Enforcing Structural Robustness for Vehicle Routing Plans Despite Stochastic Demands
In this paper we propose an approach to derive a structurally robust solution of the capacitated dynamic vehicle routing problem with stochastic demands. The approach designs an a priori plan that minimizes transportation costs while allowing to accommodate changes in the demands without losing structural properties such as number of vehicles or optimality. We compare the proposed approach with stochastic programming with recourse. Considering a benchmark dataset, computational results show that the robust approach outperforms stochastic programming with recourse.