Intuitionistic fuzzy expected value model for industrial grinding process

During deterministic optimization, only the decision variables are varied whereas all other parameters appearing in the given optimization framework are kept constant. However, some of these parameters are subjected to real life uncertainty and assuming them as constants during the course of optimization leads to suboptimal or infeasible solutions. This paper presents intuitionistic fuzzy expected value model (IFEVM) technique to handle such problems under the category of optimization under uncertainty (OUU). In this approach, uncertain parameters are assumed as intuitionistic fuzzy numbers, which considers not only the degree of membership but also the degree of non-membership to a given set. The concept of IFEVM has been applied to an industrial grinding process where parameters are non-linearly related to one another. Different degrees of risk averseness of a decision maker can be modelled under this framework considering different viewpoints viz., aggressive, conservative and balanced approach. The resultant deterministic equivalent of the multi-objective intuitionistic fuzzy uncertain optimization problem has been solved using a variant of an evolutionary optimization technique, intuitionistic fuzzy expected value non-dominated sorting genetic algorithm II (IFEV-NSGAII), and various scenarios are analyzed