Pengembangan Metode Dekomposisi Benders untuk Optimisasi Robust

Abstract

Robust optimization assumes that uncertain data has a convex set and a finite set called uncertainty. The discussion begins by determin- ing the robust counterpart which is achieved by assuming the uncertain data set is in the uncertainty set in the box-interval. In this study, the robust counterpart is expressed in form of the box-interval uncertainty set. Then the robust counterpart formulation is presented as a mas- ter problem and sub problem. Robust Benders decomposition method is used to solve robust optimization problems where the objective function is convex and the problem constraints are quasiconvex. This method is applied to find the optimal robust solution in the feasible region for all data parameters. Numerical simulation of this problem is given manually and the process is continued using POM-QM software.