Article: Optimisation of cross border export e-commerce supply chain network based on machine learning and random programming Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.28 pp.84 - 102 Abstract: This paper proposes a collaborative optimisation method that integrates machine learning and stochastic programming to address the high demand uncertainty, complex logistics links, and rising operating costs faced by cross-border e-commerce supply chain networks for exports. Firstly, a dynamic demand forecasting model is constructed using random forest and XGBoost algorithm. Secondly, based on the predicted results, a two-stage stochastic programming model is established. In the first stage, the overseas warehouse location and basic inventory configuration are decided, and in the second stage, dynamic replenishment strategies are generated. Further introduce an improved sample average approximation (SAA) algorithm to solve the model, and design a multi-objective evaluation system to balance cost, timeliness, and service level indicators. Through actual enterprise data verification, it has been shown that this method reduces total costs by 14.7% compared to traditional deterministic models, and the demand forecasting error is controlled within 8.5%. Inderscience Publishers - linking academia, business and industry through research

Title: Optimisation of cross border export e-commerce supply chain network based on machine learning and random programming

Authors: Ruiqi Li

Addresses: School of International Business and Trade, Chengdu Polytechnic, Chengdu 610041, China

Abstract: This paper proposes a collaborative optimisation method that integrates machine learning and stochastic programming to address the high demand uncertainty, complex logistics links, and rising operating costs faced by cross-border e-commerce supply chain networks for exports. Firstly, a dynamic demand forecasting model is constructed using random forest and XGBoost algorithm. Secondly, based on the predicted results, a two-stage stochastic programming model is established. In the first stage, the overseas warehouse location and basic inventory configuration are decided, and in the second stage, dynamic replenishment strategies are generated. Further introduce an improved sample average approximation (SAA) algorithm to solve the model, and design a multi-objective evaluation system to balance cost, timeliness, and service level indicators. Through actual enterprise data verification, it has been shown that this method reduces total costs by 14.7% compared to traditional deterministic models, and the demand forecasting error is controlled within 8.5%.

Keywords: machine learning; random programming; cross border e-commerce supply chain; dynamic demand forecasting.

DOI: 10.1504/IJICT.2025.147712

International Journal of Information and Communication Technology, 2025 Vol.26 No.28, pp.84 - 102

Received: 23 May 2025
Accepted: 08 Jun 2025
Published online: 25 Jul 2025 *

Title: Optimisation of cross border export e-commerce supply chain network based on machine learning and random programming

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