Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems

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