Abstract:

Steel logistics platforms often need to split steel products into multiple waybills for transportation when handling customer orders. Less-Than-Truckload （LTL） cargo， which fails to meet the minimum load requirements of a truck， needs to be consolidated with goods from other customer orders to optimize transportation efficiency. Although previous studies had proposed some solutions for consolidation decision-making， none considered the issues of detour distance and prioritizing high-priority cargo simultaneously in consolidated shipments. Therefore， a multi-objective optimization framework for steel cargo consolidation under multiple constraints was proposed. The globally optimal cargo consolidation decisions were achieved by the framework through designing a hierarchical decision network and a representation enhancement module. Specifically， a hierarchical decision network based on Proximal Policy Optimization （PPO） was used to determine the priorities of the optimization objectives first， and then the LTL cargo was consolidated and selected on the basis of these priorities. Meanwhile， a representation enhancement module based on Graph ATtention network （GAT） was employed to represent cargo and LTL cargo information dynamically， which was then input into the decision network to maximize long-term multi-objective gains. Experimental results on a large-scale real-world cargo dataset show that compared to other online methods， the proposed method achieves a 17.3% increase in the proportion of high-priority cargo weight and a 7.8% reduction in average detour distance， with reducing the total shipping weight by 6.75% compared to the LTL cargo consolidation method that only maximizes cargo capacity. This enhances the efficiency of consolidated transportation effectively.

Key words: consolidation decision-making, Markov decision process, Proximal Policy Optimization (PPO), Graph ATtention network (GAT), decision optimization

摘要：

钢铁物流平台在处理客户订单时，常需将钢材产成品拆分成多个运单运输，而未达到货车最低载重限制（LTL）的“尾货”通常需要与其他客户订单的货物拼载以优化运输效率。尽管之前的研究已经提出一些拼载决策的解决方案，但均未能同时考虑拼货运输中可能产生的绕行距离以及高优先级货物优先发运的问题。因此，提出一个多约束条件下多目标优化的钢铁拼载决策框架。通过设计分层决策网络和表征增强模块实现全局最优的拼货决策。具体地，采用基于近端策略优化（PPO）的分层决策网络，先确定各个优化目标的优先级，再基于这些优先级进行尾单的组合与选择；同时，利用基于图注意力网络（GAT）的表征增强模块实时表征货物信息和尾货信息，并将这些信息输入决策网络以实现多目标的长期收益最大化。在大规模真实货运数据集上的实验结果表明，与其他在线方法相比，所提方法与仅最大化承运量的尾单拼货方法相比，在发运总重量减少6.75%的前提下，分别实现了高优先级货物重量占比和平均绕行距离比次优的贪心算法提升17.3%和降低7.8%，有效提升了拼载运输的效率。

关键词: 拼货决策, 马尔可夫决策过程, 近端策略优化, 图注意力网络, 决策优化