A Four Directional Cooperative Three-dimensional Packing Method Based on Deep Reinforcement Learning

As an important part of the modern economy, logistics plays an important role in the national economy and social development. The three-dimensional bin packing problem (3D-BPP) in logistics is one of the key problems that must be solved to improve the efficiency of logistics operations. Deep reinforcement learning (DRL) has a powerful learning and decision-making ability, and the three-dimensional bin packing method based on DRL (DRL-3DBP) has become one of the research hotspots in the field of intelligent logistics. The existing DRL-3DBPs have difficulty in striking a balance between the action space, computational complexity, and exploration capability when solving 3D-BPP with large-size bins. To this end, this paper proposes a four directional cooperative packing (FDCP) method. The two-stage policy network receives the rotated bin states and generates four directional packing policies. Based on the actions sampled from the four policies, the four states are updated accordingly, and the action corresponding to the highest value is selected as the packing action. FDCP encourages agent to explore reasonable packing positions in all four directions while compressing the action space and reducing computational complexity. Experimental results show that FDCP achieves 1.2% ~ 2.9% improvement in space utilization on the packing problem with 100 × 100 large-sized bin and the numbers of 20, 30, and 50 items. © 2024 Science Press. All rights reserved.