Multiobjective Optimization of Mixed-Integer Linear Programming Problems: A Multiparametric Optimization Approach
Iosif Pappas, Styliani Avraamidou, Justin Katz, Baris Burnak, Burcu Beykal, Metin Türkay, Efstratios N. Pistikopoulos
Abstract
Industrial process systems need to be optimized, simultaneously satisfying financial, quality, and safety criteria. To meet all of those potentially conflicting optimization objectives, multiobjective optimization formulations can be used to derive optimal trade-off solutions. In this work, we present a framework that provides the exact Pareto front of multiobjective mixed-integer linear optimization problems through multiparametric programming. The original multiobjective optimization program is reformulated through the well-established ϵ-constraint scalarization method, in which the vector of scalarization parameters is treated as a right-hand side uncertainty for the multiparametric program. The algorithmic procedure then derives the optimal solution of the resulting multiparametric mixed-integer linear programming problem as an affine function of the ϵ parameters, which explicitly generates the Pareto front of the multiobjective problem. The solution of a numerical example is analytically presented to exhibit the steps of the approach, while its practicality is shown through a simultaneous process and product design problem case study. Finally, the computational performance is benchmarked with case studies of varying dimensionality with respect to the number of objective functions and decision variables.