用混合图卷积网络同时预测物流类型、延误和交通状态，提升供应链韧性与可持续性。
Improving a Hybrid Graphsage Deep Network for Automatic Multi-objective Logistics Management in Supply Chain
- 构建混合图SAGE网络，联合建模多任务物流信息。
- 在Smart Logistics数据集上对10类物流ID和3种交通状态预测准确率达97.8%与100%。
- 适用于需多目标优化的智能物流管理场景，尤其适合供应链数字化转型企业。
系统化物流、运输设施及仓储信息对促进供应链盈利发展至关重要。产业转型的目标是提升供应链韧性，而韧性策略有助于公司与物流服务商建立更积极的合作关系。高效的物流与运输管理可持续降低空气污染物排放。货物运输类型管理是分析物流与供应链可持续性的关键因素。为提升供应链管理效率，亟需自动预测运输类型、物流延迟及交通状态。本文提出一种用于供应链物流多任务管理的混合图SAGE网络（H-GSN）。研究以Kaggle平台上的DataCo、Shipping和Smart Logistcis三个数据集为基础，涵盖物流ID、运输类型、运输状态、交通状态及物流延迟等目标。在Smart Logistcis数据集上，10类物流ID与3种交通状态预测的平均准确率分别达到97.8%和100%；在DataCo数据集上运输类型预测准确率为98.7%，在Shipping数据集上物流延迟预测准确率为99.4%。不同物流场景下的评估指标验证了该方法在提升供应链韧性与可持续性方面的有效性。
原文摘要 · Abstract (English)
Systematic logistics, conveyance amenities and facilities as well as warehousing information play a key role in fostering profitable development in a supply chain. The aim of transformation in industries is the improvement of the resiliency regarding the supply chain. The resiliency policies are required for companies to affect the collaboration with logistics service providers positively. The decrement of air pollutant emissions is a persistent advantage of the efficient management of logistics and transportation in supply chain. The management of shipment type is a significant factor in analyzing the sustainability of logistics and supply chain. An automatic approach to predict the shipment type, logistics delay and traffic status are required to improve the efficiency of the supply chain management. A hybrid graphsage network (H-GSN) is proposed in this paper for multi-task purpose of logistics management in a supply chain. The shipment type, shipment status, traffic status, logistics ID and logistics delay are the objectives in this article regarding three different databases including DataCo, Shipping and Smart Logistcis available on Kaggle as supply chain logistics databases. The average accuracy of 97.8% and 100% are acquired for 10 kinds of logistics ID and 3 types of traffic status prediction in Smart Logistics dataset. The average accuracy of 98.7% and 99.4% are obtained for shipment type prediction in DataCo and logistics delay in Shipping database, respectively. The evaluation metrics for different logistics scenarios confirm the efficiency of the proposed method to improve the resilience and sustainability of the supply chain.
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