A Study of Different Distance Metrics in the TOPSIS Method

To improve the decision-making process, more and more systems are being developed based on a group of multi-criteria decision analysis (MCDA) methods. Each method is based on different approaches leading to a final result. It is possible to modify the default performance of these methods, but in this case, it is worth checking whether it affects the achieved results. In this paper, the technique for order preference by similarity to an ideal solution (TOPSIS) method was used to examine the chosen distance metric’s influence to obtained results. The Euclidean and Manhattan distances were compared, while obtained rankings were compared with the similarity coefficients to check their correlation. It shows that used distance metric has an impact on the results and they are significantly different.