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Abstract or Table of Contents
Abstract: "Optimization of engineering problems relies on the use of modern softwares for solving algebraic models, including NLP, MILP andMINLP models. Although a number of powerful optimization packages are currently available, it is still far from being trivial for general design engineers to properly reformulate optimization models. Since a qualitative knowledge base by experts in optimization does exist for modelling, the objective of our work is to develop an interface written in LISP to encode this knowledge to formulate the model so as to increase the robustness and the efficiency of algorithms for nonlinear and integer problems. In this work, models specified in GAMS input files are automatically preprocessed by the interface which generates a new well-formulated model.Capabilities include constraint reformulation to induce linearity and convexity, tightening of bounds, scaling as well as fixing 0-1 variables a priori. Computational results are presented to illustrate how the proposed interface can reformulate poor models to increase both therobustness and the efficiency of the optimization methods."