Learning to multi-vehicle cooperative bin packing problem via sequence-to-sequence policy network with deep reinforcement learning model

In the logistics bin packing scenario with only rear bin doors, the packing sequence of items determines the utilization of vehicle packing space, but there is relatively little research on optimizing the packing sequence of items. Therefore, this article focuses on the bin packing sequence problem in the multi-vehicle cooperative bin packing problem(MVCBPP) and proposes a deep reinforcement learning model based on the sequence-to-sequence policy network with deep reinforcement learning model(S2SDRL). Firstly, the sequence-to-sequence neural networks model is constructed, which determines the packing probability of all items. The items will be packed by combining the bidirectional LSTM model and the attention module to construct the encoder and decoder. Secondly, the bin packing strategy of the items is obtained by the constructed reinforcement learning packing framework. Finally, the Seq2Seq policy network is updated and optimized by the policy gradient method with a baseline to obtain the current optimal packing strategy. In several bin packing scenarios, S2SDRL improves the average vehicle space utilization by more than 4.0% compared with the traditional packing algorithm, and the forward computation time of the model is much smaller than that of the traditional heuristic algorithm, so the model also has more realistic application value. Ablation experiments also confirm the effectiveness of the modules in the S2SDRL model for optimization of the packing order. The sensitivity analysis shows the model's some stability when the input data changes.