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Revenue management in a dynamic network environment

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The authors investigate dynamic policies for allocating inventory to correlated, stochastic demand for multiple classes, in a network environment so as to maximize total expected revenues. Typical applications include airline networks or sequential reservations for a hotel or car rental service. They propose and analyse both theoretically and computationally a new algorithm, based on approximate dynamic programming, which provides structural insights into the optimal policy by using adaptive, non-additive bid-prices from a linear programming relaxation. They provide computational results that give insight into the performance of the new algorithm and the widely used bid price control, for several networks and demand scenarios. They extend the proposed algorithm to handle cancellations and no-shows by incorporating overbooking decisions in the underlying linear programming formulation. They report encouraging computational results that show that the new algorithm leads to higher revenues and more robust performance.

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