In this paper, we consider the classical multi-fare, single resource (leg) problem in revenue management for the case where demand information is limited. Our approach employs a competitive analysis, which guarantees a certain performance level under all possible demand scenarios. The only information required about the demand for each fare class is lower and upper bounds. We consider both comp...

W within a Markov decision process (MDP) framework, we study revenue management policies that combine aspects of mathematical programming approaches and pure MDP methods by decomposing the problem by time, state, or both. The “time decomposition” policies employ heuristics early in the booking horizon and switch to a more-detailed decision rule closer to the time of departure. We present a fami...

We develop a Markov decision process formulation of a dynamic pricing problem for multiple substitutable flights between the same origin and destination, taking into account customer choice among the flights. The model is rendered computationally intractable for exact solution by its multidimensional state and action spaces, so we develop and analyze various bounds and heuristics. We first desc...

We consider a seller who wishes to sell multiple goods by a deadline, such as the end of a season. Potential buyers enter over time, are long-lived and form rational expectations, so can strategically time their purchases. At any point in time, profit is maximized by awarding the good to the buyer with the highest valuation exceeding a cutoff. These cutoffs are deterministic, depending only on ...

number: 007-0058 Adaptive Critics for Airline Revenue Management Abhijit Gosavi Department of Industrial and Systems Engineering University at Buffalo, SUNY 317 Bell Hall, Buffalo, NY 14260 [email protected] POMS 18th Annual Conference Dallas, Texas, U.S.A. May 4 to May 7, 2007. Abstract We present an approximate dynamic programming (DP) technique, called adaptive critic, for solving an airli...

In this paper, we study optimal revenue management applied to carparks, with primary objective to maximize revenues under a continuous-time framework. We develop a stochastic discrete-time model and propose a rejection algorithm that makes optimal decisions (accept/reject) according to the future expected revenues generated and on the opportunity cost that arises before each sale. For this aspe...