  A 3D flexible bin packing problem (3D-FBPP) arises from the process of
warehouse packing in e-commerce. An online customer's order usually contains
several items and needs to be packed as a whole before shipping. In particular,
5% of tens of millions of packages are using plastic wrapping as outer
packaging every day, which brings pressure on the plastic surface minimization
to save traditional logistics costs. Because of the huge practical
significance, we focus on the issue of packing cuboid-shaped items orthogonally
into a least-surface-area bin. The existing heuristic methods for classic 3D
bin packing don't work well for this particular NP-hard problem and designing a
good problem-specific heuristic is non-trivial. In this paper, rather than
designing heuristics, we propose a novel multi-task framework based on Selected
Learning to learn a heuristic-like policy that generates the sequence and
orientations of items to be packed simultaneously. Through comprehensive
experiments on a large scale real-world transaction order dataset and online AB
tests, we show: 1) our selected learning method trades off the imbalance and
correlation among the tasks and significantly outperforms the single task
Pointer Network and the multi-task network without selected learning; 2) our
method obtains an average 5.47% cost reduction than the well-designed greedy
algorithm which is previously used in our online production system.
