A stochastic approximation algorithm for making pricing decisions in network revenue management problems

Journal of Revenue and Pricing Management

Volumn 9, Issue 5, 2010, Pages 419-442

  Kunnumkal, Sumit ^a   Topaloglu, Huseyin ^b  

^a INDIAN SCHOOL OF BUSINESS   (India)

^b Cornell University   (United States)

Author keywords

network revenue management; pricing; stochastic approximation

Indexed keywords

EID: 78649502118     PISSN: 14766930     EISSN: 1477657X     Source Type: Journal    
DOI: 10.1057/rpm.2010.27     Document Type: Article

References (30)

1. Bashyam, S. and Fu, M. C. (1998) Optimizaton of (s, S) inventory systems with random lead times and a service level constraint. Management Science 44(12):243-256.
2. Benveniste, A., Metivier, M. and Priouret, P. (1991) Adaptive Algorithms and Stochastic Approximations. New York: Springer.
3. Bertsimas, D. and de Boer, S. (2005) Simulation-based booking limits for airline revenue management. Operations Research 53(1):90-106.
4. Bertsekas, D. P. and Tsitsiklis, J. N. (1996) Neuro-Dynamic Programming. Belmont, MA: Athena Scientific.
5. Bitran, G. and Caldentey, R. (2003) An overview of pricing models for revenue management. Manufacturing & Service Operations Management 5(3):203-229.
6. Chaneton, J. and Vulcano, G. (2009) Computing Bid-prices for Revenue Management under Customer Choice Behavior. Stern School of Business, New York University. Technical Report.
7. Elmaghraby, W. and Keskinocak, P. (2003) Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science 49(10):1287-1309.
8. Feng, Y. and Gallego, G. (1995) Optimal starting times for end-of-season sales and optimal stopping times for promotional fares. Management Science 41(8):1371-1391.
9. Feng, Y. and Gallego, G. (2000) Perishable asset revenue management with markovian time dependent demand intensities. Management Science 46(7):641-656.
10. Feng, Y. and Xiao, B. (2000) A continuous-time yield management model with multiple prices and reversible price changes. Management Science 46(5):644-657.
11. Fu, M. (1994) Sample path derivatives for (s, S) inventory systems. Operations Research 42(2):351-363.
12. Gallego, G. and van Ryzin, G. (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science 40(8):999-1020.
13. Gallego, G. and van Ryzin, G. (1997) A multiproduct dynamic pricing problem and its applications to yield management. Operations Research 45(1):24-41.
14. Glasserman, P. (1994) Perturbation analysis of production networks. In: D. D. Yao (ed.) Stochastic Modeling and Analysis of Manufacturing Systems. New York: Springer.
15. Glasserman, P. and Tayur, S. (1995) Sensitivity analysis for base-stock levels in multiechelon production-inventory systems. Management Science 41(2):263-281.
16. Karaesmen, I. and van Ryzin, G. (2004) Overbooking with substitutable inventory classes. Operations Research 52(1):83-104.
17. Kleywegt, A. J. (2001) An Optimal Control Problem of Dynamic Pricing. School of Industrial and Systems Engineering, Georgia Institute of Technology, Technical Report.
18. Kunnumkal, S. and Topaloglu, H. (2008) Using stochastic approximation algorithms to compute optimal base-stock levels in inventory control problems. Operations Research 56(3):646-664.
19. Kunnumkal, S. and Topaloglu, H. (2009) A Stochastic Approximation Algorithm to Compute Bid Prices for Joint Capacity Allocation and Overbooking over an Airline Network. School of Operations Research and Information Engineering, Cornell University. Technical Report, http://legacy.orie cornell.edu/~huseyin/publications/publications.html.
20. Kushner, H. J. and Clark, D. S. (1978) Stochastic Approximation Methods for Constrained and Unconstrained Systems. Berlin, Germany: Springer-Verlag.
21. Kushner, H. J. and Yin, G. G. (2003) Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer.
22. Maglaras, C. and Meissner, J. (2006) Dynamic pricing strategies for multiproduct revenue management problems. Manufacturing & Service Operations Management 8(2):136-148.
23. Mahajan, S. and van Ryzin, G. (2001) Stocking retail assortments under dynamic customer substitution. Operations Research 49(3):334-351.
24. McGill, J. I. and van Ryzin, G. J. (1999) Revenue management: Research overview and prospects. Transportation Science 33(2):233-256.
25. Talluri, K. T. and van Ryzin, G. J. (2005) The Theory and Practice of Revenue Management. New York: Springer.
26. Topaloglu, H. (2008) A stochastic approximation method to compute bid prices in network revenue management problems. INFORMS Journal on Computing 20(4):596-610.
27. Van Ryzin, G. and McGill, J. (2000) Revenue management without forecasting or optimization: An adaptive algorithm for determining airline seat protection levels. Management Science 46(6):760-775.
28. Van Ryzin, G. and Vulcano, G. (2008a) Computing virtual nesting controls for network revenue management under customer choice behavior. Manufacturing & Service Operations Management 10(3):448-467.
29. Van Ryzin, G. and Vulcano, G. (2008b) Simulation-based optimization of virtual nesting controls for network revenue management. Operations Research 56(4):865-880.
30. Zhao, W. and Zheng, Y.-S. (2000) Optimal dynamic pricing for perishable assets with nonhomogenous demand. Management Science 46(3):375-388.