Correspondence: Burak Büke, IWSE Department, The Ohio State University, Columbus, OH 43210, USA Tel: +1 512 963 1486; Fax: +1 614 292 7852; E-mail: buke.1@osu.edu

¹Burak Büke is a lecturer in Industrial, Welding and Systems Engineering Department at the Ohio State University. Prior to joining OSU, he attended the Operations Research and Industrial Engineering programme at the University of Texas at Austin, from where he received his PhD in December 2007. He also holds an MSc degree in Operations Research and Industrial Engineering from the University of Texas at Austin. His current research addresses parameter uncertainty issues for problems arising in manufacturing and service industries.

²Utku Yildirim is a senior operations research analyst at Disney Parks and Resorts. He is a member of the Decision Science and Support team, which is responsible for the development and deployment of revenue management solutions. Before joining Disney, Utku received his PhD degree in Operations Research and Industrial Engineering at the University of Texas at Austin. During his graduate studies, Utku worked on stability and pricing of queueing networks as well as hotel and airline revenue management problems.

³Harun Ahmet Kuyumcu is the founder of Prorize and engaged with The Rainmaker Group developing next-generation pricing solutions for gaming resorts and multifamily housing firms. He has more than 12 years of hands-on experience building profit-generating pricing systems across a wide range of industries. Previously chief scientist at Zilliant and senior scientist at Talus (now JDA), Ahmet has taught graduate-level courses in pricing and revenue management at the University of Texas at Austin. He has published several articles in professional journals and holds MS and PhD degrees in Operations Research from Texas A&M University.

Received 10 July 2007; Revised 10 July 2007; Published online 14 December 2007.

Abstract

It is well known that the network capacity control problem can be formulated as a dynamic programming model. However, this formulation is intractable in practice due to its size and complexity. As a result, various approximation methods are proposed in the literature. Decomposition and deterministic linear programming approximations are formulated and have been successfully used in practice. Lately, several stochastic programming (SP) approaches that take demand uncertainty into account have been published. This paper adds to recent research on SP methodologies by considering the customer's buy-up behaviour. We provide three new formulations based on different sets of assumptions. Then, we simulate demand arrival processes under four different simulation scenarios to compare the performance of each model with deterministic and randomised linear programming approximations.