Abstract
Process planning and scheduling are actually interrelated and should be solved simultaneously. Most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. The initial part of this paper describes a genetic algorithm (GA) based algorithm that only considers the time aspect of the alternative machines. The scope of consideration is then further extended to include the processing capabilities of alternative machines, with different tolerance limits and processing costs. In the proposed method based on GAs, the processing capabilities of the machines, including processing costs as well as number of rejects produced in alternative machine are considered simultaneously with the scheduling of jobs. The formulation is based on multi-objective weighted-sums optimization, which are to minimize makespan, to minimize total rejects produced and to minimize the total cost of production. A comparison is done w ith the traditional sequential method and the multi-objective genetic algorithm (MOGA) approach, based on the Pareto optimal concept.
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References
Baker, J. E. (1985) Adaptive selection methods of genetic algorithms, in Proceedings of the First International Conference on Genetic Algorithms and their Applications, Grefenstette, J.J. (ed), Pittsburgh, PA, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 101–111.
Brandimarte, P. and Calderini, M. (1995) A hierarchical bicriterion approach to integrated process plan selection and job shop scheduling. International Journal of Production Research, 33(1), 161–181.
Bruns, R. (1993) Direct chromosome representation and advanced genetic operators for production scheduling, in Proceedings of the Fifth International Conference on Genetic Algorithms, Forrest, S. (ed), Morgan Kaufmann, San Mateo, California, pp. 352–359.
Fonseca, C. M. and Fleming, P. J. (1995) An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, Vol. 3, 1–16.
Garey, M. R. and Johnson, D. S. (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman New York.
Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Massachusetts.
Grefenstette, J. J. (1987) Incorporating problem specific knowledge into genetic algorithms, in Genetic Algorithms and Simulated Annealing, Davis, L. (ed), Morgan Kaufmann Publishers, Los Altos, CA, pp. 42–60.
Gülesin, M. (1996) Finished part modelling in a computer-aided process-planning system. Journal of Intelligent Manufacturing, 7, 251–256.
Husbands, P. (1993) An ecosystems model for integrated production planning. Journal of Computer Integrated Manufacturing, 6, 74–86.
Husbands, P. and Mill, F. (1991) Simulated co-evolution as the mechanism for emergent planning and scheduling, in Proceeding of the Fourth International Conference on Genetic Algorithms, Belew, R.K. and Booker, L.B. (eds), Morgan Kaufmann Publishers, San Mateo, California, pp. 264–269.
Khoshnevis, B. and Chen, Q. (1990) Integration of process planning and scheduling functions. Journal of Intelligent Manufacturing, 1, 165–176.
Michalewicz, Z. (1994) Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, Berlin.
Morad, N. (1997) Optimisation of cellular manufacturing systems using genetic algorithms, PhD thesis, University of Sheffield.
Murata, T. and Ishibuchi, H. (1996) Multi-objective genetic algorithm and its application to flowshop scheduling. Computer and industrial Engineering, 30, 957–968.
Muth, J. and Thompson, G. (1963) Industrial Scheduling, Prentice-Hall, Englewood Cliffs.
Palmer, G. J. (1996) A simulated annealing approach to integrated production scheduling. Journal of Intelligent Manufacturing, 7(3), 163–176.
Pennell, J. P. and Winner, R. I. (1989) Concurrent engineering: practices and prospects, in Proceedings of the IEEE Global Telecommunications Conference & Exhibition (GLOBCO), Part I, Published by IEEE Service Center, Piscataway, New Jersey, pp. 647–655.
Singh, N. and Sushil, S. (1990) A physical system theory framework for modelling manufacturing systems. International Journal of Production Research, Vol. 28, No. 6, pp. 1067–1082.
Singh, N. (1996) Systems Approach to Computer-Integrated Design and Manufacturing, John Wiley, New York.
Sundaram, R. M. and Fu, S. S. (1988) Process planning and scheduling. Computers and Industrial Engineering, 15(1–4), 296–307.
Syswerda, G. (1991) Schedule optimization using genetic algorithms, in Handbook of Genetic Algorithms, Davis, L. (ed.), Van Nostrand Reinhold, New York, pp. 332–349.
Uckun, S., Bagchi, S. and Kawamura, K. (1993) Managing genetic search in job shop scheduling. IEEE Expert, Vol. 10, pp. 15–24.
Warnecke, H. J. and Muthsam, H. (1992) Knowledge-based systems for process planning, in Intelligent Design and Manufacturing, Kusiak, A. (ed.), Wiley, New York, pp. 377–396.
Zentner, M. G. Pekny, J. F., Reklaitis, G. V. and Gupta, J. N. D. (1994) Practical considerations in using model-based optimisation for the scheduling and planning of batch/semicontinuous process. Journal of Process Control, 4(4), 259–280.
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MORAD, N., ZALZALA, A. Genetic algorithms in integrated process planning and scheduling. Journal of Intelligent Manufacturing 10, 169–179 (1999). https://doi.org/10.1023/A:1008976720878
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DOI: https://doi.org/10.1023/A:1008976720878