A model-solver integration framework for autonomous and intelligent model solution

Complex mathematical models are being increasingly adopted for corporate decision-making, and lay users are becoming more involved through institutional decision-making processes. Because of the technical complexity and variability of mathematical models, difficulties arise in supporting complicated model solution processes and in maintaining new models with existing solvers (i.e., problem-solving algorithms). This paper proposes an intelligent model-solver integration framework that facilitates an intuitive and user-friendly model solution process and evolutionary model maintenance. Specifically, for an intuitive model solution, the framework gives a model management system the ability to suggest autonomously compatible solvers of a model without direct user intervention. In addition, it solves the model by matching intelligently model parameters with solver parameters without any serious conflicts. Thus, the framework improves the productivity of institutional model solving tasks by relieving the user from the risk of erroneous application of a solver to syntactically and semantically incompatible models, and by reducing the burden given by the considerable learning process of model and solver semantics.