Generic Model Base Design for Decision Support Systems in Revenue Management: Applications to Hotel and Health Care Sectors - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 183

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Generic Model Base Design for Decision
Support Systems in Revenue Management:
Applications to Hotel and Health Care Sectors

Miguel Gutiérrez and Alfonso Durán
Área de Ingeniería de Organización, Universidad Carlos III de Madrid
Spain

1. Introduction

The sustainable development of an increasingly service-based economy requires procedures
for the efficient allocation, to the various existing user classes, of non-storable service
infrastructures with essentially fixed costs and whose value potential is dilapidated if not
utilized. That decision is often taken in phases; for instance, assigning a hospital wing to a
specific use (e.g. surgery or radiology) is a long-term decision, given the high refurbishing
costs. Similarly, strategic decisions have to be made regarding the distribution of hotel
rooms in single, double, suites... In a second decision stage, operational decisions will be
taken, such as the individual patients or customers to whom those infrastructures will be
assigned.
The aforementioned problem of infrastructure allocation is frequently addressed by
Revenue Management (RM) techniques—also known as Yield Management—. RM
techniques were initially developed to deal with strategies regarding the offering and
allocation of flight seats. According to Zaki (2000) “the main objective of RM is to sell the
right seat to the right customer at the right time for the right price to maximize the profit.”
In a more general approach, RM is the process of reacting or anticipating to the consumer
behaviour, so that the revenue is optimized. It implies the use of dynamic forecasting
models, the allocation of perishable resources/services to the diverse price categories and
channels, as well as the price to charge for every category-channel (Talluri & Van Ryzin,
2004).
To accomplish these tasks, RM uses optimization heuristics, whose relative effectiveness
depends strongly on the characteristics of the demand, such as predictability, differences in
sensitivity to the price between the different segments of clients and temporary pattern of
evolution of the relative weight of each segment of clients when the date of execution
approaches.
On the other hand, RM algorithms reflect (and therefore are contingent upon) the design of
the business process whose optimization they must support (in this case, infrastructure
assignment) in three related ways:
 The specific traits of the business process, such as whether overbooking is allowed or

not and whether two different prices can be offered simultaneously through two
different channels or not. It would also encompass such aspects as alternative