This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references.

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Revenue Management: Research Overview and Prospects

In this paper, we examine the research and results of dynamic pricing policies and their relation to revenue management. The survey is based on a generic revenue management problem in which a perishable and nonrenewable set of resources satisfy stochastic price sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we formulate the stochastic control problem of capacity that the seller faces: How to dynamically set the menu and the quantity of products and their corresponding prices to maximize the total revenue over the selling horizon.

https://pages.stern.nyu.edu/~rcaldent/papers/Abstract-Survey-Paper-MSOM.pdf

Commissioned Paper: An Overview of Pricing Models for Revenue Management

Revenue management has become an important tool in the airline, hotel, and rental car industries. We describe asymptotic properties of revenue management policies derived from the solution of a deterministic optimization problem. Our primary results state that, within a stochastic and dynamic framework, solutions arising out of a single well-known linear program can be used to generate allocation policies for which the normalized revenue converges in distribution to a constant upper bound on the optimal value. We also show similar asymptotic results for expected revenues. In addition, we describe counterintuitive behavior that can occur when allocations are updated during the booking process (updating allocations can lead to lower expected revenue). These results add to the understanding of allocation policies and help to make concrete the statement that simple policies from easy-to-solve formulations can be relatively effective, even when analyzed in the more realistic stochastic and dynamic framework.

Asymptotic Behavior of an Allocation Policy for Revenue Management

Mathematical programming for network revenue management revisited

We develop a duopoly model to analyze the impact of air transport and high-speed rail (HSR) competition on the environment and social welfare. We show that the introduction of HSR may have a net negative effect on the environment, since it may result in additional demand, i.e., there is a trade-off between the substitution effect and the traffic generation effect. Furthermore, if environmental externalities are taken into account when assessing social welfare, the surplus measure may be higher when only air transport serves the market than when the two modes compete. When the airline and the HSR operator decide frequencies, the airline reduces the aircraft size in order to keep load factors high while offering lower frequency and carrying fewer passengers. In these circumstances, the introduction of HSR may be beneficial to the environment on a per seat basis only if the market size is large enough. When the HSR operator decides speed, it has incentive to keep it at the maximum level in order to reduce travel time. When the increase in the emissions of HSR due to the increase in the speed of the train is sufficiently high, the overall level of emissions grows after the introduction of HSR. Therefore, there can be a trade-off between the attractiveness of the service due to reduced travel time and the effects on the environment.

Would competition between air transport and high-speed rail benefit environment and social welfare?

Railway passenger transportation plays a fundamental role in Europe, particularly in view of the growing number of trains offering valuable services such as high speed travel, high comfort, etc. Hence, it is advantageous to submit seat inventories to a Yield Management system to get the maximum revenue. We consider a deterministic linear programming model and a probabilistic nonlinear programming model for the network problem with non-nested seat allocation. A first comparative analysis of the computational results obtained by the two models, both in terms of the overall expected revenue and in terms of CPU time, is carried out. Furthermore, we describe a new nonlinear algorithm for the solution of the probabilistic nonlinear programming model that exploits the structure of the optimization problem. The numerical results obtained on a set of real data show that, for this class of problems, this algorithm is more efficient than other standard algorithms for nonlinear programming problems.

G. Inzerillo

Papers

A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem