Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm

Abstract

Inventory management is a crucial task for any industry. In this paper, we have determined the optimum profit and economical order quantity under variety of assumptions such as the demand per unit time follows either a log-normal or a generalized exponential distribution. Parametric relationship between these two distributions, the proposed models become comparable. For modeling, we consider the expected demand and variable deterioration. Under these probabilistic assumptions, inventory models are developed for situations like no, complete and partial backlogging. Classical methods are unable to solve these situations under these assumptions. Thus genetic algorithm is proposed to solve these models. Economic order quantity is obtained for maximizing the total profit for the respective demand per unit time distributions. A real-world case study of a deteriorated product is presented to illustrate the procedures of the proposed inventory models.