Thomas Isele

Planning and organisation of last-mile delivery offers a lot of challenges. Traditional planning based purely on mathematical optimization often lacks important real-life aspects and thus does not satisfy relevant operational requirements. Experienced couriers have tacit knowledge about the delivery area and its customers, enabling them to choose more efficient routes than the originally planned ones. This in turn renders predictions of arrival times very imprecise because those predictions can only be based on planned routes. This courier's tacit knowledge is almost impossible to collect and maintain, let alone to incorporate in optimization and prediction algorithms. Thus, we at Deutsche Post DHL developed a novel, more holistic approach. We implicitly learn this tacit knowledge from historical tours and combine this with optimization algorithms to plan routes that an experienced courier would choose. Based on these routes, the delivery time predictions are done using machine learning trained on a large amount of past delivery events. In this talk we will present details of our algorithm, which incorporates machine learning, statistics, and optimization in a novel way. Furthermore, we show how it impacted last-mile delivery planning at Deutsche Post after its rollout across Germany.