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.

中文翻译：

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Yager 偏好的一些方法涉及多标准和多源评估中的聚合

OWA 算子和相关的聚合技术通常侧重于线性排序的输入向量。然而，在通常面临的多标准和多源评估和决策中，所涉及的输入形成评估矩阵。考虑到被评估的数据都具有二维含义的事实，本研究通过使用RIM量词和OWA算子，探索并提出了四种新的涉及偏好的聚合技术。前两个模型都有两个步骤来执行聚合过程，一个使用两次 OWA 算子，另一个将评估矩阵视为向量格。最后两个模型从一个整体的角度来指导聚合过程，一个来自全局幅度视图，另一个基于使用两个特殊定义的排列集合的交错排序。说明性示例和注释也立即出现在建议的模型之后或适当的位置。