Sensitivity analysis of optimization : Examining sensitivity of bottleneck optimization to input data models

Abstract: The aim of this thesis is to examine optimization sensitivity in SCORE to the accuracy of particular input data models used in a simulation model of a production line. The purpose is to evaluate if it is sufficient to model input data using sample mean and default distributions instead of fitted distributions. An existing production line has been modeled for the simulation study. SCORE is based on maximizing any key performance measure of the production line while simultaneously minimizing the number of improvements necessary to achieve maximum performance. The sensitivity to the input models should become apparent the more changes required. The experiments concluded that the optimization struggles to obtain convergence when fitted distribution models were used. Configuring the input parameters to the optimization might yield better optimization result. The final conclusion is that the optimization is sensitive to what input data models are used in the simulation model.