Anticipation vs. Reoptimization for Dynamic Vehicle Routing with Stochastic Requests

Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Solving dynamic vehicle routing problems is challenging, since it requires optimization in two directions. First, as a reaction to newly revealed information, current routing plans need to be reoptimized. Second, uncertain future changes of information need to be anticipated by explicitly quantifying the impact of future information and routing developments. Since customers or drivers usually wait for response, decisions need to be derived in real-time. This limited time often prohibits extensive optimization in both directions and the question arises how to utilize the limited calculation time effectively. In this paper, we compare the merit of route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we present a policy allowing for a tunable combination of two existing approaches, each one aiming on optimization in one direction. We show that anticipation is beneficial in every case. We further reveal how the optimization direction is strongly connected to the degree of dynamism, the percentage of unknown requests.