constrained clustering

Due to the complex and multifaceted nature of supply chain resilience, hasn't yet been proposed a comprehensive, concrete, and aggregative model that includes the prevailing consensus of researchers in this field. In order to try to achieve that, the present study was conducted with the aim of forming a comprehensive model for supply chain resilience assessment using an integrated approach based on scientometrics and various artificial intelligence methods based on knowledge extraction from the text. The statistical population includes all indexed articles related to supply chain resilience from 2002 to 2020 in the two scientific databases Scopus and WOS. During the three stages of document refinement with a systematic review approach, scientometric information, and the full text of 346 articles were extracted and used in the analysis process. Utilizing an integrated approach based on the fusion of scientometrics of related metadata, and artificial intelligence tools to extraction of supply chain resilience assessment tool obtain the main innovation of this research which makes feasible establishing an evaluation model without interfering with researcher source biases. Finally, the supply chain resilience evaluation model including 4 main structures and 25 sub-structures was extracted from related scientific documents.