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Vendor selection is an important issue in most company based on many criteria that includes ambiguous or uncertain data. Therefore in the study, it is essential that fuzzy approach is employed for coping with the uncertainty and achieving more accurate results. In other hand, the relationships between criteria and sub-criteria are complex; for encompassing the complexity, most conventional decision models cannot help us explain the interrelationships among the criteria. In this paper, a hybrid multi-criteria decision making (MCDM) technique is proposed to determine the structural relationships and the interrelationships among all the evaluation's dimensions based the Analytic Network Process (ANP) method determining appropriate weightings to each sub-criterion. Then alternatives priority should be determined which can aid the decision making. For the purpose, The TOPSIS (technique for order performance by similarity to idea solution) is used to rank all competing alternatives in terms of their overall performances. In MCDM studies and research, applying TOPSIS in ranking alternatives has recently been customary because of its advantages. In the end, a case study of an Iranian company, in automotive industry, is demonstrated to illustrate the proposed model can improve solving of vendor selection problem.

Sourcing resilience has become a primary concern in most closed-loop supply chains (CLSC).Companies face the option of sourcing their raw materials from suppliers or recycling centers though the latter can be disrupted sometimes. In this study, a multi-stage, stochastic programming (MSSP) model is developed to analyze how a company can proactively employ sourcing strategies along with pricing policies to enhance sourcing resilience in a CLSC, where the return of end-of-life (used) products into recycling centers is stochastic and sensitive to the purchasing price. The stochastic return is modelled using a scenario-tree-based approach. Since the sample average approximation algorithm (SAA) in scenario generation can lead to an increased number of scenarios and make the model hard to solve, a backward scenario reduction algorithm is employed to efficiently reduce the problem size. The findings indicate that an effective pricing policy can help determine the resilient sourcing strategy in the CLSC network design problem and, therefore, maximize the total profit and mitigate the disruption risks.