Robust Optimization for Simulated Systems Using Risk Management and Kriging

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Mohseni, Ali | 2015

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 48223 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Mahlooji, Hashem
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 optimization problem, we use a two level Kriging metamodel. First we consider a design of all the variables and fit a reliable metamodel for the system outputs. Then, a larger design based on Taguchi’s approach is built. By combining risk management tactics with Taguchi’s view, we compute the risk for the system and fit Kriging for its outputs. Then the optimization problem which is minimizing this risk, is solved by means of nonlinear programming or global search methods
Keywords:
Simulation Optimization ; Taguchi Optimization ; Kriging Metamodel ; Risk Management ; Robust Optimization

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