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Pricing bridges to cross a river
Authors:Mustapha Bouhtou  Alexander Grigoriev  Stan van Hoesel  Anton F van der Kraaij  Frits CR Spieksma  Marc Uetz
Institution:1. France Télécom R&D, 39‐40 rue du Général Leclerc, F‐92131 Issy‐Les‐Moulineaux, France;2. Maastricht University, Quantitative Economics, P.O. Box 616, NL‐6200 MD Maastricht, The Netherlands;3. Katholieke Universiteit Leuven, Applied Economics, Naamsestraat 69, B‐3000 Leuven, Belgium;4. Maastricht University, Quantitative Economics, P.O. Box 616, NL‐6200 MD Maastricht, The NetherlandsMaastricht University, Quantitative Economics, P.O. Box 616, NL‐6200 MD Maastricht, The Netherlands
Abstract:We consider a pricing problem in directed, uncapacitated networks. Tariffs must be defined by an operator, the leader, for a subset of m arcs, the tariff arcs. Costs of all other arcs in the network are assumed to be given. There are n clients, the followers, and after the tariffs have been determined, the clients route their demands independent of each other on paths with minimal total cost. The problem is to find tariffs that maximize the operator's revenue. Motivated by applications in telecommunication networks, we consider a restricted version of this problem, assuming that each client utilizes at most one of the operator's tariff arcs. The problem is equivalent to pricing bridges that clients can use in order to cross a river. We prove that this problem is APX‐hard. Moreover, we analyze the effect of uniform pricing, proving that it yields both an m approximation and a (1 + lnD)‐approximation. Here, D is upper bounded by the total demand of all clients. In addition, we consider the problem under the additional restriction that the operator must not reject any of the clients. We prove that this problem does not admit approximation algorithms with any reasonable performance guarantee, unless P = NP, and we prove the existence of an n‐approximation algorithm. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007
Keywords:telecommunication networks  pricing  Stackelberg game  complexity  approximation
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