Multi-objective optimization model for risk assessment in the supply chain of a closed close loop under uncertainty conditions in parameters: Using a Constrained Risk Value Approach (CVaR)

In this research, a model for a stable closed loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Random programming has been used for modeling this problem and also using the scale of value Exposure to conditional risk is measured by risk. The purpose of this model is to maximize network design benefits, reduce unemployment and increase job opportunities resulting from the construction of facilities and minimize the production of carbon produced through intranets, production centers, recycling, repair, re-production. Other goals include minimizing the risk posed by uncertainty in transportation costs and customer demand. In the end, in order to demonstrate the efficiency of the model, an example is solved with certainty and uncertainty with the risk measurement criterion, and the pareto optimal solutions are compared. The results of this study indicate that, with increasing risk, the profit from the supply chain network has decreased and should be costlier to face the risk.