A novel Multiple Attribute Decision Making approach based on interval data using U2P-Miner algorithm

Abstract

This paper aims to introduce a technique for order of preference using pattern mining based on Decision Makers (DMs) level of risk aversion. However, the model is essentially defined on the problem of supplier selection, it can be used to deal with almost any similar decision making problem. This novel Multiple Attribute Decision Making (MADM) model takes the advantages of the U2P-Miner algorithm, the interval data weighting method, and the Linear Assignment Method (LAM). The key idea behind the method is to consider the attribute with more frequent patterns as the common attribute and to assign a smaller weight to it. Since, the model handles interval data as input, it can be guaranteed that the model uses the detailed information and, therefore, the resulting weight factors are more realistic. The DMs risk aversion level is also addressed in the model, which is necessary in real-life situations. Accordingly, the proposed decision making process depends directly on DMs attitude toward risk. It gives DM the opportunity to make a decision in two ways: 1) based on the specified risk aversion level, 2) based on an integrated approach using LAM. The linearity of the LAM, by itself, enhances the scalability of the model. Moreover, the necessity of providing pairwise comparison judgments is completely eliminated in the model and, therefore, the reliability of the decision making is enhanced. The effectiveness of the model is finally demonstrated through a numerical example while the broad comparative and sensitivity analysis further proves its validity and superiority.