A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

34 Pages Posted: 30 Aug 2011

See all articles by Serkan Eren

Serkan Eren

Independent

Costis Maglaras

Columbia University - Columbia Business School, Decision Risk and Operations

Multiple version iconThere are 2 versions of this paper

Date Written: April 11, 2009

Abstract

We propose a tractable, data-driven demand estimation procedure based on the use maximum entropy (ME) distributions, and apply it a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare-class "Littlewood" problem in a setting where the firm has access to only potentially censored sales observations. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the "spiral down" effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.

Keywords: Revenue management, censored demand, uncensoring, spiral down, maximum en- tropy distributions, stochastic approximations

Suggested Citation

Eren, Serkan and Maglaras, Costis, A Maximum Entropy Joint Demand Estimation and Capacity Control Policy (April 11, 2009). Columbia Business School Research Paper , Available at SSRN: https://ssrn.com/abstract=1919155 or http://dx.doi.org/10.2139/ssrn.1919155

Serkan Eren

Independent ( email )

Costis Maglaras (Contact Author)

Columbia University - Columbia Business School, Decision Risk and Operations ( email )

New York, NY
United States

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