Fuzzy analytic hierarchy process with ordered pair of normalized real numbers

Abstract

The Analytic hierarchy process (AHP) is a widely used multi-criteria decision theory, and most AHP relies on the judgments of experts to derive priority scales. However, the judgment of experts may be subjective, and different experts may give different judgments for a problem. In order to make the decision results more objective, machine learning algorithms can be used to make the judgment. However, machine learning algorithms are strongly related to the collected data and not being flexible enough. This paper tries to combine expert judgments with algorithmic judgments to improve the bias of experts' judgments while still making decision-making flexibility. The authors combine expert's judgments with the judgments of the machine learning algorithm into Ordered pair of normalized real numbers (OPNs) and then make decisions through OPNs. Experiments on real data sets show that the proposed algorithm can get reasonable decision results. Moreover, when the expert's judgments are wrong or invalid, the judgments given by the machine learning algorithm can correct the expert's judgments to obtain a reasonable decision-making result.