Mining linear programming models from databases using means ends analysis and artificial neural network

Abstract

Abstract Since formulating linear programming models from scratch is knowledge-intensive and, hence, very costly, knowledge-based formulation support systems have been proposed. The drawback of knowledge-based formulation support systems, however, is that they require that sufficient domain knowledge be captured in advance. Hence, the purpose of this paper is to propose a methodology that automatically recognizes and captures relevant knowledge on formulating linear programming models from a relational database. Our methodology has two components. First, first-cut models are recognized from a data dictionary via means-ends analysis (MEA). Second, valid first-cut models are isolated through the application of an artificial neural network technique. To demonstrate the integrity of our methodology, Model Miner, a prototype system, is described and tested.