A dual-level multi-attribute group decision-making model considering interaction factors based on CCSD and MARCOS methods with R-numbers

Abstract

The current body of research on multi-attribute group decision-making (MAGDM) exhibits some limitations, namely the utilization of the single attribute hierarchy and the assumption of attribute independence. This paper presents a novel approach, referred to as the R-DLMAGDM (R-numbers Dual-Level Multi-Attribute Group Decision-Making) model with interaction factors, which integrates the advantages of R-numbers in risk evaluation. The proposed model aims to address the limitations related to attribute hierarchies and the assumption of attribute independence. The first step in the new model involves establishing the entropy model with R-numbers to determine the expert weights. Subsequently, the R-numbers generalized weighted arithmetic average (RNGWAA) operator and the R-numbers generalized weighted geometric average (RNGWGA) operator are introduced to combine the information provided by the experts. Next, the application of the enhanced correlation coefficient and standard deviation (CCSD) method is utilized to ascertain the relative weights of the dual-level attributes using the inverse order concept. Then, the fuzzy cognitive map (FCM) is employed to evaluate the interrelationships among components in order to derive the final attribute weights. Additionally, the paper discusses the implementation of the R-DLMAGDM model for the assessment of risk in a virtual supply chain in the metaverse. The evaluation process entails the prioritizing of five different alternatives based on four Level 1 criteria and seven Level 2 criteria. The model’s flexibility and validity are showcased through the execution of comprehensive sensitivity analysis and three-dimensional comparative analysis.