Title: A Multi-task Selected Learning Approach for Solving New Type 3D Bin Packing Problem,
Abstract: This paper studies a new type of 3D bin packing problem (BPP), in which a
number of cuboid-shaped items must be put into a bin one by one orthogonally.
The objective is to find a way to place these items that can minimize the
surface area of the bin. This problem is based on the fact that there is no
fixed-sized bin in many real business scenarios and the cost of a bin is
proportional to its surface area. Based on previous research on 3D BPP, the
surface area is determined by the sequence, spatial locations and orientations
of items. It is a new NP-hard combinatorial optimization problem on
unfixed-sized bin packing, for which we propose a multi-task framework based on
Selected Learning, generating the sequence and orientations of items packed
into the bin simultaneously. During training steps, Selected Learning chooses
one of loss functions derived from Deep Reinforcement Learning and Supervised
Learning corresponding to the training procedure. Numerical results show that
the method proposed significantly outperforms Lego baselines by a substantial
gain of 7.52%. Moreover, we produce large scale 3D Bin Packing order data set
for studying bin packing problems and will release it to the research
community.