DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making

This paper develops a comprehensive algorithm for multi-expert multi-criteria decision making problems considering quantitative and qualitative criteria in forms of benefit, cost or target types. We focus on using probabilistic linguistic term sets to express the qualitative evaluations due to their excellence in expressing complex individual and collective linguistic assessments. Firstly, we develop a target-based linear normalization technique and a target-based vector normalization technique. A weight adjustment method is proposed to achieve the tradeoff between criteria after normalization. Given that the two target-based normalization techniques have different advantages, we then propose a ranking method, which consists three subordinate models, based on these two target-based normalization approaches and three aggregation techniques. Reliable results of a multi-expert multi-criteria decision making problem are determined by integrating the subordinate utility values and the ranks of alternatives. The proposed method is implemented to solve the green enterprise ranking problems and the excavation scheme selection problem for shallow buried tunnels, respectively. The advantages of the proposed method are emphasized through comparative analyses with other ranking methods.