Intuitionistic Fuzzy Chance Constrained Programming for Handling Parametric Uncertainty: An Industrial Grinding Case Study

Uncertainty in the parameters of an optimization problem has a large impact on the outcome of the optimization results. Intuitionistic fuzzy chance constrained programming (IFCCP) is one technique used to handle optimization under uncertainty (OUU) problems. This technique assumes uncertain parameters as intuitionistic fuzzy numbers, which is a super set of conventional fuzzy numbers. With the assumption of intuitionistic fuzzy numbers, different degrees of risk can be modeled considering different viewpoints, e.g., optimistic, pessimistic, and mixed approaches. This generic concept of IFCCP has been applied on two different multiobjective optimization problems: the Binh korn test function, where uncertain parameters are linearly related, and a real life case study of an industrial grinding process, in which uncertain parameters are nonlinearly related. The proposed approach is generic and can be applied to any OUU problem. In addition to the trade-off between solution optimality and quality, sensitivity analysis has also been carried out