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Equivalence of piecewise-linear approximation and Lagrangian relaxation for network revenue management

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  • Sumit Kunnumkal
  • Kalyan Talluri

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    Abstract

    The network revenue management (RM) problem arises in airline, hotel, media, and other industries where the sale products use multiple resources. It can be formulated as a stochastic dynamic program but the dynamic program is computationally intractable because of an exponentially large state space, and a number of heuristics have been proposed to approximate it. Notable amongst these -both for their revenue performance, as well as their theoretically sound basis- are approximate dynamic programming methods that approximate the value function by basis functions (both affine functions as well as piecewise-linear functions have been proposed for network RM) and decomposition methods that relax the constraints of the dynamic program to solve simpler dynamic programs (such as the Lagrangian relaxation methods). In this paper we show that these two seemingly distinct approaches coincide for the network RM dynamic program, i.e., the piecewise-linear approximation method and the Lagrangian relaxation method are one and the same.

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    Bibliographic Info

    Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 1305.

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    Date of creation: Dec 2011
    Date of revision: Nov 2012
    Handle: RePEc:upf:upfgen:1305

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    Web page: http://www.econ.upf.edu/

    Related research

    Keywords: network revenue management; linear programming; approximate dynamic programming; Lagrangian relaxation methods;

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    1. Joern Meissner & Arne Strauss, 2008. "Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice," Working Papers, Department of Management Science, Lancaster University MRG/0008, Department of Management Science, Lancaster University, revised Apr 2010.
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