Mir Saman Pishvaee - Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Iran
Fariborz Jolai - Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Iran
Jafar Razmi - Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Iran

In this paper, we develop a stochastic programming model for integrated forward/reverse logistics network design under uncertainty. First, an efficient deterministic mixed integer linear programming model is developed for integrated logistics network design to avoid the sub-optimality caused by a separate design of the forward and reverse networks. Then the stochastic counterpart of the proposed MILP model is developed by using scenariobased stochastic approach. Numerical results show the power of the proposed stochastic model in handling data uncertainty.

Closed-loop logistics network, integrated logistics network design, stochastic programming