  Express systems play important roles in modern major cities. Couriers serving
for the express system pick up packages in certain areas of interest (AOI)
during a specific time. However, future pick-up requests vary significantly
with time. While the assignment results are generally static without changing
with time. Using the historical pick-up request number to conduct AOI
assignment (or pick-up request assignment) for couriers is thus unreasonable.
Moreover, even we can first predict future pick-up requests and then use the
prediction results to conduct the assignments, this kind of two-stage method is
also impractical and trivial, and exists some drawbacks, such as the best
prediction results might not ensure the best clustering results. To solve these
problems, we put forward an intelligent end-to-end predict-then-optimize
clustering method to simultaneously predict the future pick-up requests of AOIs
and assign AOIs to couriers by clustering. At first, we propose a deep
learning-based prediction model to predict order numbers on AOIs. Then a
differential constrained K-means clustering method is introduced to cluster
AOIs based on the prediction results. We finally propose a one-stage end-to-end
predict-then-optimize clustering method to assign AOIs to couriers reasonably,
dynamically, and intelligently. Results show that this kind of one-stage
predict-then-optimize method is beneficial to improve the performance of
optimization results, namely the clustering results. This study can provide
critical experiences for predict-and-optimize related tasks and intelligent
assignment problems in express systems.
