Mixed-integer multiobjective process planning under uncertainty

This paper presents a methodology for addressing investment planning in the process industry using a mixed-integer multiobjective approach. The classic single-objective MILP stochastic model is treated as a multiobjective programming problem by the use of multiparametric decomposition. To ensure computation of the entire efficient frontier, use of the augmented Tchebycheff algorithm is proposed. This allows for the decision making to be based on the suggested solutions and their neighbor solutions, a feature that other stochastic programming models fail to capture. An iterative procedure is proposed to hell) the decision maker visualize the efficient solutions in the multidimensional space and facilitate the assessment of the economical risk of the project.