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OPTIMAL PRICING AND PRODUCTION POLICIES OF A MAKE-TO-STOCK SYSTEM WITH FLUCTUATING DEMAND

  • Jean-Philippe Gayon (a1), Işılay Talay-Değirmenci (a2), Fikri Karaesmen (a3) and E. Lerzan Örmeci (a3)

Abstract

We study the effects of different pricing strategies available to a production–inventory system with capacitated supply, which operates in a fluctuating demand environment. The demand depends on the environment and on the offered price. For such systems, three plausible pricing strategies are investigated: static pricing, for which only one price is used at all times, environment-dependent pricing, for which price changes with the environment, and dynamic pricing, for which price depends on both the current environment and the stock level. The objective is to find an optimal replenishment and pricing policy under each of these strategies. This article presents some structural properties of optimal replenishment policies and a numerical study that compares the performances of these three pricing strategies.

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1.Beyer, D. & Sethi, S.P. (1997). Average cost optimality in inventory models with markovian demands. Journal of Optimization Theory Applications 92: 497526.
2.Bitran, G. & Caldentey, R. (2003). An overview of pricing models for revenue management. Manufacturing and Service Operations Management 5(3): 203229.
3.Buzacott, J.A. & Shanthikumar, J.G. (1993). Stochastic models of manufacturing systems. Englewood Cliffs, NJ: Prentice Hall.
4.Caldentey, R. & Wein, L.M. (2006). Revenue management of a make-to-stock queue. Operations Research 54(5): 859875.
5.Chan, L.M.A., Shen, Z.J.M., Simchi-Levi, D. & Swann, J. (2004). Coordination of pricing and inventory decisions: A survey and classification. In Simchi-Levi, D., Wu, S.D. & Shen, Z.J.M., (eds.) Handbook of quantitative supply chain analysis: Modeling in the e-business era. Amsterdam: Kluwer, pp. 335392.
6.Chan, L.M.A., Simchi-Levi, D. & Swann, J. (2006). Dynamic pricing strategies for manufacturing with stochastic demand and discretionary sales. Manufacturing and Service Operations Management 8: 149168.
7.Chen, H., Wu, O. & Yao, D.D. (2004). Optimal pricing and replenishment in a single-product inventory system. Working paper, Cheung Kong Graduate School of Business, University of British Columbia and Columbia University.
8.Chen, X. & Simchi-Levi, D. (2002). Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The continuous review model. Working paper, Operations Research Center, MIT, Cambridge, MA.
9.Chen, X. & Simchi-Levi, D. (2004). Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The finite horizon case. Operations Research 52: 887896.
10.Chen, X. & Simchi-Levi, D. (2004). Coodinating inventory control and pricing strategies with random demand and fixed ordering cost: The infinite horizon case. Mathematics of Operations Research 29(3): 698723.
11.Elmaghraby, W. & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices and future directions. Management Science 49(10): 12871309.
12.Federgruen, A. & Heching, A. (1999). Combined pricing and inventory control under uncertainty. Operations Research 47(3): 454475.
13.Feldman, R.M. (1978). A continuous review (s, S) inventory system in a random environment. Journal of Applied Probability 15: 654659.
14.Feng, Y. & Chen, F.Y. (2003). Joint pricing and inventory control with setup costs and demand uncertainty. Working paper, The Chinese University of Hong Kong.
15.Feng, Y. & Chen, F. (2004). Optimality and optimization of a joint pricing and inventory-control policy for a periodic-review system. Working paper, The Chinese University of Hong Kong.
16.Gallego, G. & van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science 40(8): 9991020.
17.Kalpakam, S. & Arivarignan, G. (1989). A lost sales system in a random environment. Stochastic Analysis and Applications 7: 367385.
18.Koole, G. (1998). Structural results for the control of queueing systems using event-based dynamic programming. Queueing Systems 30: 323339.
19.Li, L. (1988). A stochastic theory of the firm. Mathematics of Operations Research 13(3): 447466.
20.McGill, J.I. & Van Ryzin, G.J. (1999). Revenue management: Research overview and prospects. Transportation Science 33(2): 233256.
21.Özekici, S. & Parlar, M. (1999). Inventory models with unreliable suppliers in a random environment. Annals of Operations Research 91: 123136.
22.Porteus, E.L. (2002). Foundations of stochastic inventory theory. Stanford, CA: Stanford University Press.
23.Puterman, M.L. (1994). Markov decision processes. New York: John Wiley and Sons.
24.Serfozo, R.F. (1979). An equivalence between continuous and discrete time markov decision processes. Operations Research 27(3): 616620.
25.Sethi, S.P. & Cheng, F. (1997). Optimality of (s, s) policies in inventory models with markovian demand. Operations Research 45: 931939.
26.Song, J.S. & Zipkin, P.H. (1993). Inventory control in a fluctuating demand environment. Operations Research 41: 351370.
27.Thomas, L.J. (1974). Price and production decisions with random demand. Operations Research 22: 513518.
28.Thowsen, G.T. (1975). A dynamic, nonstationary inventory problem for a Price/Quantity setting firm. Naval Research Logistics 22: 461476.
29.Yano, C.A. & Gilbert, S.M. (2002). Coordinated pricing and production/procurement decisions: A review. In Chakravarty, A. & Eliashbert, J. (eds.) Managing business interfaces: Marketing, engineering and manufacturing perspectives. Amsterdam: Kluwer Academic.
30.Zabel, E. (1970). Monopoly and uncertainty. The Review of Economic Studies 37: 205219.
31.Zabel, E. (1972). Multi-period monopoly under uncertainty. Economic Theory 5: 524536.
32Zipkin, P.H. (2000). Foundations of inventory management. New York: McGraw-Hill.

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OPTIMAL PRICING AND PRODUCTION POLICIES OF A MAKE-TO-STOCK SYSTEM WITH FLUCTUATING DEMAND

  • Jean-Philippe Gayon (a1), Işılay Talay-Değirmenci (a2), Fikri Karaesmen (a3) and E. Lerzan Örmeci (a3)

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