Multicriteria ABC Inventory Classification Using the Social Choice Theory

Abstract

The multicriteria ABC inventory classification has been widely adopted by organizations for the purpose of specifying, monitoring, and controlling inventory efficiently. It categorizes the items into three groups based on some certain criteria, such as inventory cost, part criticality, lead time, and commonality. There has been extensive research on such a problem, but few have considered that the judgments about criteria’s importance order usually exhibit a substantial degree of variability. In light of this, we propose a new methodology for handling the multicriteria ABC inventory classification problem using the social choice theory. Specifically, the pessimistic and optimistic results for all possible individual judgments are obtained in a closed-form manner, which are then balanced by the Hurwicz criterion with a “coefficient of optimism”. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is used to aggregate the individual judgments into a collective choice, according to which the items are classified into Groups A, B, and C. Through a numerical experiment, we show that the proposed methodology not only considers all possible preferences among the criteria, but also generates flexible classification schemes.

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

“…Metaheuristic techniques such as Simulated Annealing (Mohammaditabar et al , 2012) and GA (Kaabi et al , 2018) have also been proposed for ABC inventory classification by illustrating the experiment with the same dataset. Other approaches to address MCIC using Shannon Entropy (Kheybari et al , 2019; Zheng et al , 2017), MOORA (Mallick et al , 2017) and social choice theory (Liu and Ma, 2020) have also adopted the same dataset for illustrative purpose. In this section, the proposed PSO approach is applied to the same inventory dataset used by Flores et al (1992) for comparison.…”

“…The study claims that the offered model can consider qualitative and quantitative criteria to the extent possible by simultaneously satisfying Pareto's law. A recent study (Liu and Ma, 2020) offered MCIC methodology using social choice theory wherein all possible individual judgments are examined by Hurwicz's criterion and are aggregated by Criteria Importance Through Intercriteria Correlation (CRITIC) method.…”

The Assignment Problem in Human Resource Project Management under Uncertainty

“…These parameters enable us to describe the decision maker's (project manager's) nature, i.e., his/her expectations, predictions, attitude towards risk, and his/her state of mind/soul. These parameters have already been applied to other issues in numerous papers (Ciullo et al 2019;Ellsberg 1961Ellsberg , 2001Fuchs et al 2019;Hernández et al 2018;Hurwicz 1952;Jagodziński 2014;Liu and Ma 2020;Perez et al 2015). Hence, the gap identified in the literature consists of the lack of methods designed for AP under uncertainty, which would be easy to use for managers, -would not require such data that are too difficult to estimate in the case of projects with a high degree of novelty and a fast-evolving environment (like fuzzy numbers, probabilities), -would be useful for any kind of decision-maker (pessimists, moderate decisionmakers, and optimists)…”

The Feasibility and Operational Performance of Implementation Median U-turn Intersections: A CRITIC Method

“…The Criteria Importance Through Intercriteria Correlation (CRITIC) method is one of the MCDM can be used to determine the weights of various criteria in the context of evaluating and selecting the best intersection design. This method eliminates the need for pairwise attribute comparisons and reduces the assessment process's reliance on decision makers (Gaur et al, 2022;Liu and Ma, 2019;Sujana et al, 2022;Wang et al, 2019). The CRITIC method is a subset of Multiple Criteria Decision Making (MCDM) that employs objective weighting against criteria to provide a comprehensive decision-making approach (Sujana et al, 2022).…”