A Genetic Algorithm-Based Classification Approach for Multicriteria ABC Analysis

ABC analysis is a widespread classification technique designed to manage inventory items in an effective way by relaxing controls on low valued items and applying more rigorous controls on high valued items. In the literature, many classification models issued from different methodologies such as Mathematical Programming (MP), Metaheuristics, Artificial Intelligence (AI) and Multicriteria Decision Making (MCDM) are proposed to perform the ABC inventory classification. To the best of our knowledge, the cross-fertilization of classification models issued from different methodologies is rarely tackled in the literature. This paper proposes some hybrid classification models based on both Genetic Algorithm (Metaheuristics) and two MCDM methods (Weighted Sum (WS) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)) to carry out the ABC inventory classification. To test the performance of the proposed classification models with respect to some existing models, a benchmark dataset from a Hospital Respiratory Therapy Unit (HRTU) is used. The computational results show that our proposed models outperformed the existing classification models according to some inventory performance measures. An additional performance analysis has also shown the effectiveness of our proposed models in inventory management.