Comparative study of MCDM methods under different levels of uncertainty

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.