IFS/ER-based large-scale multiattribute group decision-making method by considering expert knowledge structure

The large-scale multiattribute group decision-making (LMGDM) consisting of at least 20 experts has been widely popular in recent years. Although lots of efforts have been spent on improving the LMGDM, the subjective factors such as experts' domain knowledges and bounded rationalities are still not well considered. This study focuses on providing a new LMGDM method by considering expert knowledge structure. An information extraction mechanism providing three kinds of inference ways including singleton attribute inference, local integral inference and global integral inference is introduced to ensure the assessments made by each expert with interval-valued intuitionistic fuzzy values (IVIFV) to be valid. Then a transformation is introduced to derive interval-valued basic probability assignment (BPA) function from the IVIFV, based on which expert reliability and attribute weight can be both reflected by evidential reasoning (ER) discounting. A pair of nonlinear optimization models that are extended by the analytical ER rule are established to make attribute fusion for expert and group fusion for alternative. An algorithm is summarized to solve the LMGDM problems by considering expert knowledge structure. Finally, an illustrative example as well as discussions is provided to demonstrate the applicability of the proposed method and algorithm.