Abstract
In this paper, a new hybrid Multi-Objective Genetic Algorithm (MOGA) is introduced to deal with the ABC Multi-Criteria Inventory Classification (MCIC). The aim of this classification is to categorize the inventory items based on multiple criteria and multiple objectives such as reducing the total relevant cost, reducing the safety stock inventory cost and maximizing the inventory turnover ratios. To the best of our knowledge the inventory items have never been categorized based on these objectives simultaneously. Hence, the MOGA is used to determine a set of non dominated solutions (the Pareto front). We have also used the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the best solution among the Pareto front. To test the performance of the proposed hybrid MOGA-TOPSIS approach with respect to some others ABC inventory classification models, a well known benchmark data set of 47 items from the literature is used.
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