Production Planning Under Supply and Demand Uncertainty: A Stochastic Programming Approach
In this chapter, we introduce a stochastic programming model for production planning under uncertainty. Our model of uncertainty extends to supply via uncertainties in the production process, and demand via probabilistic descriptors of quantities and due dates even after orders have been received. In contrast to much of the existing literature, our models of uncertainty are dynamic, in that they reflect the evolution of supply through a multistage production process as well as volatility in customer orders as due dates approach. The resulting model is a multistage stochastic linear program that incorporates Markov chains within the probabilistic models.
KeywordsProduction Facility Order Quantity Production Schedule Production Stage Markov Chain Model
This work was supported by Grant No. DMS 04-00085 from The National Science Foundation.
- Axsater, S.: Inventory Control, 2nd Ed. Springer, New York, NY (2006)Google Scholar
- Duenyas, I., Tsai, C.-Y.: Control of a manufacturing system with random product yield and downward substitutability. IIE Trans. 32, 785–795 (2000)Google Scholar
- Hadley, G., Whitin, T.M.: Anal Invent Syst Prentice-Hall, London (1963)Google Scholar
- Ignizio, J.P.: The implementation of conwip in semiconductor fabrication facilities. Future Fab. Intl. Feb. (2003)Google Scholar
- Kempf, K.G.: Control-oriented approaches to supply chain management in semiconductor manufacturing. In: Proceeding of the 2004 American Control Conference, June 30–July 2 (2004) Boston, MA (2004)Google Scholar
- Kouvelis, P., Milner, J.M.: Supply chain capacity and outsourcing decisions: the dynamic interplay of demand and supply uncertainty. IIE Trans. 34, 717–728 (2002)Google Scholar
- Ovacik, I.M., Uzsoy, R.: Decomposition Methods for Complex Factory Scheduling Problems. Springer, New York, NY (2006)Google Scholar
- Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer, New York, NY (2006)Google Scholar
- Porteus, E.L.: Foundations of Stochastic Inventory Theory. Stanford University Press, Stanford, CA (2002)Google Scholar
- Smith, S., Keng, N., Kempf, K.: Exploiting local flexibility during execution of pre-computed schedules. In: Famili, A., Nau, D.S., Tong, S.H., (eds.) Artificial Intelligence Applications in Manufacturing, pp. 277–292. MIT Press, Cambridge, MA (1992)Google Scholar
- Vollman, T.E., Barry, W.L., Whybark, D.C., Jacobs, F.R.: Manufacturing Planning and Control Systems for Supply Chain Management, 5th ed. McGraw-Hill, New York, NY (2004)Google Scholar
- Zipkin, P.H.: Foundations of Inventory Management. McGraw-Hill/Irwin, New York, NY (2000)Google Scholar