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An application of genetic algorithms to lot-streaming flow shop scheduling

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IIE Transactions

Abstract

A Hybrid Genetic Algorithm (HGA) approach is proposed for a lot-streaming flow shop scheduling problem, in which a job (lot) is split into a number of smaller sublots so that successive operations can be overlapped. The objective is the minimization of the mean weighted absolute deviation of job completion times from due dates. This performance criterion has been shown to be non-regular and requires a search among schedules with intermittent idle times to find an optimal solution. For a given job sequence, a Linear Programming (LP) formulation is presented to obtain optimal sublot completion times. Objective function values of LP solutions are used to guide the HGA's search toward the best sequence. The performance of the HGA approach is compared with that of a pairwise interchange method.

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References

  • Baker, K.R. and Jia, D. (1993) A comparative study of lot streaming procedures. Omega-International Journal of Management Science, 21, 561–566.

    Google Scholar 

  • Baker, K.R. and Pyke, D.F. (1990) Solution procedures for the lotstreaming problem. Decision Sciences, 21, 475–491.

    Google Scholar 

  • Bean, J.C. (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6, 154–160.

    Google Scholar 

  • Beasley, D., Bull, D.R. and Martin, R.R. (1993a) An overview of genetic algorithms: part 1, fundamentals. University Computing, 15, 58–69.

    Google Scholar 

  • Beasley, D., Bull, D.R. and Martin, R.R. (1993b) An overview of genetic algorithms: part 2, research topics. University Computing, 15, 170–181.

    Google Scholar 

  • Bhattacharyya, S. (1999) Direct marketing performance modeling using genetic algorithms. INFORMSJournal on Computing, 11, 248–257.

    Google Scholar 

  • Chen, C.-L., Vempati, V.S. and Aljaber, N. (1995) An application of genetic algorithms for flow shop problems. European Journal of Operational Research, 80, 389–396.

    Google Scholar 

  • Chen, J. and Steiner, G. (1998) Lot streaming with attached setups in three-machine flow shops. IIE Transactions, 30, 1075–1084.

    Google Scholar 

  • Chen, J. and Steiner, G. (1999) Discrete lot streaming in two-machine flow shops. INFOR, 37, 160–173.

    Google Scholar 

  • Cleveland, G.A. and Smith, S.F. (1989) Using genetic algorithms to schedule flow shop releases, in Proceedings of the Third International Conference on Genetic Algorithms and their Applications, ed.J.D. Schaffer, Morgan Kaufmann Publishers, San Mateo, CA, pp. 160–169.

    Google Scholar 

  • Davis, L. (ed) (1991) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, NY.

    Google Scholar 

  • Davis, L. and Steenstrup, M. (1987) Genetic algorithms and simulated annealing: an overview, in Genetic Algorithms and Simulated Annealing, Davis, L. (ed), Morgan Kaufmann Publishers, Los Altos, CA, pp. 1–11.

    Google Scholar 

  • Etzel, M. and Dickinson, K. (1999) Digital Visual Fortran Programmer's Guide, Digital Press, Boston, MA.

    Google Scholar 

  • Gazen, C. and Ersoy, C. (1999) Genetic algorithms for designing multihop lightwave network topologies. Artificial Intelligence in Engineering, 13, 211–221.

    Google Scholar 

  • Glass, C.A., Gupta, J.N.D. and Potts, C.N. (1994) Lot streaming in three-stage production processes. European Journal of Operational Research, 75, 378–394.

    Google Scholar 

  • Glass, C.A. and Potts, C.N. (1998) Structural properties of lot streaming in a flow shop. Mathematics of Operations Research, 23, 624–639.

    Google Scholar 

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

    Google Scholar 

  • Goldberg, D.E. and Lingle, R. (1985) Alleles, loci, and the traveling salesman problem, in Proceedings of the First International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates Publishers, Hillsdale, NJ, pp. 154–159.

    Google Scholar 

  • Goldberg, D.E. and Segrest, P. (1987) Finite Markov chain analysis of genetic algorithms, in Proceedings of the Second International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates Publishers, Hillsdale, NJ, pp. 1–8.

