基于可调多粒度对偶犹豫模糊概率粗糙集的三支多属性群决策

Three-way Multi-attribute Group Decision Making Based on Adjustable Multigranulation Dual Hesitant Fuzzy Probabilistic Rough Sets

本文为减少三支决策中主观性因素对决策结果的影响,并克服经典多粒度粗糙集模型在信息融合方面存在极端性的局限,在对偶犹豫模糊信息系统中探索了基于可调多粒度概率粗糙集的三支决策模型与方法,用于求解多属性群决策问题。首先,本文将对偶犹豫模糊的概念引入三支决策中,提出了可调多粒度对偶犹豫模糊概率粗糙集模型。然后,本文依据离差最大化法计算属性权重与专家权重,进一步利用多粒度三支决策框架建立了对偶犹豫模糊多属性群决策方法。最后,通过医学诊断的实例验证了本文所建立方法的可行性与有效性。

In order to reduce the influence of subjective factors on decision making results in three-way decisions, and overcome the extremeness in information fusion of classic multigranulation rough set models, this paper explores three-way decision making models and methods based on adjustable multigranulation probabilistic rough sets for solving multi-attribute group decision making problems in dual hesitant fuzzy information systems. First, the paper introduces the concept of dual hesitant fuzziness into three-way decisions, and puts forward adjustable multigranulation dual hesitant fuzzy probabilistic rough set models. Then, the paper calculates attribute weights and expert weights in light of the maximum deviation method, and constructs a dual hesitant fuzzy multi-attribute group decision making method via the multi-granularity three-way decision framework. Finally, the feasibility and the effectiveness of the constructed method are verified by a medical diagnosis example.