4.3 Article Proceedings Paper

Joint optimization of pricing and resource allocation in competitive telecommunications networks

期刊

NETWORKS
卷 50, 期 1, 页码 37-49

出版社

WILEY
DOI: 10.1002/net.20164

关键词

bilevel programming; integer programming; Lagrangian relaxation; pricing; resource allocation; revenue management; telecommunications

向作者/读者索取更多资源

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 [Cote 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. (C) 2007 Wiley Periodicals, Inc.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据