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customer discrimination, capacity and revenue management that allows highly
reactive and time-based pricing is vital for LCC.
Revenue Management (RM) has been “invented” by American Airlines in
1987 and has developed into an important strategy in many business areas like
car rental, hotels etc. Revenue management deals with the problem of effectively
using perishable resources or products in industries or markets with high fixed
cost and low margins, which are price-segmentable (see [4], [9], and [5]). RM can
shortly be described as “selling the right product to the right customer at the
right time through the right channel for the right price” (cf. [8], [6]). Thus, RM is
a complex tactical planning problem which has to be supported by sophisticated
forecasting models/systems and optimization models/systems.
In contrast to the pricing and capacity planning at full-cost carriers, which op-
erate a hub and spoke network enabling numerous relations through connecting
flights, RM for LCC follows a completely different logic. The central (dynamic)
pricing paradigm of a LCC is to offer for each flight only one single price at
a time and to increase this price constantly towards the end of the booking
period. Now, as the low-cost market is becoming more saturated, some LCC
also try to achieve further growth by offering connecting flights. Yet, the pricing
paradigm remains still valid and it is, therefore, the central assumption for our
development. With this trend traditional RM systems for LCC designed only for
point-to-point trac become ineffective.
In this paper we present an optimization model which can handle direct as
well as connecting flights. The model reflects the real situation that the airline
has established a basic price schema
P
and that it specifies for every flight a
subset of potential prices in advance. The advantage of our model is twofold.
First, there is no need to adapt the solution to the price schema, i.e., to round
optimal prices and to split optimal capacities as is necessary for some established
systems which by this post-processing loose the optimality property. Secondly,
the model can quite straightforwardly be extended to also consider connecting
flights. We present computational results with this model on real data.
The intention of our case study has been the development of a model which
can easily replace the existing model without changing the basic logic of the
overall process and the systems. The development or testing of new approaches
for modeling demand and customer choice, as well as different pricing paradigms
such as bid price based methods and virtual nesting, is not in the focus of the
paper.
The purpose of the computational study has been the identification of the
loss in revenue if the RM-system for point to point connections is used for small
connection networks with manual adaptions only (as it was done by the airline)
instead of applying our simple LP-model.
The paper is structured as follows. In Section 2 we shortly introduce revenue
management in LCC. In Section 3 after presenting a basic model we introduce
our models. In Section 4 we report some experiments with these models on
empirical data and we close with a conclusion in Section 5.
