Blind Network Revenue Management

Operations Research, 2012, 60(6):1537-1550

49 Pages Posted: 12 Dec 2014

See all articles by Omar Besbes

Omar Besbes

Columbia Business School - Decision Risk and Operations

Assaf Zeevi

Columbia Business School - Decision Risk and Operations

Date Written: December 6, 2011

Abstract

We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time but does not know the underlying demand function that maps prices into instantaneous demand rate. We introduce a family of “blind” pricing policies that are designed to balance trade-offs between exploration (demand learning) and exploitation (pricing to optimize revenues). We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori, and we prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.

Keywords: revenue management; network; pricing; nonparametric estimation; minimax; learning; asymptotic optimality; curse of dimensionality

Suggested Citation

Besbes, Omar and Zeevi, Assaf, Blind Network Revenue Management (December 6, 2011). Operations Research, 2012, 60(6):1537-1550. Available at SSRN: https://ssrn.com/abstract=2536603

Omar Besbes (Contact Author)

Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States

Assaf Zeevi

Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States
212-854-9678 (Phone)
212-316-9180 (Fax)

HOME PAGE: http://www.gsb.columbia.edu/faculty/azeevi/

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