Tuesday, 13:15 - 13:40 h, Room: MA 004


ihsan Yanikoglu
Robust simulation-based optimization with Taguchian regression models

Coauthor: Dick Den Hertog


A Taguchian way to deal with uncertain environmental parameters in simulation-based optimization is to create a regression model in both the optimization variables and the uncertain parameters, and then formulate the explicit optimization problem in terms of expectations and variances, or chance constraints. The disadvantages of this approach are that one has to assume that the distribution function for the uncertain parameters is normally distributed, and that both the mean and variance are known. The final solution may be very sensitive to these assumptions. We propose a Robust Optimization approach that do not need these assumptions. Based on historical data, uncertainty regions for the distribution is generated, and tractable robust counterparts are generated. This approach can be used for many types of regression models: polynomials, Kriging, etc. The novel approach is illustrated through numerical examples. Finally, for those simulation-based optimization problems that contain `wait-and-see' variables, we describe how to apply Adjustable Robust Optimization.


Talk 1 of the invited session Tue.2.MA 004
"Applications of robust optimization I" [...]
Cluster 20
"Robust optimization" [...]


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