Sustainable supplier selection for smart supply chain considering internal and external uncertainty: An integrated rough-fuzzy approach - Chen, Zhihua; Ming, Xinguo; Zhou, Tongtong; Chang, Yuan (Applied soft computing, 2020)
This study proposes a novel framework to identify smart-sustainable SCMP (supply chain management practices) as supplier selection criteria for a smart supply chain. Supplier selection consists of two parts: criteria weights determination and suppliers ranking. DEMATEL (Decision Making Trial and Evaluation Laboratory) has been acknowledged as a relatively feasible method for determining the criteria weights due to its effectiveness in acquiring the interrelationships between criteria. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) has been identified as the most frequently used method for supplier ranking due to its superiority in quickly finding the best alternatives. However, most existing research contains scant study of the simultaneous manipulation of internal uncertainty (individual linguistic vagueness) and external uncertainty (group preference diversity), which are involved in the supplier selection process. Therefore, this study proposes a hybrid rough-fuzzy DEMATEL-TOPSIS approach to sustainable supplier selection for a smart supply chain. The proposed method combines the strength of the fuzzy set in handling internal uncertainty and the advantages of the rough set in manipulating external uncertainty. The effectiveness and accuracy of the proposed methodology are illustrated through its application in sustainable vehicle transmission supplier selection and through comparisons with other methods. •New topic on sustainable supplier selection for smart supply chain is put forward.•A novel rough-fuzzy TOPSIS-DEMATEL is proposed for sustainable supplier selection.•Internal and external uncertainties are manipulated by proposed rough-fuzzy numbers.•Decision uncertainty is measured through the presented rough-fuzzy polygons.•Showed the method’s feasibility with vehicle transmission supplier selection.