Analysis and Comparison of State-of-the-Art Fuzzy Multi-criteria Decision-making Methods Under Different Levels of Uncertainty

Abstract

Often, data in multi-criteria decision-making (MCDM) problems are imprecise and changeable due to the mandatory participation of human judgement, which is often unclear and vague. Besides, different MCDM methods may produce different results under different levels of uncertainty and require divergent levels of computational resources. Therefore, the quality of the decision and the amount of effort are heavily affected by selection of the MCDM method. 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 study, we use the randomized quasi-Monte Carlo simulation approach to compare empirically the results produced by 12 classic and contemporary fuzzy MCDM (FMCDM) approaches with rank-reversal perspective over increasing uncertainty in various decision scenarios. Furthermore, this study also investigates the similarity between ranks produced by each pair of methods for the same decision problems. The study further compares the results obtained by quasi-Monte Carlo simulation with the results obtained by Monte Carlo simulation. The findings of this study will assist decision-makers in the selection of most appropriate fuzzy MCDM approach 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.