Reza Babazadeh - Department of industrial engineering, University ofTehran, Tehran, Iran
Masoud Rabbani

In the last decade, Reverse logistics, induced by various forms of return, because of their economic benefits and environmental legislation, increasinglyhas attracted. Reverse logistics network design is a major strategic issue. In this paper a two-stage scenario-based stochastic programming model for reverse logistic networkdesign is presented that uses the conditional Value-at-Risk (CVaR) criteria in its objective function to control the risklevel resulted from uncertain nature of model parameters. We assume that the return quantity of products and their quality are uncertain input parameters because of theiruncertain nature. Numerical results show the efficiency of the proposed stochastic programming model with CV aR criteria in handling data uncertainty and controlling risk level

Reverse logistics, Stochastic programming,conditional value at Risk