A framework for developing optimization-based decision support systems

Optimization-based decision support systems (DSSs) are an interesting and important area among the many classes of decision support systems. This paper presents SiGMA, a generic core to build Optimization-based DSSs that tries to be as generic as possible on the on-line addition and use of solvers while preserving the maximum functionality on the Analysis stage that this criterion allows. SiGMA serves as a framework to build more complex DSSs where problem specific knowledge can be used to improve the functionality available at the Formulation and Analysis stages. Two application examples from different domains are also presented: SiGMAPhub and SiGMAProt. These applications include additional analysis capabilities for the p-hub and the protein structure comparison problems, respectively.