Supplier selection using a new russell model in the presence of undesirable outputs and stochastic data

Supplier selection is one of the significant topics in Supply Chain Management (SCM). One of the techniques tli{dotless}at can be used for selecting suppliers is Data Envelopment Analysis (DEA). In this study, to handle uncertainty in supplier selection problem, a new Russell model in the presence of undesirable outputs and stochastic data is developed. This study proposed a deterministle equivalent of the stochastic model and convert this deterministle problem into a quadratic programming problem. This quadratic programming problem is then solved using algorithms available for this elass of problems. A numerical example is presented to demonstrate the applicability of the proposed approach.