Some Methods for Yager Preference Involved Aggregations in Multi-Criteria and Multi-Sources Evaluation

Abstract

OWA operators and related aggregation techniques generally focus on input vector with a linear ordering. However, in commonly faced multi-criteria and multi-sources evaluation and decision making, the inputs involved form an evaluation matrix. Considering the fact that the data under evaluation are all with two dimensional meanings, this study explores and proposes four novel preference involved aggregation techniques by using RIM quantifiers and OWA operators. The first two models are both with two steps to carry out the aggregation processes, with one using two times of OWA operators and another considering evaluation matrix as a vector lattice. The last two models come from a whole perspective to direct the aggregation processes, with one arising from a global magnitude view and another based on staggered ordering using two specially defined collections of permutations. Illustrative examples and remarks are also spotted immediately following the proposed models or at suitable positions.