transition probabilities. Our approach is based on an optimization model. More specifically we consider two competing objectives …

We develop a framework for automated optimization of stochastic simulation models using Response Surface Methodology …. The framework is especially intended for simulation models where the calculation of the corresponding stochastic response … function is very expensive or time-consuming. Response Surface Methodology is frequently used for the optimization of …

can be estimated by optimization of the goodness-of-fit. We compared the efficiency and accuracy of the Response Surface … Methodology and the Nelder and Mead Simplex Method for optimization of microsimulation models. To this end, we tested several … respect to accuracy, Response Surface Methodology performed better in case of optimization of the microsimulation model …

simulation. DOE may have two goals: sensitivity analysis including factor screening and optimization. This contribution starts … simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random … Bifurcation. Then it discusses Kriging including Latin Hyper cube Sampling and sequential designs. It ends with optimization …

combination of the following four proven techniques: (1) discrete-event simulation, (2) heuristic optimization, (3) risk or …Whereas Operations Research has always paid much attention to optimization, practitioners judge the robustness of the …