Intelligent Method and Algorithm for Multi-attribute Decision-Making under Linguistic Setting Based on Risk-Weighted

Abstract

Decision Suppose System is playing an important role in computer science, technology and engineering, while intelligent decision-making is one of the current hotspots. Intelligent decision-making methods and their algorithms are one of the most important basics and key cores in intelligent information processing, intelligent pervasive computing and so on. In view of multi-attribute decision-making under linguistic setting, propose one new decision method. Firstly construct a range pole plan and introduce the policy-maker risk-preference weight because the attributes¿ measure-value are uncertainty. Then with three tuples (Limit low similarity, Risk degree, Risk preference value) reflect the risk-degree existing in the decision-making process. Then construct the risk-weighted similarity measure operator (RWSMO) to measure the risk balance similarity's size between each of decision schemes and the range pole plan.