Title: Purposeful underestimation of demands for the airline seat allocation with incomplete information

Authors: Lijian Chen; Dengfeng Sun; Wen-Chyuan Chiang; Shuguang He

Addresses: School of Business Administration, Department of MIS, Operations Management, and Decision Sciences, University of Dayton, Dayton, OH 45469, USA ' School of Aeronautics and Astronautics Engineering, Purdue University, West Lafayette, IN 47907-2045, USA ' School of Finance, Operations Management, and International Business, Collins College of Business, The University of Tulsa, Tulsa, OK 74104, USA ' School of Management, Tianjin University, Tianjin 300072, China

Abstract: We study stochastic programming formulations for the origin destination model in airline seat allocation under uncertainty. In particular, we focus on solving the stability issues of the traditional probabilistic model by purposefully underestimating the demands. The stochastic seat allocation models assume at least the possession of the distributional information, which is usually difficult to satisfy in a constantly changing environment. We propose a heuristic that consists of dynamically incorporating available information by solving a sequence of stochastic programming models. We show that the proposed method, named 'seat reservation (SR)', can ease most negative effects of incomplete distributional information and under some restrictive conditions, the SR will yield optimal revenue. The seat reservation method suggests that a revenue management company must (1) obtain timely results using adequately up-to-date computational facilities; (2) be conservative when allocating resources and (3) actively and continually revise previous estimations.

Keywords: stochastic programming; incomplete information; airline seat allocation; revenue management; demand underestimation; uncertainty; stochastic modelling; seat reservations; resource allocation.

DOI: 10.1504/IJRM.2014.067333

International Journal of Revenue Management, 2014 Vol.8 No.1, pp.34 - 55

Received: 19 Jun 2014
Accepted: 31 Aug 2014

Published online: 04 Feb 2015 *

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