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 193

the RM problems can be accomplished by different techniques that may be classified in two
main groups:
 Algorithmic optimization. Since the purpose of the model base is to cover a generality

of infrastructure allocation problems, it is necessary to incorporate a generic algorithmic
solution. Among the different optimization techniques, we find particularly interesting
to explore the capabilities of Constraint Programming (CP). CP shows natural fitting
with this generic approach due to three main characteristics (Tsang, 1993): in the first
place, its flexibility in real-time definition of variables, constraints and objective
functions; in the second place, the use of domain variables which makes it easier to
handle infrastructure access constraints; and thirdly, the ease to define complex multi-
criteria objective functions. Nevertheless, it appears to be a hard challenge to achieve a
pre-compiled engine capable of dealing with the diversity of optimization problem
families that can be modeled with the proposed model base.

 Simulation-based optimization. When stochastic parameters are involved—in RM

problems, typically customer arrivals and service times, and occasionally others like
customer behavior and choice preferences—simulation appears to be the natural
approach. We believe that the model base is extensible to handle a generality of RM
simulation models. The extension would stem from defining a 4 th layer in the modeling
hierarchy which would correspond to the execution level. This layer would correspond
to the database model of the DSS and encompass all the execution data associated with
a set of simulations of the system to be optimized.

8. Conclusions

A generic model base design for a DSS intended to deal with the problem of maximizing
revenue in the allocation of service infrastructures offers great flexibility and allows
overcoming the intrinsic dependency of the algorithm on the business process model. This
dependency hinders the application of RM techniques through traditional DSS. The
hierarchical metamodel-model-instance approach proposed to define the RM DSS model
base here shows great potential and addresses the three core elements of the mentioned
algorithm dependency: The specific traits of the business processes, the decision variable(s)
to be optimized and the business objective(s) to be achieved. It also supports the structured
definition of business process typologies along these dimensions, which can lead to
applications of generalized interest to the RM community regarding the definition of
taxonomies of RM optimization problems. Furthermore, this approach allows defining and
including in the DSS the specific parameters required in a given model without ad-hoc
coding or compilation. Examples taken from the Hotel and Health Care sectors illustrate the
potential of the proposed generic approach. The modeling hierarchy can be extended to
encompass the data model of the RM DSS database component.

9. Acknowledgment

This work is supported by the research project DPI2008-04872, “Optimization of service
infrastructures assignment through simulation - hotel and health sectors” (Spanish National
Research Plan) and by the Human Resources Mobility Action I-D+i 2008-2011 of the Spanish
Ministry of Education for a visiting scholar stay at MIT Center for Transportation &
Logistics.