Improving a Hybrid Graphsage Deep Network for Automatic Multi-objective Logistics Management in Supply Chain

TL;DR

This paper introduces a hybrid GraphSAGE deep network for multi-objective logistics management, achieving high accuracy in predicting shipment types, delays, and traffic status to enhance supply chain resilience and sustainability.

Contribution

The paper proposes a novel hybrid GraphSAGE network for multi-task logistics prediction, improving accuracy and efficiency over existing methods in supply chain management.

Findings

Achieved up to 97.8% accuracy in logistics ID prediction.

Achieved 100% accuracy in traffic status prediction.

Demonstrated improved supply chain resilience and sustainability.

Abstract

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.