Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions

Abstract

With the increasing complexity of decision-making problems and environment, the integration and fusion of three-way decisions, rough set and multi-attribute group decision-making (MAGDM) have become a major trend in the field of decision analysis. Although many researchers have presented various MAGDM methods under different environments, there are still some imperfections, such as the weight information is not comprehensive or flexible enough, the decision results lack interpretability, and the impact of risk attitude is not fully taken into account. In order to overcome the above shortcomings and improve the scientificity and rationality of decision-making, a novel data-driven MAGDM method under interval-valued intuitionistic uncertain linguistic environment is established based on the idea of multi-granulation and three-way decisions. Our contributions can be identified as follows: (1) The multi-granulation weight mining and fusion methods for experts and attributes are proposed, respectively; (2) The coarse-granulation grading information based on three-way decisions is developed to enhance the interpretability and reference value of decision results; (3) The expected value with risk attitude factor is defined to compare interval-valued intuitionistic uncertain linguistic variables (IVIULVs) and then is used to grade and rank alternatives under different risk attitudes. To illustrate the feasibility and practicality of the proposed method, a case of logistics supplier selection in e-commerce enterprises is demonstrated. Furthermore, the advantages and characteristics of the proposed method are highlighted via detailed comparison and thorough analysis.