Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-158
IoT-Enabled Intelligent Warehousing Optimization for Cross-Border E-Commerce
1 Lecturer,
Digital Business College, Heilongjiang Polytechnic, Harbin, 150001,
China.
2 Associate Professor, Digital
Business College, Heilongjiang Polytechnic, Harbin, 150001, China.
E-mail: ZheChencc@outlook.com (corresponding author).
Production Management
Received August 11, 2025; revised October 11, 2025; accepted October 11, 2025
Available online March 7, 2026
Abstract: To address the limitations of traditional warehousing systems in coping with order fluctuations and unexpected events, resulting in an imbalance between inventory and cost control, this study proposes an intelligent warehousing management system that integrates the Ant Colony Genetic Algorithm (ACGA) and the Internet of Things (IoT). To enhance the intelligence of warehouse management, this study selected Ant Colony Optimization for path planning and Genetic Algorithm (GA) to enhance global search capability. The combination of the two forms ACGA to improve convergence performance. The system simultaneously integrates IoT to enable real-time data collection and dynamic feedback, compensating for the algorithm's shortcomings in sensing unexpected events. By deeply integrating ACGA with IoT, this study constructs an intelligent warehouse management system that enables integrated, closed-loop operation of perception and scheduling. Experimental results show that the root mean square error of path planning in scheduling tasks is 62.3m. In terms of accuracy, the proposed system improves from 36.9% to 96.7%, and the F1-score stabilizes at 0.97 by the end of iterations. In a simulated cross-border e-commerce warehousing management environment, the system availability increases to 94.2% after 100 iterations. Regarding overall performance, the response delay is 25ms, and the computational load rate reaches 68%, helping to avoid resource idleness and system overload. At the same time, the energy consumption is only 18 W·h, the task completion rate reaches 98.5%, and the system stability is 99.2%. These results indicate that the proposed system significantly outperforms the comparison system in comprehensive performance and meets the optimization needs in complex cross-border e-commerce task environments, offering a feasible technical solution for intelligent warehousing management.
Keywords: Internet of things, genetic algorithms, ant colony optimization, cross-border, E-commerce, intelligent warehousing, management system, optimization design.
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Citation: Zhang, G. and Chen, Z (2026). IoT-Enabled Intelligent Warehousing Optimization for Cross-Border E-Commerce. Journal of Engineering, Project, and Production Management, 16(2), 2025-158.
