulated annealing algorithm to the solution of the n/m/C max flow shop problem. Comput Oper Res 17(3):243–253

    Article  MATH  MathSciNet  Google Scholar 

  17. Nowicki E, Smutnicki C (1996) A fast tabu search algorithm for the permutation flow-shop problem. Eur J Oper Res 91(1):160–175

    Article  MATH  Google Scholar 

  18. Ben-Daya M, Al-Fawzan M (1998) A tabu search approach for the flow shop scheduling problem. Eur J Oper Res 109(1):88–95

    Article  MATH  Google Scholar 

  19. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Massachusetts

    MATH  Google Scholar 

  20. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, Michigan

    Google Scholar 

  21. van Laarhoven PJM, Aarts EHL (1987) Simulated annealing: theory and applications. D. Reidel, Dordrecht, The Netherlands

    MATH  Google Scholar 

  22. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculation by fast computing machine. J Chem Phys 21(6):1087–1092

    Article  Google Scholar 

  23. Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, Boston, Massachusetts

    MATH  Google Scholar 

  24. Nowicki E, Smutnicki C (2006) Some aspects of scatter search in the flow-shop problem. Eur J Oper Res 169(2):654–666

    Article  MATH  MathSciNet  Google Scholar 

  25. Stutzle T (1998) Applying iterated local search to the permutation flow shop problem. Technical report, AIDA-98-04, FG Intellektik, TU Darmstadt, Germany

  26. Ruiz R, Maroto C (2005) A comprehensive review and evaluation of permutation flowshop heuristics. Eur J Oper Res 165(2):479–494

    Article  MATH  MathSciNet  Google Scholar 

  27. Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64:278–285

    Article  MATH  Google Scholar 

  28. Chen C-L, Vempati VS, Aljaber N (1995) An application of genetic algorithms for flow shop problems. Eur J Oper Res 80(2):389–396

    Article  Google Scholar 

  29. Reeves C, Yamada T (1998) Genetic algorithms, path relinking, and the flowshop sequencing problem. Evol Comput 6(1):230–234

    Article  Google Scholar 

  30. Murata T, Ishibuchi H, Tanaka H (1996) Genetic algorithms for flowshop scheduling problems. Comput Ind Eng 30(4):1061–1071

    Article  Google Scholar 

  31. Ponnambalam SG, Aravindan P, Chandrasekaran S (2001) Constructive and improvement flow shop scheduling heuristics: an extensive evaluation. Prod Plan Control 12(4):335–344

    Article  Google Scholar 

  32. Reza Hejazi S, Saghafian S (2005) Flowshop-scheduling problems with makespan criterion: a review. Int J Prod Res 43(14):2895–2929

    Article  MATH  Google Scholar 

  33. Noorul Haq A, Saravanan M, Vivekraj AR, Prasad T (2007) A scatter search approach for general flowshop scheduling problem. Int J Adv Manuf Technol 31(7–8):731–736

    Google Scholar 

  34. Colin RR (1995) A genetic algorithm for flowshop sequencing. Comput Oper Res 22(1):5–13

    Article  MATH  MathSciNet  Google Scholar 

  35. Glover F (1998) A template for scatter search and path relinking. In: Hao JK, Lutton E, Ronald E, Schoenauer M, Snyers D (eds) Lecture notes in computer science, vol 1363, pp 13–54 (expanded version available on request)

  36. Martí R, Laguna M, Campos V (2005) Scatter search vs. genetic algorithms: an experimental evaluation with permutation problems. In: Rego C, Alidaee B (eds) Metaheuristic optimization via adaptive memory and evolution: tabu search and scatter search. Kluwer Academic Publishers, Norwell, Massachusetts, pp 263–282

    Chapter  Google Scholar 

  37. Ying K-C, Liao C-J (2004) An ant colony system for permutation flow-shop sequencing. Comput Oper Res 31(5):791–801

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, R.V.S. College of Engineering & Technology, Dindigul, 624005, Tamil Nadu, India

    M. Saravanan

  2. Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    A. Noorul Haq, A. R. Vivekraj & T. Prasad

Authors
  1. M. Saravanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Noorul Haq
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. R. Vivekraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Saravanan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saravanan, M., Noorul Haq, A., Vivekraj, A.R. et al. Performance evaluation of the scatter search method for permutation flowshop sequencing problems. Int J Adv Manuf Technol 37, 1200–1208 (2008). https://doi.org/10.1007/s00170-007-1053-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-007-1053-5

Keywords

Search

Navigation