ELECTRE II method to deal with probabilistic linguistic term sets and its application to edge computing

The edge node selection problem in edge computing is a typical multi-criteria group decision-making problem. In this paper, we put forward an ELECTRE II method with the probabilistic linguistic information to handle the edge node selection problem. First, a novel distance measure is developed for probabilistic linguistic term sets (PLTSs) and an entropy measure is devised to measure the uncertainty degree of PLTSs. Based on the score value and entropy, a novel method is put forward to compare two PLTSs. Next, a weight-determining method for criteria based on multiple correlation coefficient and a weight-determining method for experts based on entropy theory are proposed. After that, a novel probabilistic linguistic ELECTRE II method is put forward to deal with the edge node selection problem. Comparison with previous methods is provided to verify the superiority of our method.