Production
https://prod.org.br/article/doi/10.1590/S0103-65132010005000001
Production
Article

Abordagem adaptativa aplicada ao planejamento agregado da produção sob incertezas

Adaptive approach applied to aggregate production planning under uncertainties

Silva Filho, Oscar Salviano; Cezarino, Wagner

Downloads: 0
Views: 515

Resumo

Um problema de planejamento agregado da produção, com incertezas sobre a flutuação da demanda, é formulado através de um modelo de otimização estocástica, com critério quadrático e restrições lineares. Dificuldades para encontrar uma solução ótima global para o problema levam à proposição de uma abordagem adaptativa de fácil implementação computacional que é baseada na formulação de um problema determinístico equivalente e que tem sua solução periodicamente revisada por meio de um procedimento clássico da literatura. Um exemplo, em que o sistema de balanço de estoque está sujeito à forte e fraca variabilidade da demanda real, é empregado para analisar o comportamento da abordagem proposta. Por fim, os resultados obtidos são comparados com outra abordagem subótima, cuja principal característica é não permitir revisões periódicas.

Palavras-chave

Controle de estoques. Planejamento. Otimização. Processos estocásticos. Simulação.

Abstract

A problem of aggregate production planning, with uncertainty about the fluctuation in demand, is formulated by a stochastic optimization model, with quadratic criterion and linear constraints. Difficulties in finding a global optimum solution to the problem lead to the proposal of an adaptive approach which is easy to implement computationally and which is based on the formulation of a deterministic equivalent problem, the solution for which is periodically reviewed through a classical procedure of literature. An example, where the inventory balance system is subject to weak and strong variability in actual demand, is employed to analyze the behavior of the proposed approach. Finally, the results provided by the proposed approach are compared with another suboptimal approach, the main characteristic of which is not allowing periodic reviews.

Keywords

Inventory control. Planning. Optimization. Stochastic process. Simulation.

References



BERTESEKAS, D. P. Dynamic programming and optimal control. Belmont: Athena Scientific, 2000. v. 1.

BERTESEKAS, D. P. Dynamic programming and stochastic control, mathematics in science and engineering. New York: Academic Press, 1976. v. 125.

BITRAN, G. R.; YANASSE, H. Deterministic approximation to stochastic production problem. Operations Research, v. 32, n. 5, p. 999-1018, 1984.

CHENG, L.; SUBRAHMANIAN, E.; WESTERBERG, A. W. A comparison of optimal and stochastic programming from a formulation and computation perspective. Computers and Chemical Engineering, v. 29, n. 15, p. 149-164, 2004.

FEIRING, B. R.; SASTRI, T. Improving production planning by utilizing stochastic programming. Computers Industrial Engineering, v. 19, n. 14, p. 53-56, 1990.

GARDNERA, W. A.; NAPOLITANO, A.; PAURAC, L. Cyclostationarity: half a century of research. Signal Processing, v. 86, n. 4, p. 639-697, 2006.

GRAMA, A. et al. Introduction to parallel computing. 2 ed. London: Addison Wesley, 2003.

GRAVES, S. C. A. Single-item inventory model for a non-stationary demand process. Manufacturing and service operations management, v. 1, n. 1, p. 50-61, 1999.

HACKMAN, S. et al. A stochastic production planning model. Georgia: Technical Report, 2002.

HAX, A. C.; CANDEA, D. Production and inventory management. New Jersey: Prentice-Hall, 1984.

HOLT, C. C. et al. Planning production inventory and workforce. New Jersey: Prentice-Hall, 1960.

KLEINDORFER, P. R. Stochastic control models in management science: theory and computation. Wiley: Applied Optimal Control, 1978. p. 69-88. (TIMS Studies in the Management Science, v. 9).

KLEINDORFER, P. R. et al. Discrete optimal control of production plans. Management Science, v. 22, n. 3, p. 261-273, 1975.

LEVI, R. et al. Approximation algorithms for stochastic inventory control models. Manufacturing and Service Operations Management, v. 7, n. 1, p. 81-99, 2005.

ORTEGA, M.; LIN, L. Control theory applications to the production-inventory problem: a review. International Journal of Production Research, v. 42, n. 11, p. 2303-2322, 2004.

ÖZDAMAR, L.; BOZYEL, M. A.; BIRBIL, S. I. A. Hierarchical decision support system for production planning. European Journal of Operational Research, v. 104, n. 3, p. 403-422, 1998

PAPOULIS A. Probability, random variables, and stochastic processes. 3 ed. Singapore: McGraw-Hill, 1991.

PEKELMAN, D.; RAUSSER, G. C. Adaptive control: survey of methods and applications. Wiley: Applied Optimal Control, 1978. p. 89-120. (TIMS Studies in the Management Science, v. 9).

PEREIRA, F. L.; SOUSA, J. B. On the receding horizon hierarchical optimal control of manufacturing systems. Journal of Intelligent Manufacturing, v. 8, n. 5, p. 425-433, 1997.

POWELL, W. B. Approximate dynamic programming: solving the course of dimensionality. New Jersey: John Wiley and Sons, 2007.

RANTALA J.; KOIVISTO, H. Production planning under stochastic time-varying demand: flexible service approach. In: IFAC WORLD CONGRESS, 2005. Proceedings... Praga: Elsevier Science, 2006.

SHEN, R. F. C. Aggregate production planning by stochastic control. European Journal of Operations Research, v. 73, n. 2, p. 346-359, 1994.

SILVA FILHO, O. S.; CEZARINO, W. Geração de planos de produção via otimização seqüencial subótima. Gestão e Produção, v. 14, n. 2, p. 239-252, 2007.

YILDIRIM, I.; BARIS, T.; KARAESMEN, F. A. Multi-period stochastic production planning and sourcing problem with service level constraints. OR Spectrum, v. 27, n. 2-3, p. 471-489, 2005.

5883a3d57f8c9da00c8b4674 1574685864 Articles
Links & Downloads

Production

Share this page
Page Sections