Method for multiple attribute decision-making with continuous random variable under risk based on projection model

Abstract

A rank approach based on projection model is proposed to deal with multiple attribute decision-making[MADM] problems under risk and with attribute value as continuous random variable on bounded intervals. Firstly, risk decision matrix is normalized by density function, and weights of attributes are calculated based on exception value of random variable by using projection pursuit model and genetic algorithm. Next, through calculating weighted correlation coefficients between alternatives and ideal solutions, weighted grey correlation projection models on ideal solutions are developed by grey correlation projection method for every alternative. Furthermore, alternatives are ranked by grey correlation projection value. Finally, an MADM example with interval numbers is provided to demonstrate the steps and effectiveness of the proposed approach © Association for Scientific Research.