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Optimizing E-commerce Warehouse Operations: Leveraging a Star and Dijkstra’s Algorithms for Efficient Picking Routes

In: Proceeding of the 19th International Congress on Logistics and Supply Chain Management Systems

Author

Listed:
  • Vo Thi Hong Hanh

    (Hong Bang International University)

  • Dinh Cao Nguyen

    (Hong Bang International University)

  • Ho Thanh Phong

    (Hong Bang International University)

Abstract

The aim of this study included research and application of optimization order picking process in warehouse E-commerce to reduce the route and time of travel. A Top-Down approach methodology was adopted, started with the comprehensive analysis of the picking process, followed by the design of a general management system architecture, and then the detailed development of functions based on this architecture. To solve the route optimization problem, two well-established algorithms, A* and Dijkstra's, were selected due to their efficiency in solving shortest path problems. Supporting software such as Python and SPSS were employed to develop the system and validate the software. Data were collected from the operational areas of a leading digital marketplace platform's warehouse facility in Ho Chi Minh City. The results showed a 36.5% reduction in picking time, a 28.5% reduction in travel distance, and a 32.55% increase in picker efficiency. These findings demonstrate that the A* algorithm is more effective in environments requiring rapid and accurate path calculations. The proposed framework can be implemented to various picking policies, independent of the specific warehouse layout, enhancing the system’s scalability and efficiency in today’s competitive online retail market.

Suggested Citation

  • Vo Thi Hong Hanh & Dinh Cao Nguyen & Ho Thanh Phong, 2026. "Optimizing E-commerce Warehouse Operations: Leveraging a Star and Dijkstra’s Algorithms for Efficient Picking Routes," Springer Books, in: Ming Li & Cheng Ren & Shenle Pan & George Q. Huang (ed.), Proceeding of the 19th International Congress on Logistics and Supply Chain Management Systems, pages 114-130, Springer.
    • Handle: RePEc:spr:sprchp:978-981-95-7700-2_9
    DOI: 10.1007/978-981-95-7700-2_9
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