    Google Scholar 

  • Graves, S.C. and Kostreva, M.M. (1986) Overlapping operations in material requirements planning. Journal of Operations Management, 6, 283–294.

    Google Scholar 

  • Gupta, M.C., Gupta, Y.P. and Kumar, A. (1993) Minimizing flow time variance in a single machine system using genetic algorithms.European Journal of Operational Research, 70, 289–303.

    Google Scholar 

  • Hermann, J.W. and Lee, C.-Y. (1995) Solving a class scheduling problem with a genetic algorithm. ORSA Journal on Computing, 7, 443–452.

    Google Scholar 

  • Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA.

    Google Scholar 

  • Kropp, D.H. and Smunt, T.L. (1990) Optimal and heuristic models for lot splitting in a flow shop. Decision Sciences, 21, 691–709.

    Google Scholar 

  • Lee, C.-Y., Piramuthu, S. and Tsai, Y.-K. (1997) Job shop scheduling with a genetic algorithm and machine learning. International Journal of Production Research, 35, 1171–1191.

    Google Scholar 

  • Liepins, G.E. and Hilliard, M.R. (1989) Genetic algorithms: foundation and applications. Annals of Operations Research, 21, 31–58.

    Google Scholar 

  • Montgomery, D.C. (1991) Design and Analysis of Experiments, (3rd edn), Wiley, New York, NY.

    Google Scholar 

  • Nagar, A. (1996) A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem. Annals of Operations Research, 63, 397–414.

    Google Scholar 

  • Nordström, A.-L. and Tufekci, S. (1994) A genetic algorithm for the talent scheduling problem. Computers and Operations Research, 21, 927–940.

    Google Scholar 

  • Potts, C.N. and Baker, K.R. (1989) Flow shop scheduling with lot streaming. Operations Research Letters, 8, 297–303.

    Google Scholar 

  • Potts, C.N. and Van Wassenhove, L.N. (1992) Integrating scheduling with batching and lot-sizing: a review of algorithms and complexity.Journal of the Operational Research Society, 43, 395–406.

    Google Scholar 

  • Rayward-Smith, V.J., Osman, I.H., Reeves, C.R. and Smith, G.D. (eds) (1996) Modern Heuristic Search Methods, John Wiley and Sons, New York, NY.

    Google Scholar 

  • Reeves, C.R. (1995) A genetic algorithm for flowshop sequencing.Computers and Operations Research, 22, 5–13.

    Google Scholar 

  • Reiter, S. (1966) A system for managing job-shop production. The Journal of Business, 34, 371–393.

    Google Scholar 

  • Şen, A. and Benli, Ö.S. (1999) Lot streaming in open shops. Operations Research Letters, 23, 135–142.

    Google Scholar 

  • Şen, A., Topaloğlu, E. and Benli, Ö.S. (1998) Optimal streaming of a single job in a two-stage flow shop. European Journal of Operational Research, 110, 42–62.

    Google Scholar 

  • Srinivas, M. and Patnaik, L.M. (1994) Genetic algorithms: a survey.Computer, 27, 17–26.

    Google Scholar 

  • Sriskandarajah, C. and Wagneur, E. (1999) Lot streaming and scheduling multiple products in two-machine no-wait flowshops. IIE Transactions, 31, 695–707.

    Google Scholar 

  • Trietsch, D. and Baker, K.R. (1993) Basic techniques for lot streaming.Operations Research, 41, 1065–1076.

    Google Scholar 

  • Wagner, B.J. and Ragatz, G.L. (1994) The impact of lot splitting on due date performance. Journal of Operations Management, 12, 13–25.

    Google Scholar 

  • Yoon, S.-H. and Ventura, J.A. (2002) Minimizing the mean weighted absolute deviation from due dates in lot-streaming flow shop scheduling. Computers and Operations Research, 29 (10), 1301–1315.

    Google Scholar 

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, 356 Leonhard Building, University Park, PA, 16802, USA

    Suk-Hun Yoon & Jose A. Ventura

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  1. Suk-Hun Yoon
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  2. Jose A. Ventura
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Yoon, SH., Ventura, J.A. An application of genetic algorithms to lot-streaming flow shop scheduling. IIE Transactions 34, 779–787 (2002). https://doi.org/10.1023/A:1015596621196

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