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In this paper, a new type of 3D bin packing problem (BPP) is proposed, inwhich a number of cuboid-shaped items must be put into a bin one by oneorthogonally. The objective is to find a way to place these items that canminimize the surface area of the bin. This problem is based on the fact thatthere is no fixed-sized bin in many real business scenarios and the cost of abin is proportional to its surface area. Our research shows that this problemis NP-hard. Based on previous research on 3D BPP, the surface area isdetermined by the sequence, spatial locations and orientations of items. Amongthese factors, the sequence of items plays a key role in minimizing the surfacearea. Inspired by recent achievements of deep reinforcement learning (DRL)techniques, especially Pointer Network, on combinatorial optimization problemssuch as TSP, a DRL-based method is applied to optimize the sequence of items tobe packed into the bin. Numerical results show that the method proposed in thispaper achieve about 5% improvement than heuristic method.

Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method