  Balanced order batching problem (BOBP) arises from the process of warehouse
picking in Cainiao, the largest logistics platform in China. Batching orders
together in the picking process to form a single picking route, reduces travel
distance. The reason for its importance is that order picking is a labor
intensive process and, by using good batching methods, substantial savings can
be obtained. The BOBP is a NP-hard combinational optimization problem and
designing a good problem-specific heuristic under the quasi-real-time system
response requirement is non-trivial. In this paper, rather than designing
heuristics, we propose an end-to-end learning and optimization framework named
Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by
reducing it to balanced graph clustering optimization problem. In BTOGCN, a
task-oriented estimator network is introduced to guide the type-aware
heterogeneous graph clustering networks to find a better clustering result
related to the BOBP objective. Through comprehensive experiments on
single-graph and multi-graphs, we show: 1) our balanced task-oriented graph
clustering network can directly utilize the guidance of target signal and
outperforms the two-stage deep embedding and deep clustering method; 2) our
method obtains an average 4.57m and 0.13m picking distance ("m" is the
abbreviation of the meter (the SI base unit of length)) reduction than the
expert-designed algorithm on single and multi-graph set and has a good
generalization ability to apply in practical scenario.
