Similarity Analysis of Methods for Objective Determination of Weights in Multi-Criteria Decision Support Systems

Bartosz Paradowski,Wojciech Sałabun,Bartłomiej Kizielewicz,Aleksandra Bączkiewicz,Andrii Shekhovtsov

doi:10.3390/sym13101874

Bartosz Paradowski, Wojciech Sałabun + Show 3 more

Open Access

https://doi.org/10.3390/sym13101874

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Abstract

Decision support systems (DSS) are currently developing rapidly and are increasingly used in various fields. More often, those systems are inseparable from information-based systems and computer systems. Therefore, from a methodical point of view, the algorithms implemented in the DSS play a critical role. In this aspect, multi-criteria decision support (MCDA) methods are widely used. As research progresses, many MCDA methods and algorithms for the objective identification of the significance of individual criteria of the MCDA models were developed. In this paper, an analysis of available objective methods for criteria weighting is presented. Additionally, the authors presented the implementation of the system that provides easy and accessible weight calculations for any decision matrix with the possibility of comparing results of different weighting methods. The results of weighting methods were compared using carefully selected similarity coefficients to emphasise the correlation of the resulting weights. The performed research shows that every method should provide distinctive weights considering input data, emphasising the importance of choosing the correct method for a given multi-criteria decision support model and DSS.

Highlights

The continuous development of digitalization causes information-based systems to become an integral part of many companies and provide helpful software for application in various domains
Two of the presented techniques calculate the correlation of raw values, while the other two focus on ranking weights and compare them in terms of criterion importance provided by the weighting method
It ensures a typical ranking scenario where the three first places are the most significant. It targets differences in the provided rankings depending on which positions changes were noticed. This coefficient is used in many decision problems, such as evaluating the convergence of the rankings provided by the multi-criteria decision analysis (MCDA) methods, the sensitivity analysis of the resulting rankings, and the compromise ranking procedure to obtain a reliable solution from the results provided by different MCDA methods [10,69]

Summary

Introduction

The continuous development of digitalization causes information-based systems to become an integral part of many companies and provide helpful software for application in various domains. The mentioned tools are more and more prominently used among numerous fields, for instance, sustainable energy development [1,2,3,4], business [5,6], scientific [7] or information processing [8]. Such decision support systems have been widely used for many years. The DSSs that make up information management systems are constantly being improved and developed [9]. Decision support systems were created for supply e-commerce [10], chain improvement [11], new employee recruitment [12], human resources [13], accounting support [14] or even for water resource management [15,16]

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