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ARTICLE

Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method)–Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm

Zoran Gligorić, Miloš Gligorić^*, Igor Miljanović, Suzana Lutovac, Aleksandar Milutinović

Faculty of Mining and Geology, University of Belgrade, Belgrade, 11 000, Serbia

* Corresponding Author: Miloš Gligorić. Email:

(This article belongs to the Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)

Computer Modeling in Engineering & Sciences 2023, 136(1), 955-979. https://doi.org/10.32604/cmes.2023.025021

Received 17 June 2022; Accepted 05 September 2022; Issue published 05 January 2023

Abstract

Information about the relative importance of each criterion or the weights of criteria can have a significant influence on the ultimate rank of alternatives. Accordingly, assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems. Three methods are commonly used for assessing the weights of criteria: objective, subjective, and integrated methods. In this study, an objective approach is proposed to assess the weights of criteria, called SPC method (Symmetry Point of Criterion). This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making. The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example. To validate the developed method, we used our numerical example and calculated the weights of criteria by CRITIC, Entropy, Standard Deviation and MEREC methods. Comparative analysis between these methods and the SPC method reveals that the developed method is a very reliable objective way to determine the weights of criteria. Additionally, in this study, we proposed the application of SPC method to evaluate the efficiency of the multi-criteria partitioning algorithm. The main idea of the evaluation is based on the following fact: the greater the uniformity of the weights of criteria, the higher the efficiency of the partitioning algorithm. The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.

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