Fuzzy Expected Value Analysis of an Industrial Grinding Process

Virivinti, N and Mitra, Kishalay (2014) Fuzzy Expected Value Analysis of an Industrial Grinding Process. Powder Technology, 286. pp. 9-18. ISSN 0032-5910

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Abstract

Uncertainty in parameters, which are assumed to be known and do not change their values during the course of deterministic optimization, can have a great impact on the outcome of an optimization study. Investigations on the development and application of optimization approaches that can accommodate such kind of uncertainty in parameters during the course of optimization are, therefore, necessitated. One of the methods to overcome such situations is to assume uncertain parameters as fuzzy variables, when distribution information for uncertain parameters are not available, and solve an equivalent deterministic formulation by transforming the original uncertain formulation. Expected value model (EVM) is one such method which converts the uncertain optimization formulation into a deterministic problem using expected values of the objective functions and constraints based on fuzzy credibility theory. In this work, an industrial grinding model has been adapted under the credibility theory based fuzzy framework to handle several uncertain parameters and shown how the presence of uncertainty leads to an operating zone of varied risk appetite of a decision maker by defining the entire frontier of the uncertain solution region. The deterministic multi-objective optimization model has been taken from the published work [1] and several modifications due to uncertainty in the parameters are carried out on this. The resultant deterministic equivalent of the multi-objective fuzzy uncertain optimization problem has been solved using Fuzzy Expected Nondominated Sorting Genetic Algorithms II (FENSGA-II). Unlike the two-stage stochastic programming (TSSP) approach, a very popular approach to handle uncertainty during optimization, the generic fuzzy approach does not give rise to the situation of unmanageable explosion in problem size with the increase in number of uncertain parameters

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