Resumen
Delivery companies are affected by emission-sensitive traffic management systems. These systems are installed in cities to react instantly to emission hot-spots through adapting traffic light programs at intersections. This results in a change of the travel times for the delivery vehicle. In this paper, we model the problem in a dynamic vehicle routing problem with stochastic transition of deterministic travel time matrices. To solve this problem, we apply approximate value iteration, a method of approximate dynamic programming, to anticipate future travel time matrix changes in dynamic routing decisions. We vary the approach in the level of information about the state of the traffic management system. This allows to distinguish the required information for the routing decisions. Further, we compare the dynamic and anticipatory routing policies with a static a priori routing. Computational studies show an improved tour duration of routing with traffic management information over the a priori routing by up to 6.5%. We further show that an efficient representation of the traffic management system status in the approximate value iteration approach is mandatory to achieve sufficient anticipation.