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Limited Resequencing for Mixed Models with Multiple Objectives

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DOI: 10.4236/ajor.2011.14025    5,151 Downloads   7,704 Views   Citations

ABSTRACT

This research presents a problem relevant to production scheduling for mixed models – production schedules that contain several unique items, but each unique item may have multiple units that require processing. The presented research details a variant of this problem where, over multiple processes, resequencing is permitted to a small degree so as to exploit efficiencies with the intent of optimizing the objectives of required set-ups and parts usage rate via an efficient frontier. The problem is combinatorial in nature. Enumeration is used on a variety of test problems from the literature, and a search heuristic is used to compare optimal solutions with heuristic based solutions. Experimentation shows that the heuristic solutions approach optimality, but with opportunities for improvement.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

P. McMullen, "Limited Resequencing for Mixed Models with Multiple Objectives," American Journal of Operations Research, Vol. 1 No. 4, 2011, pp. 220-228. doi: 10.4236/ajor.2011.14025.

References

[1] J. Miltenburg, “Level Schedules for Mixed-Model Assembly Lines in Just in Time Production Systems,” Management Science, Vol. 35, No. 2, 1989, pp. 192-207. doi:10.1287/mnsc.35.2.192
[2] P. R. McMullen, “JIT Sequencing for Mixed-Model Assembly Lines Using Tabu Search,” Production Planning and Control, Vol. 9, No. 5, 1998, pp. 504-510. doi:10.1080/095372898233984
[3] P. R. McMullen and G. V. Frazier, “A Simulated Annealing Approach to Mixed-Model Sequencing with Multiple Objectives on a Just in Time Line,” IIE Transactions, Vol. 32, No. 8, 2000, pp. 679-686. doi:10.1080/07408170008967426
[4] A. Joly and Y. Frein, “Heuristics for an Industrial Car Sequencing Problem Considering Paint and Assembly Shope Objectives,” Computers & Industrial Engineering, Vol. 55, No. 2, 2008, pp. 295-310. doi:10.1016/j.cie.2007.12.014
[5] M. Masin and Y. Bukchin, “Diversity Maximization Approach for Multiobjective Optimization,” Operations Research, Vol. 56, No. 2, 2008, pp. 411-424. doi:10.1287/opre.1070.0413
[6] M. Lahmar and S. Banjaafar, “Sequencing with Limited Flexibility,” IIE Transactions, Vol. 39, No. 10, 2007, pp. 937-955. doi:10.1080/07408170701416665
[7] P. R. McMullen, “A Kohonen Self-Organizing Map Approach to Addressing a Multiple Objective, Mixed Model JIT Sequencing Problem,” International Journal of Production Economics, Vol. 72, No. 1, 2001, pp. 59-71. doi:10.1016/S0925-5273(00)00091-8
[8] LINGO, “The Modeling Language and Optimizer,” LINDO Systems, Inc., Chicago, 1995.
[9] S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, Vol. 220, No. 4598, 1983, pp. 671-679. doi:10.1126/science.220.4598.671
[10] R. W. Eglese, “Simulated Annealing: A Tool for Operational Research,” European Journal of Operational Research, Vol. 46, No. 3, 1990, pp. 271-281. doi:10.1016/0377-2217(90)90001-R
[11] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, 1975.
[12] F. Glover, “Tabu Search: A Tutorial,” Interfaces, Vol. 20, No. 1, 1990, pp. 74-94. doi:10.1287/inte.20.4.74
[13] M. Dorigo and L. M. Gambardella, “Ant Colonies for the Traveling Salesman Problem,” Biosystem, Vol. 43, No. 1, 1997, pp. 73-81. doi:10.1016/S0303-2647(97)01708-5
[14] N. Metropolis, A. Rosenbluth, N. Rosenbluth, A. Teller and E. Teller, “Equation of State Calculations by Fast Computing Machines,” Journal of Chemical Physics, Vol. 51, No. 6, 1953, pp. 177-190.
[15] P. R. McMullen, “An Ant Colony Optimization Approach to Addressing a JIT Sequencing Problem with Multiple Objective,” Artificial Intelligence in Engineering, Vol. 15, No. 3, 2001, pp. 309-317. doi:10.1016/S0954-1810(01)00004-8
[16] R. Sedgewick, “Algorithms in Java,” 3rd Edition, Addison-Wesley, New York.

  
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