  The recent past has witnessed a notable surge in on-demand food delivery
(OFD) services, offering delivery fulfillment within dozens of minutes after an
order is placed. In OFD, pooling multiple orders for simultaneous delivery in
real-time order assignment is a pivotal efficiency source, which may in turn
extend delivery time. Constructing high-quality order pooling to harmonize
platform efficiency with the experiences of consumers and couriers, is crucial
to OFD platforms. However, the complexity and real-time nature of order
assignment, making extensive calculations impractical, significantly limit the
potential for order consolidation. Moreover, offline environment is frequently
riddled with unknown factors, posing challenges for the platform's
perceptibility and pooling decisions. Nevertheless, delivery behaviors of
skilled couriers (SCs) who know the environment well, can improve system
awareness and effectively inform decisions. Hence a SC delivery network (SCDN)
is constructed, based on an enhanced attributed heterogeneous network embedding
approach tailored for OFD. It aims to extract features from rich temporal and
spatial information, and uncover the latent potential for order combinations
embedded within SC trajectories. Accordingly, the vast search space of order
assignment can be effectively pruned through scalable similarity calculations
of low-dimensional vectors, making comprehensive and high-quality pooling
outcomes more easily identified in real time. SCDN has now been deployed in
Meituan dispatch system. Online tests reveal that with SCDN, the pooling
quality and extent have been greatly improved. And our system can boost
couriers'efficiency by 45-55% during noon peak hours, while upholding the
timely delivery commitment.
