A q-Rung Orthopair Cloud-Based Multi-Attribute Decision-Making Algorithm: Considering the Information Error and Multilayer Heterogeneous Relationship of Attributes

• Main Authors: Ping He, Chaojun Li, Harish Garg, Jian Liu, Zaoli Yang, Xudong Guo
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: q-rung orthopair cloud; interaction operator; Maclaurin symmetric mean operator; information error; multilayer heterogeneous relationship
• Online Access: https://ieeexplore.ieee.org/document/9541401/

The representation and aggregation of attribute information is the key to address the multi-attribute decision-making (MADM) problems. Based on the error problem of attribute information given by experts and the heterogeneous relationship between attributes, a new MADM method based on q-rung orthopair cloud interaction weighted Maclaurin symmetric mean (q-ROCIWMSM) operator is proposed. In it, this method firstly considers the randomness of evaluation information given by experts, integrates the q-rung orthopair fuzzy (q-ROF) set and cloud model and define the new concept of q-rung orthopair cloud (q-ROC), so as to betray the error distribution characteristics of membership and non-membership information caused by randomness. Then, to investigate the multi-layer heterogeneous relationship among membership functions and different quantitative attributes, the interaction operator and Maclaurin symmetric mean (MSM) operator are introduced into q-ROC information, and the q-ROCIWMSM operator is proposed to aggregate q-ROC information with multi-dimensional parameter characteristics. Thirdly, a framework of MADM based on q-ROCIWMSM operator is established. Finally, a cross-border e-commerce consumption decision-making case is used to test the effectiveness of the proposed method. At the same time, robustness analysis and method comparison analysis further show the advantages of our approach. The study results show that the proposed method can accurately describe the error distribution characteristics of attribute information and eliminate the adverse effects of extreme values in the evaluation information on the decision-making results. In addition, the method proposed in this paper can flexibly reflect the multi-layer heterogeneous relationship between attributes, and has strong flexibility and applicability.