Abstract
Although Master Production Scheduling (MPS) has been studied and used by both academia and industries for quite a long time, the real complexity involved in making a master plan when capacity is limited, when products have the flexibility of being made at different production lines, and when performance goals are tight and conflicting, has not yet been presented in the literature in a simple and practical way. In this context, one should consider how to attain a given performance by balancing different objectives, such as maximizing service level, and minimizing inventory levels, risk of stockouts, overtime, and setup time. Many decisions need to be made during the development of an MPS, such as: Which product should be scheduled, in what quantity, and to which resource? Is overtime needed? Should inventory be built for future periods? Should backlogging be considered? Clearly, an MPS process depends on the combination of many different parameters. For this type of problem, it is extremely difficult to find a solution that satisfies all objectives involved simultaneously, mainly because of the great number of variables involved. It is known that finding an optimal MPS solution for industrial scheduling scenarios is time consuming - despite nowadays computers being extremely fast. It is common, therefore, to use heuristics (or meta-heuristics) to find good plans in reasonable computer time. Using a plain language, this chapter describes some of the complexity involved in the MPS creation without, however, paying too much attention to mathematical formalisms and definitions, using mostly the author’s industry experience and practical examples faced during research in the production scheduling area.
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References
Bonomi, E.; & Lutton, J. “The N-City Traveling Salesman Problem: Statistical Mechanics and the Metropolis Algorithm.” SIAM Review, 26, 551–568, 1984.
Brochonski, P. C. “Um Sistema para Programação da Produção de Máquinas de Composição SMT”, Curitiba. 1999.
Cavalcanti, E. M. B.; & Moraes, W. F. A. de. Programa-mestre de produção: concepção teórica X aplicação prática na indústria de cervejas e refrigerantes. Anais do ANPAD. Foz do Iguaçu — PR, 1998.
Connolly, D. T., “An Improved Annealing Scheme for the Quadratic Assignment Problem.” European Journal of Operation Research, 46, 93–100, 1990.
Cox III, J. F.; & Blackstone Jr, J. H. “APICS Dictionary” — Ninth Edition. 1998.
Fernandes, C. A. O.; Carvalho, M. F. H.; & Ferreira, P. A. V., “Planejamento Multiobjetivo da Produção da Manufatura através de Programação Alvo” 13th Automatic Brazilian Congress, Florianópolis, Brazil, 2000.
Gaither, N.; & Frazier, G. Production and Operations Management. 8th Edition. Southwestern Educational Publishing. 1999.
Garey, M.; & Johnson, D. “Computers, Complexity and Intractability. A Guide to Theory of NP-Completeness.” Freeman, San Francisco, USA, 1979.
Higgins, P.; & Browne, J. “Master production scheduling: a concurrent planning approach.” Production Planning & Control. Vol.3, No.l, 2–18. 1992.
Kirkpatrick, S., Gelatt, C. D. Jr.; & Vecchi, M. P. “Optimization by Simulated Annealing.” Science, Vol.220, No.4598, 671–680, 1983.
McClelland, M. K., “Order Promising and the Master Production Schedule.” Decision Science, 19, 4. Fall 1988.
McLaughlin, M. P. “Simulated Annealing.” Dr. Dobb’s Journal, 26–37, 1989.
Metropolis, N.; Rosenbluth, A.; Rosenbluth, M.; Teller, A.; & Teller, E. “Equations of State Calculations by Fast Computing Machines.” J. Chemical Physics, vol. 21, 1087–1091, 1953.
Moccellin, J. V., dos Santos, M. O., & Nagano, N. S., “Urn Método Heurístico Busca Tabu — Simulated Annealing para Flowshops Permutacionais” 23rd Annual Symposium of the Brazilian Operational Research Society, Campos do Jordão, Brazil, 2001.
Prado, D. Programação Linear — Série Pesquisa Operacional — 1 Volume. 2a. Edição, Editora DG. 1999.
Radhakrishnan, S. & Ventura, J. A. “Simulated Annealing for Parallel Machine Scheduling with Earliness-Tardiness penalties and Sequence-Dependent Setup Times.” International Journal of Production Research”, vol.38, no 10, 2233–2252, 2000.
Slack, N.; Chambers, S.; & Johnston, R. Operations Management. Prentice Hall — 3rd Edition-2001.
Tanomaro, J. “Fundamentos e Aplicações de Algoritmos Genéticos.” Segundo Congresso Brasileiro de Redes Neurais.
Tsang, E. P. K. “Scheduling Techiques — A Comparative Study.” BT Technol J Vol-13 N. 1 Jan 1995.
Vieira, G. E.; Soares, M. M.; & Gaspar Jr, O. “Otimização do planejamento mestre da produção através de algoritmos genético. In: XXII Encontro Nacional de Engenharia de Produção, 2002, Curitiba. Anais de Resumos ENEGEP 2002. São Paulo: ABEPRO, 2002.
Vollmann, T. E., Berry, W. L., & Whybark, D. C. Manufacturing Planning and Control Systems. Irwin — Third Edition. 1992.
Wall, M. B. “A Genetic Algorithm for Resource-Constrained Scheduling.” Department of Mechanical Engineering-Massachusetts Institute of Technology. 1996.
Zolfaghari, S.; & Liang, M. “Comparative Study of Simulated Annealing, Genetic Algorithms and Tabu Search of Solving Binary and Comprehensive Machine-Grouping Problems.” International Journal of Production Research, vol.40, no 9, 2141–2158, 2002.
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Vieira, G.E. (2006). A Practical View of the Complexity in Developing Master Production Schedules: Fundamentals, Examples, and Implementation. In: Herrmann, J.W. (eds) Handbook of Production Scheduling. International Series in Operations Research & Management Science, vol 89. Springer, Boston, MA. https://doi.org/10.1007/0-387-33117-4_7
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DOI: https://doi.org/10.1007/0-387-33117-4_7
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