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In last-mile routing, the task of finding a route is often framed as aTraveling Salesman Problem to minimize travel time and associated cost.However, solutions stemming from this approach do not match the realized pathsas drivers deviate due to navigational considerations and preferences. Toprescribe routes that incorporate this tacit knowledge, a data-driven model isproposed that aligns well with the hierarchical structure of delivery datawherein each stop belongs to a zone - a geographical area. First, on the globallevel, a zone sequence is established as a result of a minimization over a costmatrix which is a weighted combination of historical information and distances(travel times) between zones. Subsequently, within zones, sequences of stopsare determined, such that, integrated with the predetermined zone sequence, afull solution is obtained.  The methodology is particularly promising as it propels itself within thetop-tier of submissions to the Last-Mile Routing Research Challenge, while itmaintains an elegant decomposition that ensures a feasible implementation intopractice. The concurrence between prescribed and realized routes underpins theadequateness of a hierarchical breakdown of the problem and the fact thatdrivers make a series of locally optimal decisions when navigating.Furthermore, experimenting with the balance between historical information anddistance exposes that historic information is pivotal in deciding a startingzone of a route. The experiments also reveal that at the end of a route,historical information can best be discarded, making the time it takes toreturn to the station the primary concern.

Learn Global and Optimize Local: A Data-Driven Methodology for Last-Mile Routing