Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems
Résumé
Due to their large variety of applications, complex optimisation problems induced a great effort to develop efficient solution techniques, dealing with both continuous and discrete variables involved in non-linear functions. But among the diversity of those optimisation methods, the choice of the relevant technique for the treatment of a given problem keeps being a thorny issue. Within the Process Engineering context, batch plant design problems provide a good framework to test the performances of various optimisation methods : on the one hand, two Mathematical Programming techniques – DICOPT++ and SBB, implemented in the GAMS environment – and, on the other hand, one stochastic method, i.e. a genetic algorithm. Seven examples, showing an increasing complexity, were solved with these three techniques. The result comparison enables to evaluate their efficiency in order to highlight the most appropriate method for a given problem instance. It was proved that the best performing method is SBB, even if the GA also provides interesting solutions, in terms of quality as well as of computational time.