Abstract
One of the most important policies adopted in inventory control is the (R,S) policy (also known as the “replenishment cycle” policy). Under the non-stationary demand assumption the (R,S) policy takes the form (R n ,S n ) where R n denotes the length of the n th replenishment cycle, and S n the corresponding order-up-to-level. Such a policy provides an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a CP approach able to compute optimal (R n ,S n ) policy parameters under stochastic demand, ordering, holding and shortage costs. The convexity of the cost-function is exploited during the search to compute bounds. We use the optimal solutions to analyze the quality of the solutions provided by an approximate MIP approach that exploits a piecewise linear approximation for the cost function.
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Tarim, S.A., Hnich, B., Rossi, R., Prestwich, S.D.: Cost-Based Filtering for Stochastic Inventory Control. In: Azevedo, F., Barahona, P., Fages, F., Rossi, F. (eds.) CSCLP. LNCS (LNAI), vol. 4651, pp. 169–183. Springer, Heidelberg (2007)
Tarim, S.A., Smith, B.: Constraint Programming for Computing Non-Stationary (R,S) Inventory Policies. European Journal of Operational Research (to appear)
Tarim, S.A., Kingsman, B.G.: The Stochastic Dynamic Production/Inventory Lot-Sizing Problem With Service-Level Constraints. International Journal of Production Economics 88, 105–119 (2004)
Tarim, S.A., Kingsman, B.G.: Modelling and Computing (R n,S n) Policies for Inventory Systems with Non-Stationary Stochastic Demand. European Journal of Operational Research 174, 581–599 (2006)
Tarim, S.A.: Dynamic Lotsizing Models for Stochastic Demand in Single and Multi-Echelon Inventory Systems. PhD Thesis, Lancaster University (1996)
Bookbinder, J.H., Tan, J.Y.: Strategies for the Probabilistic Lot-Sizing Problem With Service-Level Constraints. Management Science 34, 1096–1108 (1988)
Wagner, H.M., Whitin, T.M.: Dynamic Version of the Economic Lot Size Model. Management Science 5, 89–96 (1958)
Silver, E.A., Pyke, D.F., Peterson, R.: Inventory Management and Production Planning and Scheduling. John Wiley and Sons, New York (1998)
Porteus, E.L.: Foundations of Stochastic Inventory Theory. Stanford University Press, Stanford (2002)
Apt, K.: Principles of Constraint Programming. Cambridge University Press, Cambridge (2003)
Charnes, A., Cooper, W.W.: Chance-Constrainted Programming. Management Science 6(1), 73–79 (1959)
Fortuin, L.: Five Popular Probability Density Functions: a Comparison in the Field of Stock-Control Models. Journal of the Operational Research Society 31(10), 937–942 (1980)
Lustig, I.J., Puget, J.-F.: Program Does Not Equal Program: Constraint Programming and its Relationship to Mathematical Programming. Interfaces 31, 29–53 (2001)
Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Springer, New York (1997)
Focacci, F., Lodi, A., Milano, M.: Cost-Based Domain Filtering. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 189–203. Springer, Heidelberg (1999)
Laburthe, F., OCRE project team: Choco: Implementing a CP Kernel. Bouygues e-Lab, France
Hadley, G., Whitin, T.M.: Analysis of Inventory Systems. Prentice-Hall, Englewood Cliffs (1964)
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Rossi, R., Tarim, S.A., Hnich, B., Prestwich, S. (2007). Replenishment Planning for Stochastic Inventory Systems with Shortage Cost. In: Van Hentenryck, P., Wolsey, L. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2007. Lecture Notes in Computer Science, vol 4510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72397-4_17
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DOI: https://doi.org/10.1007/978-3-540-72397-4_17
Publisher Name: Springer, Berlin, Heidelberg
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