Comparative study of MCDM methods under different levels of uncertainty

Akshay Hinduja and Manju Pandey

International Journal of Information and Decision Sciences, 2021, vol. 13, issue 1, 16-41

Abstract: Often, data in MCDM problems are imprecise and changeable due to the mandatory participation of human judgement, which is often unclear and vague. Hence, the selection of an appropriate MCDM method is crucial to the optimal decision-making. All the MCDM methods are heavily affected by individual or group preferences and therefore even a small change in the data can cause rank-reversal. With the regular proliferation of such methods and their modifications, it is important to carry out a comparative study that provides comprehensive insight into their performances under uncertain conditions. In this paper, we use the Monte Carlo simulation approach to empirically compare the results of five well-known and widely applied MCDM methods, WSM, WPM, TOPSIS, GRA, and MULTIMOORA under different levels of uncertainty. The findings of this paper will assist decision-makers in the selection of most robust and reliable MCDM methods for different decision scenarios. The results of this research are significant additions to the current repository of knowledge in the multi-criteria decision analysis as well as the literature pertaining to the information systems. It also provides insights for many managerial applications of these MCDM methods.

Keywords: multi-criteria decision-making; MCDM; comparative analysis; decision sciences; MCDM methods; Monte Carlo simulation; rank-reversal; rank-correlation; uncertainty. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=113598 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijidsc:v:13:y:2021:i:1:p:16-41

Access Statistics for this article

More articles in International Journal of Information and Decision Sciences from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().