A stochastic optimization model for integrated forward/reverse logistics network design

In this paper, we develop a stochastic programming model for an 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 the separate design of the forward and reverse networks. Then the stochastic counterpart of the proposed MILP model is developed by using scenario-based stochastic approach. Numerical results show the power of the proposed stochastic model in handling data uncertainty.