A Genetic Algorithm-Based Classification Approach for Multicriteria ABC Analysis

Abstract

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.

A Scatter Search Algorithm for Multi-Criteria Inventory Classification considering Multi-Objective Optimization

“…In this situation, we developed a model for maximizing satisfaction levels to provide convenience to decisionmakers due to having two objective functions. The Total Relevant Cost (TRC), which is one of the objective functions of our algorithm used in studies by Mohammaditabar et al (2012), Ramanathan (2006), and Kaabi et al (2018) to minimize inventory costs. However, materials with more than one criterion are effective in classification (Cohen, M. A., Ernst, 1988;B.…”

“…The ADU, ACU and LT criteria weights used for classification were taken as (0.407, 0.037, 0.556) from the study by Kaabi et al, (2015). We compare our model with other MCIC studies in the literature (Traditional (ADU); (Douissa & Jabeur, 2019;Kaabi et al, 2015;Kaabi et al, 2018;Lolli et al, 2014;Mohammaditabar et al, 2012;Zhang et al, 2018) that is used to benchmark dataset by Reid (1987) and also evalute the TRC functions. To operate the model, firstly the MINLP model was programed with Lingo 2018 and tested with small size problems.…”

“…They found classifications achieving inventory cost with variable holding cost (hi=0.2 and si=0.5) and ordering cost levels $1232.30. Kaabi, Jabeur, and Ladhari (2018) used GA to decide on criteria weights and weighted sum (WS) and TOPSIS methods to find the weighted score of items and developed a hybrid model. In their study, they made decisions according to two performance values.…”

ABC analysis using Particle Swarm Optimization and its performance evaluation with other models

“…Cost and dissimilarity index (dissimilarity of items classified in the same groups) are used as the objective function, and results are compared with other models. In the year 2018, the literature (Kaabi et al , 2018) presented a hybrid methodology combining GA with MCDM methods, namely TOPSIS and Weighted Sum (WS). The study used two performance measures, TRC and ITR, and thus provided four classifications models, two for each performance measures.…”

“…A few studies on MCIC incorporated quantitative as well as qualitative criteria in the model to arrive weights of criteria for classification (Hatefi et al, 2014;Kheybari et al, 2019;Torabi et al, 2012). The criteria weights have also been arrived by using well-known metaheuristics like Genetic Algorithm (GA) (Altay Guvenir and Erel, 1998;Kaabi et al, 2018), Particle Swarm Optimization (PSO) (Tsai and Yeh, 2008) and Simulated Annealing (Liu et al, 2016;Mohammaditabar et al, 2012). However, in the event of making an effective inventory control system, one cannot overlook the performance of inventory classification toward cost-effectiveness.…”