Dynamic pricing for nonperishable products with demand learning

Operations Research

Volumn 57, Issue 5, 2009, Pages 1169-1188

  Araman, Victor F ^a,b   Caldentey, Reń ^a  

^a NEW YORK UNIVERSITY   (United States)

^b AMERICAN UNIVERSITY OF BEIRUT   (Lebanon)

Author keywords

Approximations; Bayesian demand learning; Dynamic pricing; Intensity control; Nonhomogeneous poisson process; Optimal stopping

Indexed keywords

APPROXIMATIONS; BAYESIAN DEMAND LEARNING; DYNAMIC PRICING; INTENSITY CONTROL; NONHOMOGENEOUS POISSON PROCESS; OPTIMAL STOPPING;

ASYMPTOTIC ANALYSIS; BAYESIAN NETWORKS; COSTS; OPTIMIZATION; POISSON DISTRIBUTION; POISSON EQUATION; PROFITABILITY; SALES;

PROCESS CONTROL;

EID: 70350228783     PISSN: 0030364X     EISSN: 15265463     Source Type: Journal    
DOI: 10.1287/opre.1090.0725     Document Type: Article

References (37)

1. Aviv, Y., A. Pazgal. 2002. Pricing of short life-cycle products through active learning. Working paper, Olin School of Business, Washington University, St. Louis.
2. Aviv, Y., A. Pazgal. 2005. A partially observed Markov decision process for dynamic pricing. Management Sci. 51(9) 1400-1416.
3. Azoury, K. S. 1985. Bayes solution to dynamic inventory models under unknown demand distribution. Management Sci. 31(9) 1150-1160.
4. Ball, M. O., M. Queyranne. 2009. Toward robust revenue management: Competitive analysis of online booking. Oper. Res. 57(4) 950-963.
5. Bertsimas, D., G. Perakis. 2006. Dynamic pricing: A learning approach. D. Hearn, S. Lawphongpanich, eds. Mathematical and Computational Models for Congestion Charging. Applied Optimization, Vol. 101. Springer, New York, 45-79.
6. Besbes, O., A. Zeevi. 2009. Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res., ePub ahead of print April 24, http://or.journal.informs.org/cgi/content/abstract/opre.1080. 0640v1.
7. Bitran, G., R. Caldentey. 2003. An overview of pricing models for revenue management. Manufacturing Service Oper. Management 5 203-229.
8. Bitran, G. R., S. V. Mondschein. 1997. Periodic pricing of seasonal products in retailing. Management Sci. 43(1) 64-79.
9. Bolton, P., C. Harris. 1999. Strategic experimentation. Econometrica 67(2) 349-374.
10. Bŕmaud, P. 1980. Point Processes and Queues, Martingale Dynamics. Springer-Verlag, New York.
11. Caro, F., J. Gallien. 2007. Dynamic assortment with demand learning for seasonal consumer goods. Management Sci. 53(2) 276-292.
12. Carvalho, A. X., M. L. Puterman. 2004. Dynamic pricing and learning over short time horizons. Working paper, Statistics Department, University of British Columbia, Vancouver, British Columbia, Canada.
13. Cheney, W. 2001. Analysis for Applied Mathematics. Springer-Verlag, New York.
14. Cope, E. 2004. Nonparametric strategies for dynamic pricing in e-commerce. Working paper, The Sauder School of Business, University of British Columbia, Vancouver, British Columbia, Canada.
15. Elmaghraby, W., P. Keskinocak. 2003. Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Sci. 49(10) 1287-1309.
16. Eppen, G. D., V. Iyer. 1997. Improved fashion buying with Bayesian updates. Oper. Res. 45(6) 805-819.
17. Eren, S., C. Maglaras. 2006. Pricing without market information. Working paper, Columbia University, New York.
18. Ethier, S. N., T. G. Kurtz. 1986. Markov Processes: Characterization and Convergence. Wiley, New York.
19. Farias, V. F., B. Van Roy. 2009. Dynamic pricing with a prior on market response. Oper. Res. Forthcoming.
20. Feng, Y., G. Gallego. 1995. Optimal starting times for end-of-season sales and optimal stopping times for promotional fares. Management Sci. 41 1371-1391.
21. Fleming, W. H., H. M. Soner. 1993. Controlled Markov Processes and Viscosity Solutions. Springer-Verlag, New York.
22. Gallego, G., G. van Ryzin. 1994. Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40 999-1020.
23. Gaur, V., M. L. Fischer, A. Raman. 2005. An econometric analysis of inventory turnover performance in retail services. Management Sci. 51 181-194.
24. Keller, G., S. Rady. 1999. Optimal experimentation in a changing environment. Rev. Econom. Stud. 66 475-507.
25. Lariviere, M. A. 2006. A note on probability distributions with increasing generalized failure rates. Oper. Res. 54(3) 602-604.
26. Lariviere, M. A., E. L. Porteus. 1999. Stalking information: Bayesian inventory management with unobserved lost sales. Management Sci. 4(3) 346-363.
27. Lim, A. E. B., J. G. Shanthikumar. 2006. Relative entropy, exponential utility, and robust dynamic pricing. Oper. Res. 55(2) 198-214.
28. Lobo, M. S., S. Boyd. 2003. Pricing and learning with uncertain demand. Working paper, Duke University, Durham, NC.
29. Lovejoy, W. S. 1990. Myopic policies for some inventory models with uncertain demand distribution. Management Sci. 36(6) 724-738.
30. Peskir, G., A. N. Shiryaev. 2000. Sequential testing problems for Poisson processes. Ann. Statist. 28 837-859.
31. Petruzzi, N. C., M. Dada. 2002. Dynamic pricing and inventory control with learning. Naval Res. Logist. 49 303-325.
32. Rockafellar, R. T. 1997. Convex Analysis. Princeton Landmarks in Mathematics. Princeton University Press, Princeton, NJ.
33. Scarf, H. 1958. Bayes solutions of the statistical inventory problems. Ann. Math. Statist. 30 490-508.
34. Shiryaev, A. N. 1978. Optimal Stopping Rules. Springer-Verlag, New York.
35. Smith, S. A., D. Achabal. 1998. Clearance pricing and inventory policies for retail chains. Management Sci. 44 285-300.
36. Talluri, K., G. van Ryzin. 2004. The Theory and Practice of Revenue Management. Springer Science, New York.
37. Xu, X., W. J. Hopp. 2005. Dynamic Pricing and Inventory Control: The Value of Demand Learning. Department of Industrial Engineering, Northwestern University, Evanston, IL.