A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands

We present a new algorithm that uses both local branching and Monte Carlo sampling in a multidescent search strategy for solving 0-1 integer stochastic programming problems. This procedure is applied to the single-vehicle routing problem with stochastic demands. Computational results show the effectiveness of this new approach to solving hard instances of the problem.