  Optimizing delivery routes for last-mile logistics service is challenging and
has attracted the attention of many researchers. These problems are usually
modeled and solved as variants of vehicle routing problems (VRPs) with
challenging real-world constraints (e.g., time windows, precedence). However,
despite many decades of solid research on solving these VRP instances, we still
see significant gaps between optimized routes and the routes that are actually
preferred by the practitioners. Most of these gaps are due to the difference
between what's being optimized, and what the practitioners actually care about,
which is hard to be defined exactly in many instances. In this paper, we
propose a novel hierarchical route optimizer with learnable parameters that
combines the strength of both the optimization and machine learning approaches.
Our hierarchical router first solves a zone-level Traveling Salesman Problem
with learnable weights on various zone-level features; with the zone visit
sequence fixed, we then solve the stop-level vehicle routing problem as a
Shortest Hamiltonian Path problem. The Bayesian optimization approach is then
introduced to allow us to adjust the weights to be assigned to different zone
features used in solving the zone-level Traveling Salesman Problem. By using a
real-world delivery dataset provided by the Amazon Last Mile Routing Research
Challenge, we demonstrate the importance of having both the optimization and
the machine learning components. We also demonstrate how we can use
route-related features to identify instances that we might have difficulty
with. This paves ways to further research on how we can tackle these difficult
instances.
