Weight Vector Generation in Multi-Criteria Decision-Making with Basic Uncertain Information - Ya-Qiang Xu; Le-Sheng Jin; Zhen-Song Chen; Ronald R. Yager; Jana Špirková; Martin Kalina; Surajit Borkotokey (Mathematics, 2022)
This paper elaborates the different methods to generate normalized weight vector in multi-criteria decision-making where the given information of both criteria and inputs are uncertain and can be expressed by basic uncertain information. Some general weight allocation paradigms are proposed in view of their convenience in expression. In multi-criteria decision-making, the given importance for each considered criterion may have different extents of uncertainty. Accordingly, we propose some special induced weight-allocation methods. The inputs can be also associated with varying uncertainty extents, and then we develop several induced weight-generation methods for consideration. In addition, we present some suggested and prescriptive weight allocation rules and analyze their reasonability.