Multiple-Criteria Decision Analysis: A Novel Rank Aggregation Method

Ranking among several objects is a very crucial operation for different applications to find a vote value for each object against the others. Multiple metrics can be combined to get a single vote value of an object. There are many studies in the literature that convert the ranking problem into a graph structure to solve it with a discrete mathematical process. Generally, these studies define multiple metrics as matrix forms and then relate them with the computations of eigenvectors to find the best ranked object. However, due to the dynamic nature of the metric values, ranking approaches should be fast and less complex. In this study a different approach for the ranking process with multiple metrics is proposed. This approach is fast and easy to implement. In order to test the approach, a network scenario is designed with computer programs. The experimental results show that this method outperforms a common conventional method in terms of various metric values, namely transmission time, packet loss rate, jitter, availability, and throughput. As a consequence, the proposed method gives the average value of each individual metric as more advantageous and without rescaling the numerical values.