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Joint optimization of pricing and resource allocation in competitive telecommunications networks
Author(s) -
Bouhtou Mustapha,
Erbs Guillaume,
Minoux Michel
Publication year - 2007
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.20164
Subject(s) - computer science , mathematical optimization , lagrangian relaxation , revenue , competition (biology) , operator (biology) , order (exchange) , bilevel optimization , optimization problem , operations research , economics , mathematics , ecology , biochemistry , chemistry , accounting , finance , repressor , gene , transcription factor , biology
Yield management techniques have been used by companies in various competitive industrial contexts in order to keep a high level of revenue. With the opening of the telecommunications markets, operators are looking for ways of including competition in their decision process. By analyzing the customers' preferences, the market can be segmented into groups of similar preferences, and offers targeted to a particular market segment. In this paper, we study a problem of revenue management for a network operator offering services on end‐to‐end markets, while facing competition. We present a natural formulation for this problem that uses bilinear bilevel programming models, similar to those used in the airline industry [Côté et al., J Revenue Pricing Manage 2 (2003), 23–36]. However, such an approach leads to optimization problems that are very difficult to solve exactly on the large scale instances found in the telecommunications industry. To address difficulties solving large problems, we introduce a new alternative formulation for the problem, give a proof of NP‐hardness, and propose solution methods related to this formulation. The first one is an exact method based on a branch‐and‐bound algorithm; then we propose two approximate methods, one based on Lagrangian relaxation, and one based on a concave approximation of the objective function to be maximized. Comparative results are given. We show that this approach is practically efficient and leads to exact solutions for instances of telecommunications networks of a size larger than previously possible. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(1), 37–49 2007