Abstract
This paper describes the characteristics of two hybrid genetic algorithms (GAs) for generating allocation and sequencing of production lots in a flow-shop environment based on a non-linear, multi-criteria objective function. Both GAs are used as search techniques: in the first model the task of the GA is to allocate and sequence the jobs; in the second model, the GA is combined with a dispatching rule (Earliest Due Date, EDD) thus limiting its task only on the allocation of the jobs. Both GAs are characterized by a dynamic population size with dynamic birth rate, as well as by multiple-operator reproduction criteria and by adaptive crossover and mutation rates. A discrete-event simulation model has been used in order to evaluate the performances of the tentative schedules. The proposed algorithms have been subsequently compared with a classical branch and bound method.
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References
Cavalieri, S. and Taisch, M. (1996) Neural networks in hybrid intelligent manufacturing systems, in Proceedings of the sixth IFIP International Conference on Advances in Production Management Systems, Okino, N. et al. (eds), 4-6 November, Kyoto, The International Federation of Information Processing, pp. 591–596.
Dagli, C. H. and Lee, H. C. (1995) A parallel genetic neuro scheduler for job-shop scheduling, in Proceedings of Thirtee nth International Conference on Production Research, Dar EL, E. M. et al. (eds), 6-10 August, Jerusalem, Freund Pub lishing House, London, pp. 602–604.
Dagli, C. H. and Sittisathanchai, S. (1993) Genetic neuro scheduler for job-shop scheduling. Computers and Industrial Engineering, 25, 267–270.
De Jong, K. A. (1975) Analysis of the behaviour of a class of genetic adaptive systems. PhD Dissertation, University of Michigan.
Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, pp. 412.
Jones, A. T. and Rabelo, L. (1993) Real decision making using neural nets, simulation and genetic algorithms. International Journal of Flexible Automation and Integrated Manufacturing, 1, 119–131.
Khuri, A. I. and Cornell, J. A. (1987) Response Surfaces: Design and Analyses, Marcel Dekker, New York.
Michalewicz, Z. (1994) GA + Data Structures = Evolution Programs, Springer-Verlag, Berlin.
Nakano, R. and Yamada, T. (1991) Conventional genetic algorithm for job-shop problem, in Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, Morgan Kaufmann, San Mateo, CA.
Reeves, C. R. (1995) A genetic algorithm for flow-shop sequencing. Computers Operations Research, 22, 5–13.
Sangalli, N., Semeraro, Q. and Tolio, T. (1995) Performance of a GA in the solution of permutation flow-shop problem, in Proceedings of the International Conference on Neural Net works and GAs, Ales, France.
Suresh, G., Vinod, V. V. and Sahu, S. (1996) A genetic algorithm for assembly line balancing. Production, Planning and Control, 7, 38–46.
Syswerda, G. (1991) Schedule optimization using genetic algo rithms, in Handbook of Genetic Algorithms, Davis, L. (ed.), VNR Computer Library, New York, pp. 332–349.
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Cavalieri, S., Gaiardelli, P. Hybrid genetic algorithmsfor a multiple-objective scheduling problem. Journal of Intelligent Manufacturing 9, 361–367 (1998). https://doi.org/10.1023/A:1008935027685
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DOI: https://doi.org/10.1023/A:1008935027685