The multicriteria group decision-making method in fuzzy environment has become the focus of many scholars. The purpose of this paper is to extend the traditional ORESTE (organísation, rangement et Synthèse de données relarionnelles) method to solve the multi-criteria group decision-making problem under IT2FS environment. The main contributions of this paper are to define a new similarity measure and a global preference score function based on the new similarity measure. First, a new similarity measure of two IT2FSs is defined. Then, some properties of the new similarity measure are investigated, and a comparison between the new similarity measure and other existing similarity measures is provided. Subsequently, a global preference score function is defined based on the new similarity measure. The average preference score is derived using the power average operator, and the weak order of the alternatives is obtained according to the average preference score. Furthermore, a PIR (P: preference; I: indifference; R: incomparability) structure is constructed to reach a strong ordering of alternatives. Finally, the feasibility and effectiveness of the proposed method is illustrated by a practical case of selecting the best supplier involving comparisons with the ELECTRE-based outranking method and IT2FS-TOPSIS method. And the new approach proposed in this paper can be applied to e-commerce, medical decision making, product recommendation, and fault diagnosis.

模糊环境下的多准则群决策方法已成为许多学者关注的焦点。本文的目的是扩展传统的ORESTE （组织，范围和Synthèsededonnéesrelarionnelles）IT2FS环境下解决多准则群体决策问题的方法。本文的主要贡献是定义新的相似性度量和基于新相似性度量的全局偏好得分函数。首先，定义了两个IT2FS的新相似性度量。然后，研究了新的相似性度量的一些性质，并提供了新的相似性度量与其他现有相似性度量之间的比较。随后，基于新的相似性度量定义全局偏好得分函数。使用幂平均算子得出平均偏好得分，并根据平均偏好得分获得备选方案的弱阶。此外，PIR（P：偏好； I：冷漠； R：不可比较性）结构，以实现替代方案的有序排列。最后，通过与基于ELECTRE的排名方法和IT2FS-TOPSIS方法进行比较的选择最佳供应商的实际案例，说明了该方法的可行性和有效性。并且本文提出的新方法可以应用于电子商务，医疗决策，产品推荐和故障诊断。