TY - UNPB

T1 - Pricing Structure Optimization in Mixed Restricted/Unrestricted Fare Environments

AU - Meissner, J

AU - Strauss, A K

PY - 2009

Y1 - 2009

N2 - In recent years, many traditional practitioners of revenue management such as airlines or hotels were confronted with aggressive low-cost competition. In order to stay competitive, these firms responded by cutting down fare restrictions that were originally meant to fence off customer segments, that is to prevent highyield customers from buying down. For the corresponding markets, unrestricted fares were introduced whose essentially only distinctive feature is its price. Some markets, however, were unaffected and here restrictions could be maintained as it was the case for long-haul flights, for example. We develop choice-based network revenue management approaches for such a mixed fare environment that can handle both the traditional opening or closing of restricted fare classes as well as handling pricing of the unrestricted fares simultaneously. For any such unrestricted fare, we assume a fixed number of price points to choose from. It is natural to ask then how these price points shall be chosen. To that end, we formulate the problem as a dynamic program and approximate it with an efficient mixed integer linear program (MIP) that selects the best n, say, price points out of a potentially large set of price candidates for each unrestricted fare. We show both theoretically and practically that it is advantageous to recompute price points later in the booking horizon using our approach. Furthermore, additional insight is gained from the fact that the dual values associated with MIP provide an upper bound on the value of having an additional price point. Numerical experiments illustrate the quality of the obtained price structure and that computational effort is relatively low given that we need to tackle the large-scale MIP with column generation techniques where each column pricing problem itself is NP-hard.

AB - In recent years, many traditional practitioners of revenue management such as airlines or hotels were confronted with aggressive low-cost competition. In order to stay competitive, these firms responded by cutting down fare restrictions that were originally meant to fence off customer segments, that is to prevent highyield customers from buying down. For the corresponding markets, unrestricted fares were introduced whose essentially only distinctive feature is its price. Some markets, however, were unaffected and here restrictions could be maintained as it was the case for long-haul flights, for example. We develop choice-based network revenue management approaches for such a mixed fare environment that can handle both the traditional opening or closing of restricted fare classes as well as handling pricing of the unrestricted fares simultaneously. For any such unrestricted fare, we assume a fixed number of price points to choose from. It is natural to ask then how these price points shall be chosen. To that end, we formulate the problem as a dynamic program and approximate it with an efficient mixed integer linear program (MIP) that selects the best n, say, price points out of a potentially large set of price candidates for each unrestricted fare. We show both theoretically and practically that it is advantageous to recompute price points later in the booking horizon using our approach. Furthermore, additional insight is gained from the fact that the dual values associated with MIP provide an upper bound on the value of having an additional price point. Numerical experiments illustrate the quality of the obtained price structure and that computational effort is relatively low given that we need to tackle the large-scale MIP with column generation techniques where each column pricing problem itself is NP-hard.

KW - Revenue Management

KW - Restriction-Free Pricing

KW - Network

KW - Pricing Structure

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Pricing Structure Optimization in Mixed Restricted/Unrestricted Fare Environments

PB - The Department of Management Science

CY - Lancaster University

ER -