Journal of Heuristics

, Volume 16, Issue 6, pp 749–770 | Cite as

A scatter search based hyper-heuristic for sequencing a mixed-model assembly line

  • Jaime Cano-Belmán
  • Roger Z. Ríos-Mercado
  • Joaquín Bautista
Article

Abstract

We address a mixed-model assembly-line sequencing problem with work overload minimization criteria. We consider time windows in work stations of the assembly line (closed stations) and different versions of a product to be assembled in the line, which require different processing time according to the work required in each work station. In a paced assembly line, products are feeded in the line at a predetermined constant rate (cycle time). Then, if many products with processing time greater than cycle time are feeded consecutively, work overload can be produced when the worker has insufficient time to finish his/her job. We propose a scatter search based hyper-heuristic for this NP-hard problem. In the low-level, the procedure makes use of priority rules through a constructive procedure. Computational experiments over a wide range of instances from the literature show the effectiveness of the proposed hyper-heuristics when compared to existing heuristics. The relevance of the priority rules was evaluated as well.

Just-in-time scheduling Assembly line Priority rules Work overload Scatter search Hyper-heuristic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bard, J.F., Dar-El, E., Shtub, A.: An analytic framework for sequencing mixed model assembly lines. Int. J. Prod. Res. 30, 35–48 (1992) MATHCrossRefGoogle Scholar
  2. Bautista, J., Cano, J.: Minimizing work overload in mixed-model assembly lines. Int. J. Prod. Econ. 112, 177–191 (2008) CrossRefGoogle Scholar
  3. Bolat, A.: Efficient methods for sequencing minimum job sets on mixed model assembly lines. Nav. Res. Logist. 44, 419–437 (1997a) MATHCrossRefMathSciNetGoogle Scholar
  4. Bolat, A.: Stochastic procedures for scheduling minimum jobs sets on mixed model assembly lines. J. Oper. Res. Soc. 48, 490–501 (1997b) MATHGoogle Scholar
  5. Bolat, A.: A mathematical model for sequencing mixed models with due dates. Int. J. Prod. Res. 41, 897–918 (2003) MATHCrossRefGoogle Scholar
  6. Bolat, A., Yano, C.A.: Scheduling algorithms to minimize utility work at a single station on a paced assembly line. Prod. Plan. Control 3, 393–405 (1992) CrossRefGoogle Scholar
  7. Boysen, N., Fliedner, M., Scholl, A.: Sequencing mixed-model assembly lines: survey, classification and model critique. Eur. J. Oper. Res. 192, 349–373 (2009) MATHCrossRefMathSciNetGoogle Scholar
  8. Burke, E., Petrovic, S.: Recent research directions in automated timetabling. Eur. J. Oper. Res. 140, 266–280 (2002) MATHCrossRefGoogle Scholar
  9. Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer, Boston (2003) Google Scholar
  10. Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A classification of hyper-heuristic approaches. School of Computer Science and Information Technology, University of Nottingham. Computer Science Technical Report No. NOTTCS-TR-SUB-0907061259-5808 (2009a) Google Scholar
  11. Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: A survey of hyper-heuristics. School of Computer Science and Information Technology, University of Nottingham. Computer Science Technical Report No. NOTTCS-TR-SUB-0906241418-2747 (2009b) Google Scholar
  12. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: Exploring hyper-heuristic methodologies with genetic programming. In: Mumford, C., Jain, L. (eds.) Collaborative Computational Intelligence. Springer, Berlin (2009c) Google Scholar
  13. Chakhlevitch, K., Cowling, P.I.: Hyper-heuristics: recent develops. In: Cotta, C., Sevaux, M., Sörensen, K. (eds.) Adaptive and Multilevel Meta-Heuristics. Studies in Computational Intelligence, vol. 136, pp. 3–29. Springer, Berlin (2008) CrossRefGoogle Scholar
  14. Erel, E., Gocgunz, Y., Sabuncuoğlu, I.: Mixed-model assembly line sequencing using beam search. Int. J. Prod. Res. 45, 5265–5284 (2007) MATHGoogle Scholar
  15. Hyun, C.J., Kim, Y., Kim, Y.K.: A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Comput. Oper. Res. 25, 675–690 (1998) MATHCrossRefGoogle Scholar
  16. ILOG Inc.: CPLEX 9.0 Reference Manual (2003) Google Scholar
  17. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 265–323 (1999) CrossRefGoogle Scholar
  18. Kim, H.G., Cho, H.S.: Sequencing in a mixed-model final assembly line with three goals: simulated annealing approach. Int. J. Ind. Eng. 10, 607–613 (2003) Google Scholar
  19. Kotani, S., Ito, T., Ohno, K.: Sequencing problem for a mixed-model assembly line in the Toyota production system. Int. J. Prod. Res. 42, 4955–4974 (2004) MATHCrossRefGoogle Scholar
  20. Laguna, M., Martí, R.: Scatter Search. Kluwer, Boston (2003) MATHGoogle Scholar
  21. Monden, Y.: Toyota Production System: An Integrated Approach to Just-In-Time. Eng. Manag. Press, Norcross (1998) Google Scholar
  22. Okamura, K., Yamashina, H.: A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor. Int. J. Prod. Res. 17, 233–247 (1979) CrossRefGoogle Scholar
  23. Rahimi-Vahed, A., Mirzaei, A.H.: A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Comput. Ind. Eng. 53, 642–666 (2007) CrossRefGoogle Scholar
  24. Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 529–556. Springer, Berlin (2005), Chap. 17 Google Scholar
  25. Ross, P., Schulenburg, S., Marin-Blazquez, J., Hart, E.: Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. In Kauffmann, M. (ed.). Proc. of the Genet. and Evol. Comput. Conf. GECCO 2002, pp. 942–948, New York, July 2002 Google Scholar
  26. Ruibin, B.: An investigation of Novel approaches for optimizing retail shelf space allocation. PhD thesis, School of Computer Science and Information Technology, University of Nottingham (September 2005) Google Scholar
  27. Sarker, B.R., Pan, H.X.: Designing a mixed-model assembly line to minimize the cost of idle and utility times. Comput. Ind. Eng. 34, 609–628 (1998) CrossRefGoogle Scholar
  28. Scholl, A.: Balancing and Sequencing of Assembly Lines. Physica-Verlag, Heidelberg (1999) Google Scholar
  29. Scholl, A., Klein, R., Domschke, W.: Pattern based vocabulary building for effectively sequencing mixed-model assembly lines. J. Heuristics 4, 359–381 (1998) MATHCrossRefGoogle Scholar
  30. Soubeiga, E.: Development and application of hyper-heuristics to personnel scheduling. PhD thesis, School of Computer Science and Information Technology, University of Nottingham (June 2003) Google Scholar
  31. Thomopoulus, N.T.: Line balancing–sequencing for mixed model assembly. Manag. Sci. 14, B59–B75 (1967) Google Scholar
  32. Wolpert, D., MacReady, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997) CrossRefGoogle Scholar
  33. Xiaobo, Z., Ohno, K.: Algorithms for sequencing mixed models on an assembly line in a JIT production system. Comput. Ind. Eng. 31, 47–56 (1997) CrossRefGoogle Scholar
  34. Yamashita, D.S., Armentano, V.A., Laguna, M.: Scatter search for project scheduling with resource availability cost. Eur. J. Oper. Res. 169, 623–637 (2004) CrossRefMathSciNetGoogle Scholar
  35. Yano, C.A., Bolat, A.: Survey, developement, and application of algorithms for sequencing paced assembly lines. J. Manuf. Oper. Manag. 2, 172–198 (1989) Google Scholar
  36. Yano, C.A., Rachamadugu, R.: Sequencing to minimize work overload in assembly lines with product options. Manag. Sci. 37, 572–586 (1991) CrossRefGoogle Scholar
  37. Zeramdini, W., Aigbedo, H., Monden, Y.: Bicriteria sequencing for just-in-time mixed-model assembly lines. Int. J. Prod. Res. 38, 3451–3470 (2000) MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Jaime Cano-Belmán
    • 1
  • Roger Z. Ríos-Mercado
    • 2
  • Joaquín Bautista
    • 3
  1. 1.Graduate Program in Systems EngineeringUniversidad Autónoma de Nuevo LeónSan Nicolas de los GarzaMexico
  2. 2.Universidad Autónoma de Nuevo LeónSan Nicolas de los GarzaMexico
  3. 3.UPC Nissan ChairUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations