## 2 Literature Review  
Supplier selection is a hot issue of common concern for both the academic circles and enterprises nowadays, which can be viewed as a common and classical MCDM problem [27]. The ultimate purpose is to pick the optimal supplier among numerous alternatives, and the reliability of the selected supplier is influenced by the rest of the supply chain [28]. Over the past few years, the existing literature concerning the problem of supplier selection has been rich in studies, which presents us fruitful individual and comprehensive methods for tackling this issue, as well as systematically summarizing the previous studies in many review papers. Some scholars have focused on modeling such problems by an individual technique. From the AHP viewpoint, [PERSON] et al. [27] extended the original AHP to D numbers context for the supplier selection by taking negative impact of DMs' subjective judgment on selection results into consideration, which can adequately handle the issue of imprecision and incompleteness. Overall, we obtained from the existing research that the application of AHP under complicated environment in the supplier selection provides the DMs with the confidence of consistency throughout the decision process. From the point view of PROMETHEE, as a traditional ranking technique, various researchers have worked on the PROMETHEE to effectively tackle the supplier selection in diverse fields. For instance, by attaching importance to DMs' personal choice regarding each alternative, the IFS-PROMETHEE was developed by [PERSON] et al. [29]. The framework can be divided into two steps, including utilizing the LBA operator to directly aggregate experts' preference scores, as well as using the PROMETHE with IFS information to rank the available suppliers. In addition, [PERSON] et al. [30] provided us with a fuzzy PROMETHEE technique to deal with supplier selection, the predefined preference functions of which were handled based on fuzzy distances between feasible alternatives. In addition, TOPSIS method was utilized to select the most satisfied supplier firm under probabilistic linguistic context [18]. All in all, diversiform methods were proposed and applied in dealing with supplier selection with different information [24]. Nevertheless, the above literature almost all utilized the subjective weights of each criterion provided by DMs, that is, the criteria weights were assigned directly. This case may result in the unreasonable and inaccurate ranking results for selecting the most satisfied suppliers. As a solution to this issue, several researchers have worked on the valid calculation process of determining the criteria weights by proposing the integrated methods [31]. A supplier selection framework was constructed for SCM by [PERSON] et al. [21], AHP and GRA were integrated to calculate the criteria weights and further rank the available suppliers respectively. [PERSON] et al. [32] weighed the criteria by means of fuzzy AHP, and then presented a combined technique namely fuzzy AHP and TOPSIS to handle a supplier selection issues in an automobile company. To mention supplier selection in the agricultural industry, the green agricultural product supplier selection is regarded as a crucial link in the operation process of retailers and also provides one of the great concerns to researchers in academic and applied fields. It is exactly because of the significance of supplier selection of green agricultural products, and to ensure the safety of agricultural products and foods simultaneously, that major production, circulation, and retail enterprises attach importance to the control of the front end of the supply chain [22]. In other words, the increasingly perfected requirements are put forward to the green agricultural product supplier, mainly including the following perspectives [31]: (1) quality safety of green products, (2) performance of suppliers, (3) environmental protection, (4) continuous improvement, and (5) social responsibility.