Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
This addresses a practical problem for logistics and manufacturing industries where bin costs depend on surface area, but it is incremental as it builds on existing methods for similar optimization tasks.
The paper tackles a new 3D bin packing problem where items are placed to minimize bin surface area, showing it is NP-hard and achieving about 5% improvement over heuristic methods using deep reinforcement learning.
In this paper, a new type of 3D bin packing problem (BPP) is proposed, 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. Our research shows that this problem is NP-hard. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. Among these factors, the sequence of items plays a key role in minimizing the surface area. Inspired by recent achievements of deep reinforcement learning (DRL) techniques, especially Pointer Network, on combinatorial optimization problems such as TSP, a DRL-based method is applied to optimize the sequence of items to be packed into the bin. Numerical results show that the method proposed in this paper achieve about 5% improvement than heuristic method.