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Selecting resilient-sustainable suppliers can improve sustainability status and reduce supply chain disruption. This study aims to design a model for selecting resilient-sustainable suppliers in the supply chain of the Shahid Ghandi Corporation Complex. For this purpose, after reviewing the theoretical literature, 76 and 50 indicators were identified for evaluating sustainable suppliers and resilient suppliers, respectively. These indicators were investigated by supply chain experts in Shahid Ghandi Corporation Complex and, then, 15 indicators were determined to be suitable for each of the sustainable and resilient suppliers. A questionnaire was distributed among the supply chain experts of Shahid Ghandi Corporation Complex and the resilient-sustainable supplier selection model was confirmed using confirmatory factor analysis (CFA) based on the 136 questionnaires gathered from the participants. Sustainability indicators were classified into three economic, social, and environmental dimensions, and resilience indicators were divided into three categories of absorptive capacity, adaptive capacity, and restorative capacity. The results showed that the economic dimension had the first rank, the environmental dimension the second rank, and the indices of adaptation capacity, restorative capacity, social capacity and absorption capacity in choosing the sustainable-resilient supplier model were the next priorities, respectively.

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Developing realistic models for the evaluation of sustainable supply chains has turned into a major challenge facing managers. The decision-making approaches proposed here consist of two stages. At the first stage, a dynamic-network data envelopment analysis (DNDEA) model is established for the first time, wherein the current efficiency of a business can be influenced by its prior social and environmental activities, as two main dimensions of sustainability. The second stage correspondingly presents, for the first time, a model in which total efficiency is calculated based on the value of historical data. Sensitivity analysis is exploited to determine the more effective factors of sustainability in efficiency evaluations. To validate the model, it is used to assess the sustainability of the suppliers of an auto spare parts manufacturer. The study results reveal that the model is well-able to evaluate the performance of dynamic network structures, with a very high discriminating power. Following the implementation of this model, only the supplier(KARAN) is found to reach the efficiency limit, and  SIRIN S.N. is recognized as the most inefficient supplier with an efficiency score of 0.6409. The sensitivity analysis outcomes demonstrate that the least amount of efficiency change is related to the economic pillar; however, the rising trend in wage costs, compared with other economic factors, brings a better effect on augmenting the efficiency of some inefficient suppliers. The highest efficiency changes during sensitivity analysis are further observed in both social and environmental dimensions. Therefore, it is claimed that investing in these two pillars can have a significant impact on the efficiency of suppliers.
 

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Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. First, suppliers' efficiency values in a supply chain are determined, using the dynamic network data envelopment analysis (DNDEA) model developed for this purpose. Then, a heuristic method is presented based on the fuzzy interpretive structural modeling (FISM) to find a common set of weights (CSWs) for the variables involved. Depending on the data conditions, two approaches, viz. centralized and decentralized, are proposed for efficiency measurement. To illustrate the model's capability, the proposed methodology is further applied to the real data of a company, named Nirou Moharekeh Industries (NMI). The results of a study on 12 suppliers within the DNDEA model accordingly reveal that one unit (i.e. KARAN) obtains an efficient value, but an inefficient score is observed in 11 units, whose technical efficiency value is in the range of 0.6409 to 0.9983. After utilizing the weights gained from the heuristic method, the efficiency value of the most inefficient supplier (that is, SIRINS.N.) dwindles from 0.6409 to 0.6319.

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Over the past few decades, there has been a growing need to address the limitations of the Data Envelopment Analysis methodology, particularly the issue of freely selecting weights. As a result, common weight models have emerged and expanded. This article aims to provide a comprehensive overview of CSW methods, analyzing papers and bibliometric information through a systematic literature review. In this study, a total of 116 articles on CSW published between 1991 and 2022 were carefully selected and reviewed. These contributions were categorized based on specific features related to the computational technique or the main purpose of the procedure. The findings revealed that uncertain models had the highest share among the articles in the field of CSW. Furthermore, the Journal of Expert Systems with Applications emerged as the leading journal in terms of the number of publications on CSW models in DEA. The analysis of the bibliometric information of the articles was carried out using advanced software tools, including R-Studio and VOS Viewer... This review offers valuable insights and discussion, which can guide future research endeavors in this field. By addressing the limitations of DEA and exploring various CSW methods, this study contributes to the advancement of knowledge and understanding in this area.