Parametric Sensitivity Through Optimization Under Uncertainty Approach

While mimicking a physical phenomenon in a computational framework, there are tuning parameters quite often pre-\ud sent in a computational model. These parameters are generally tuned with the experimental data to capture the process\ud behavior as close as possible. Any optimization study based on this model assumes the values of these tuning parameters\ud as constant. However, it is known that these parameters are subjected to inherent source of uncertainties such as errors in\ud measurement or model tuning etc. for which they are not tuned for. Assuming these parameters constant for rest of the op-\ud timization is, therefore, not realistic a\ud nd one should ideally check the sensitivity of these parameters on the final results.\ud In\ud this study, we are going to use approach based on the paradigm of optimization under uncertainty that allows a decision\ud maker to carry out such an analysis. Additionally, this study\ud captures the tradeoff between solution quality and solution\ud reliability that is captured here using non-dominated gene\ud tic algorithm II. The generic concept has been applied on\ud a grinding process model and can be extended to any other process model