Title: Multiple criteria ABC classification: an accelerated hybrid ELECTRE-PSO method

Authors: Ezzatollah Asgharizadeh; Ehsan Yadegari; Fariba Salahi; Mahdi Homayounfar; Amir Daneshvar

Addresses: Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran ' Henry FAYOL Center, Department of Mathematical and Industrial Engineering, École des mines de Saint-Étienne, 158 cours Fauriel, 42023 Saint-Etienne cedex 2, France ' Department of Industrial Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran ' Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran ' Department of Information Technology Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran

Abstract: ABC classification analysis categorises inventory items into predefined classes namely A, B and C. The limitation of the ABC system is that only one criterion is considered, however, as emphasised in the literature, the inventory classification is multi-criteria problem. So, this paper proposed a multiple criteria ABC inventory classification (MCIC) method integrating ELECTRE TRI with particle swarm optimisation (PSO) algorithm. Since, the application of ELECTRE TRI method requires to determine the preferences of decision makers (DMs) as parameter values, the solution process is very complex and time-consuming especially in large-scale problems. Tackling these difficulties, all ELECTRE TRI parameters are inferred from training data through a procedure using hybrid PSO algorithm, for accelerating the PSO, the variable position (VP) model is also proposed as an exploitation and variable exploration. Finally, the model applied to six inventory datasets and the results revealed high applicability of the proposed model to inventory classification problems.

Keywords: inventory classification outranking relations; particle swarm optimisation; PSO; ELECTRE TRI.

DOI: 10.1504/IJIDS.2022.127458

International Journal of Information and Decision Sciences, 2022 Vol.14 No.4, pp.325 - 344

Received: 01 Dec 2019
Accepted: 29 Nov 2020
Published online: 06 Dec 2022 *

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