Dynamic Pricing, Revenue Optimization, Choice Modeling, Assortment Optimization, Supply Chain Management, Inventory Theory

• Informs Management Science Best Paper Award 2017
• Informs Impact Prize 2016
• HKIE Fellow class 2016
• Keynote Speaker INFORMS Annual meeting 2013
• MSOM Fellows class of 2013
• Informs Fellow class of 2012
• Informs Revenue Management and Pricing Practice Prize, Berlin (2012)
• AGIFORS technical paper award, Barcelona (2012)
• Informs Revenue Management and Pricing Historical Prize, with G. van Ryzin (2011)
• AGIFORS Best technical paper award, Montreal (2008)
• AGIFORS Best paper award, Tahiti (2008)
• AGIFORS Best paper award, Korea (2007)
• IBM Faculty Award (2006-2007)
• Informs Revenue Management and Pricing Section Award (2005)
• AGIFORS Best innovation award, Brazil (2005)
• AGIFORS Best paper award, South Africa (2005)
• IBM Faculty award (2005-2006)
• AGIFORS Best paper award, New Zealand (2004)
• NSF Research Initiation Award (2000)
• AT&T Scholarship
• Alpha Pi Mu
• Omega Rho

• The Institute for Operations Research and the Management Sciences
• Manufacturing and Operations Management Society
• The Production and Operations Management Society
• Pricing and Revenue Management Section
• The Airline Group of the International Federation of Operational Research Societies
• Society of Hispanic Professional Engineers

1. G. Gallego and G. van Ryzin (1994), Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons, Management Science, 40, pp. 999-1020. [Top 5 in Citation]


2. G. Gallego and I. Moon (1993), The Distribution Free Newsboy Problem: Review and Extensions, Journal of the Operational Research Society, 44, pp. 825-834. [Top 5 in Citation]


3. G. Gallego and G. van Ryzin (1997), A Multiproduct Dynamic Pricing Problem and its Applications to Network Yield Management, Operations Research, 45, pp. 24-41. [Top 5 in Citation]

   Abstract

   A firm has inventories of a set of components that are used to produce a set of products. There is a finite horizon over which the firm can sell its products. Demand for each product is a stochastic point process with an intensity that is a function of the vector of prices for the products and the time at which these prices are offered. The problem is to price the finished products so as to maximize total expected revenue over the finite sales horizon. An upper bound on the optimal expected revenue is established by analyzing a deterministic version of the problem. The solution to the deterministic problem suggests two heuristics for the stochastic problem that are shown to be asymptotically optimal as the expected sales volume tends to infinity. Several applications of the model to network yield management are given. Numerical examples illustrate both the range of problems that can be modeled under this framework and the effectiveness of the proposed heuristics. The results provide several fundamental insights into the performance of yield management systems.


4. Y. Feng and G. Gallego (1995), Optimal Starting Times for End of Season Sales and Optimal Stopping Times for Promotional Fares, Management Science, 41, pp. 1371-1391. [Top 5 in Citation]


5. G. Gallego and Ö Özer (2001), Integrating Replenishment Decisions with Advance Demand Information, Management Science, 47, pp. 1344-1360. [Top 5 in Citation]

   

   Abstract

   There is a growing consensus that a portfolio of customers with different demand lead times can lead to higher, more regular revenues and better capacity utilization. Customers with positive demand lead times place orders in advance of their needs, resulting in advance demand information. This gives rise to the problem of finding effective inventory control policies under advance demand information. We show that state-dependent (s, S) and base-stock policies are optimal for stochastic inventory systems with and without fixed costs. The state of the system reflects our knowledge of advance demand information. We also determine conditions under which advance demand information has no operational value. A numerical study allows us to obtain additional insights and to evaluate strategies to induce advance demand information.


6. G. Gallego, R. Ratliff and S. Shebalov (2014), A General Attraction Model and Sales-based Linear Program for Network Revenue Management under Customer Choice, Operations Research, 63 (1), pp. 212-232. [Recent Publications]


7. G. Gallego, R. Wang, M. Hu, J. Ward and J. L. Beltran (2014), No Claim? Your Gain: Design of Residual Value Extended Warranties Under Risk Aversion and Strategic Claim Behavior, MSOM, 17 (1), 87-100. [Recent Publications]


8. G. Gallego and H. Topaloglu (2014), Constrained Assortment Optimization for the Nested Logit Model, Management Science, 60, pp. 2583-2601. [Recent Publications]


9. J. Davis, G. Gallego and H. Topaloglu (2014), Assortment Optimization under Variants of the Nested Logit Model, Operations Research, 62 (2), pp. 250-273. [Recent Publications]


10. G. Gallego and R. Wang (2014), Multiproduct Price Optimization and Competition under the Nested Logit Model with Product-differentiated Price Sensitivities, Operations Research, 62 (2), pp. 450-461. [Recent Publications]


11. N. Chen and G. Gallego (2016), Do Consumers Benefit from Dynamic Pricing? Available at SSRN: http://ssrn.com/abstract=2708892 [Papers Submitted for Publication]


12. G. Gallego, A. Li, VA Truong and X. Wang (2015), Online Resource Allocation with Customer Choice, Available at  arXiv:1511.01837v1 [math.OC] [Papers Submitted for Publication]