Pricing structure optimization in mixed restricted/unrestricted fare environments

Abstract

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 reducing fare restrictions that were originally meant to fence off customer segments. In markets where traditional practitioners faced low-cost competition, unrestricted fares were introduced. Some markets, including airline long-haul markets, were unaffected. And here restrictions could be maintained.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. Due to technical constraints of the reservation system, we have a limit on the number of price points for each unrestricted fare. 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 a mixed integer linear program (MIP) that selects the best price points out of a potentially large set of price candidates for each unrestricted fare.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.

Designing and Pricing Services Based on Customer-Perceived Value: An Airline Company Feasibility Study

“…It has been heavily used in the airline industry since the 1980s (see Meissner and Strauss 2010 for more details), where perishable goods are sold, seat capacity is fixed, and demand is volatile. Pricing based on traditional revenue management concepts aim to segment customers according to their price sensitivity by imposing conditions and restrictions on the low fares offered (Botimer andBelobaba 1999, Meissner andStrauss 2010). Littlewood (1972) suggested treating the price management for flights as a control policy (Fiig et al 2010, Meissner andStrauss 2010).…”

“…Pricing based on traditional revenue management concepts aim to segment customers according to their price sensitivity by imposing conditions and restrictions on the low fares offered (Botimer andBelobaba 1999, Meissner andStrauss 2010). Littlewood (1972) suggested treating the price management for flights as a control policy (Fiig et al 2010, Meissner andStrauss 2010). He proposed a rule (referred to as Littlewood's rule) that involves considering each flight leg separately and accepting a low fare booking only if the revenue contribution received is greater than the cost of displacement (expected revenue loss) of later arrivals.…”

“…He proposed a rule (referred to as Littlewood's rule) that involves considering each flight leg separately and accepting a low fare booking only if the revenue contribution received is greater than the cost of displacement (expected revenue loss) of later arrivals. The popularity and wide use of such optimization techniques can be attributed to the creation of the EMSR heuristics by Belobaba (1987Belobaba ( , 1989; this method tests the effects of passengers buying up (Botimer and Belobaba 1999, Fiig et al 2010, Meissner and Strauss 2010. Two variations of this heuristic, referred to as EMSRa and EMSRb, were developed by Belobaba in 1992 and have been applied in industry; both make use of Littlewood's rule.…”