Article: Cross-border e-commerce supply chain optimisation with transfer learning models Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.34 pp.58 - 73 Abstract: To solve the problems of data sparsity, unstable demand and multi-stage coordination in the cross-border e-commerce supply chain optimisation bottleneck, this paper puts forward an end-to-end decision-making framework based on multi-source hierarchical transfer learning and deep reinforcement learning. By performing multi-source adversarial adaptation in feature space to learn domain-invariant representations and introducing a meta-learning weighting mechanism at the sample level to refine the selection of beneficial knowledge, this approach systematically mitigates model cold-start and negative transfer issues. Experiment shows that the framework greatly improves the forecast accuracy of demand from 4.83 to 5.76 compared to the basic model. In simulated cross-border supply chain networks, it cuts down the overall operation expenses by 7.2%, and shortens the time taken to fulfil orders by 13.5%. Inderscience Publishers - linking academia, business and industry through research

Title: Cross-border e-commerce supply chain optimisation with transfer learning models

Authors: Peng Xu; Bing Han

Addresses: School of Finance and Economics, Xinxiang Vocational and Technical College, Xinxiang, 453000, China ' School of Finance and Economics, Xinxiang Vocational and Technical College, Xinxiang, 453000, China

Abstract: To solve the problems of data sparsity, unstable demand and multi-stage coordination in the cross-border e-commerce supply chain optimisation bottleneck, this paper puts forward an end-to-end decision-making framework based on multi-source hierarchical transfer learning and deep reinforcement learning. By performing multi-source adversarial adaptation in feature space to learn domain-invariant representations and introducing a meta-learning weighting mechanism at the sample level to refine the selection of beneficial knowledge, this approach systematically mitigates model cold-start and negative transfer issues. Experiment shows that the framework greatly improves the forecast accuracy of demand from 4.83 to 5.76 compared to the basic model. In simulated cross-border supply chain networks, it cuts down the overall operation expenses by 7.2%, and shortens the time taken to fulfil orders by 13.5%.

Keywords: transfer learning; cross border e-commerce; supply chain; deep reinforcement learning; DRL.

DOI: 10.1504/IJICT.2026.152999

International Journal of Information and Communication Technology, 2026 Vol.27 No.34, pp.58 - 73

Received: 16 Dec 2025
Accepted: 22 Jan 2026
Published online: 17 Apr 2026 *

Title: Cross-border e-commerce supply chain optimisation with transfer learning models

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