Construction and application of logistics scheduling model based on heterogeneous graph neural network

Article type: Research Article

Authors: Wang, Lei^{; *}

Affiliations: Henan College of Transportation, Zhengzhou Henan, China

Correspondence: [*] Corresponding author. Lei Wang. E-mail: zhangnan8604@163.com.

Abstract: The core of logistics is scheduling and monitoring. After the modern interprise logistics development concept change, the development prospect of enterprise logistics is more optimistic. Major enterprises have begun to use intelligent logistics scheduling platforms. In order to solve the problem that heterogeneous information fusion is complex in the temporal heterogeneous graphs, this paper proposes to dynamically store and update node representation through an augmented memory matrix in a memory network. At the same time, the model also designs a novel read-write module for the memory matrix, which can effectively capture the timing information in the long interaction sequence and has high flexibility. The model has significantly improved in tasks such as node classification, timing recommendation and visualization. This paper studies the logistics supply chain of modern enterprises and establishes the mathematical model of vehicle scheduling. This paper takes the non-full load scheduling model as the critical research object. Based on the research of logistics supply chain, the vehicle scheduling model is established. The intelligent heuristic algorithm is applied to solve it, and the effective vehicle distribution scheme and driving route are formed. The simulation results show that the approximate Pareto optimal solution obtained by our designed model and algorithm has good robustness. NSGAIIROELSDR can get a better solution in small-scale scheduling. However, in large-scale numerical experiments, the final solution obtained by MOEA/DROELSDR is obviously better than that of NSGAIIROELSDR, and the running time of MOEA/DROELSDR is also shorter. Therefore, we conclude that MOEA/DROELSDR is more suitable for large-scale scheduling, and NSGAIIROELSDR is more suitable for more minor scheduling.

Keywords: Logistics scheduling, heterogeneous graph neural network, edge feature coding, memory network

DOI: 10.3233/JIFS-234562

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12301-12312, 2023