A COPRAS Approach for Multi-Attribute Decision-Making Based on Pythagorean Triangular Fuzzy Linguistic Sugeno-Weber Aggregation Operators

针对属性权重未知且属性值为毕达哥拉斯三角模糊语言数的多属性决策问题，研究毕达哥拉斯三角模糊语言集的集成算子，考虑Sugeno-Weber范数在信息集成中的优势，提出一种基于毕达哥拉斯三角模糊语言Sugeno-Weber集成算子的COPRAS (COmplex PRoportional ASsessment)决策方法。首先，定义毕达哥拉斯三角模糊语言集的Sugeno-Weber运算法则，提出几种新型集成算子并讨论其性质。其次，建立基于离差最大化的权重模型确定属性权重。最后，基于所提算子构建改进的COPRAS方法并通过案例分析验证其有效性、实用性和可行性。所提出的毕达哥拉斯三角模糊语言Sugeno-Weber集成算子丰富了其集成算子理论。This study addresses multi-attribute decision-making problems where attribute weights are unknown and attribute values are expressed as Pythagorean Triangular fuzzy linguistic numbers. Focusing on aggregation operators for Pythagorean Triangular fuzzy linguistic sets, and leveraging the advantages of the Sugeno-Weber norms in information aggregation, we propose an improved COPRAS (COmplex PRoportional ASsessment) decision-making method based on novel Pythagorean Triangular fuzzy linguistic Sugeno-Weber aggregation operators. Firstly, the Sugeno-Weber operational laws for Pythagorean Triangular fuzzy linguistic sets are defined, and several new aggregation operators are proposed, along with a discussion of their properties. Secondly, a weight determination model based on maximum deviation is established to ascertain attribute weights. Finally, an improved COPRAS method is constructed utilizing the proposed operators, and its effectiveness, practicability, and feasibility are validated through a case study. The proposed Pythagorean Triangular fuzzy linguistic set Sugeno-Weber aggregation operators enrich the theory of aggregation operators.