A Soft-Computing Approach to Fuzzy EOQ Model for Deteriorating Items with Partial Backlogging

Abstract

Genetic Algorithm (GA) is an optimized method to find a perfect solution which is based on general genetic process of life cycle. In this article we discussed a crisp and a fuzzy inventory model keeping its demand rate constant for the imprecision and uncertainly deteriorating items with special reference to shortage and partially backlogging systems. The objective of this paper is to minimize the total cost of fuzzy inventory environment for which Graded mean representation, Signed distance and Centroid methods are used to defuzzify the total cost of the systems. Consequently, we are comparing the total average cost, obtained through these methods with the help of numerical example, and sensitively analysis is also given to show the effects of the values on these items. Moreover, Genetic Algorithm (GA) is also applied to the optimistic value of the total cost of the crisp model for the effective and fruitful results.