About the Relevance of Mathematical Programming and Stochastic Optimisation Methods : Application to Optimal Batch Plant Design Problems

Abstract

Due to their large variety of applications, combinatorial optimisation problems led to a great effort to develop efficient solution techniques. But among the diversity of those optimisation methods, the choice of the relevant technique to the treatment of a given problem keeps being hard. Within the Process Engineering framework, Optimal Batch Plant Design problems provide a good support to tackle, on the one hand, two Mathematical Programming techniques – DICOPT and SBB, implemented in the GAMs software – and, on the other hand, one stochastic method, i.e. a Genetic Algorithm. Various instances of a problem, with an increasing complexity, were solved with these techniques which performances were compared : the results enable to judge their efficiency in order to propose the most appropriate method according to the studied problem instance.