A dynamic multi-attribute group decision-making method with R-numbers based on MEREC and CoCoSo method

Abstract

AbstractDynamic multi-attribute group decision-making (DMAGDM) is a widespread practice in which evaluations are provided by multiple decision-makers at various times and early evaluations impact later evaluations. Additionally, attributes and alternatives can be added or removed over time. An R-numbers DMAGDM method is developed based on the advantages of R-numbers in capturing risks. This paper introduces the R-numbers Einstein weighted averaging (RNEWA) operator and R-numbers weighted Einstein geometric (RNEWG) operator, which are distinct from conventional algebraic operations, and examines their properties. Moreover, an expert weight determination model is constructed using the similarity measure of R-numbers. The attribute weight determination model in the R-numbers environment is also proposed with the method based on the criteria removal effects method (MEREC). A static rating calculation model, which utilizes the combination compromise solution (CoCoSo) method in the R-numbers environment, is built using the RNEWA operator and RNEWG operator. Furthermore, a new dynamic rating calculation model is proposed which does not require storage of all decision information over time. Finally, the applicability and effectiveness of the R-numbers DMAGDM method is demonstrated through a case study on supply chain risk assessment of manufacturing enterprises.