Robust Optimization for Simulated Systems Using Risk Management and Kriging, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
Many simulation optimization problems are defined in random settings and their inputs have uncertainty. Therefore, in defining an optimal solution for these problems, uncertainties should be taken into account. The primary way of dealing with this , is Robust Optimization which finds solution immune to these changing settings. Aiming at finding a new approach for simulation optimization problems, this study investigates these uncertainties and robust methods. In the optimization problem, the goal and constraints are considered with separate risk measures and a related problem is defined as follows: Minimizing the weighted sum of all risks subject to the problem constraints. To solve the...
Cataloging briefRobust Optimization for Simulated Systems Using Risk Management and Kriging, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
Many simulation optimization problems are defined in random settings and their inputs have uncertainty. Therefore, in defining an optimal solution for these problems, uncertainties should be taken into account. The primary way of dealing with this , is Robust Optimization which finds solution immune to these changing settings. Aiming at finding a new approach for simulation optimization problems, this study investigates these uncertainties and robust methods. In the optimization problem, the goal and constraints are considered with separate risk measures and a related problem is defined as follows: Minimizing the weighted sum of all risks subject to the problem constraints. To solve the...
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