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

Abstract

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