A Model Context-Based Solution Framework in Decision Support Systems

Abstract

Research in the decision sciences has continued to develop a variety of mathematical models as well as software tools supporting corporate decision-making. Yet, in spite of their potential usefulness, the models are little used in real-world decision making since in the presence of a decision model, the flexible selection and execution of solvers is still unsatisfactory model and thus solution processes are still too complex for ordinaty users to apply appropriate solvers to the model. This paper proposes a model context-based solution framework that enables the user to solve decision problems using models and their associated multiple solvers without requiring precise knowledge of the model-solution processes. Specifically, for intuitive model-solution, the framework enables a model management system to suggest the compatible solvers of a model autonomously without direct user intervention and to solve the model by matching the model and solver parameters intelligently without any serious conflicts. Thus, the framework would improve the productivity of institutional model solving tasks by relieving the user from the burden of leaning model and solver semantics requiring considerable time and efforts.