A NOVEL SUPPLIER SELECTION APPROACH BASED ON EXTENDED DATA ENVELOPMENT ANALYSIS UNDER A HESITANT FUZZY LINGUISTIC ENVIRONMENT

Supplier selection is a core supply chain issue. Choosing suitable suppliers will directly affect the success and sustainable development of the overall supply chain. Further, the assessment criteria of supplier selection include qualitative and quantitative assessment factors simultaneously. Thus, experts may give assessment criteria scores that include hesitant fuzzy information or incomplete information. These factors make the problem of choosing the right suppliers more complicated. The traditional average value approach and the traditional data envelopment analysis (DEA) method can only handle complete assessment criteria score information given by experts. They cannot simultaneously handle complete information, incomplete information, and hesitant information in the supplier selection process. In order to further handle this issue, this paper proposed a novel supplier selection approach based on extended DEA under a hesitant fuzzy linguistic (HFL) environment. The innovation of the proposed method lies in its capacity to simultaneously process complete information, incomplete information, and hesitant information in the supplier selection process. Furthermore, it can effectively solve a high number of duplicated DEA values of 1 for the DEA method. An illustrative example of consultant company selection was used to verify the rationality and correctness of the proposed approach. This study also compares the simulation results of the traditional average value method and the DEA method with those achieved using the proposed approach. The numerical test results show that the proposed approach can handle the above supplier selection issues under an HFL environment