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

Abstract: Abstract: Steel logistics platforms often need to split steel products into multiple shipping bills for transportation when handling customer orders. Less-than-truckload (LTL) cargo, which fails to meet the minimum load requirements of a truck, needed to be consolidated with goods from other customer orders to optimize transportation efficiency. Although previous studies had proposed some solutions, none had simultaneously considered the issues of detour distances and prioritizing high-priority cargo in consolidated shipments. Therefore, a multi-objective optimization framework for steel cargo consolidation under multiple constraints was proposed in this study. The globally optimal cargo consolidation decisions were achieved by 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 three optimization objectives first, and then the LTL cargo was consolidated and selected based on these priorities. Meanwhile, a representation enhancement module based on Graph Attention Network (GAT) was employed to dynamically represent cargo and LTL cargo information, which was then input into the decision network to maximize long-term multi-objective gains. Experimental results on a large-scale real-world freight dataset show that this method improves the weight ratio of high-priority cargo and reduces average detour distances while slightly sacrificing the total weight of tail cargo shipments, effectively enhancing the efficiency of consolidated transportation.

Key words: consolidation decision-making, Markov decision process, Proximal Optimization(PPO), Graph Attention Network(GAT), Decision optimization