A new comprehensive possibilistic group decision approach for resilient supplier selection with mean–variance–skewness–kurtosis and asymmetric information under interval-valued fuzzy uncertainty

Since resilient supplier selection problems (RSSPs) are regarded vague, uncertain and complex, interval-valued fuzzy sets (IVFSs) and possibilistic statistical theories can assist to deal with preferences and experience of group of experts into meaningful results to appraise the potential suppliers. This research presents a novel comprehensive possibilistic statistical group decision approach with IVFSs and asymmetric information to solve RSSPs in the supply chain networks (SCNs). Possibilistic statistical concepts, including mean, variance, skewness and kurtosis, are proposed for the first time in the literature of SCNs for the group decision process. Also, asymmetric information with IVFSs is provided in the presented approach along with introducing two new extensions of weighting methods for experts as well as evaluation criteria. In addition, new relations, new separation measures and novel distinguish indices are introduced regarding to the preference by similarity to ideal solutions with mean–variance–skewness–kurtosis modeling. Then, the proposed comprehensive decision approach is implemented to an application in automobile industry for the RSSPs to assess the resilience strategy in the SCNs under uncertain conditions.