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Data-Driven Design and Optimization for Smart Logistics Parks: Towards the Sustainable Development of the Steel Industry

Author

Listed:
  • Yaqiong Lv

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China)

  • Shangjia Xiang

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China)

  • Tianyi Zhu

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China)

  • Shuzhu Zhang

    (Department of Information Management and Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou, Zhejiang 310018, China)

Abstract

The design of steel logistics parks acts as fundamental infrastructure supporting the operations of storage, allocation, and distribution of steel products in the steel logistics industry, which actually lags behind the development of other logistics industries, such as e-commerce logistics, due to its large lot bulk storage, low turnover rate, and costly transportation and operations. This research proposes a data-driven approach for a specific steel logistics park, aiming to improve its operational efficiency in terms of product layout and allocation in multiple yards. The entry and delivery order data are analyzed comprehensively so as to determine the products with high operational frequency and the corresponding relevancy among them. Experimental results show that, among the 69 steel specifications, 14 high-frequency products are identified, and the correlation among the 14 identified high-frequency products possesses evident distribution characteristics concerning their brands and specifications. The identified frequency and correlation among various products can not only facilitate the product layout and allocation in steel logistics parks, but also advance the vehicle scheduling efficiency for product pick-up and delivery. Moreover, the research methodology and framework can provide managerial insights for other industries with mass data processing requirements.

Suggested Citation

  • Yaqiong Lv & Shangjia Xiang & Tianyi Zhu & Shuzhu Zhang, 2020. "Data-Driven Design and Optimization for Smart Logistics Parks: Towards the Sustainable Development of the Steel Industry," Sustainability, MDPI, vol. 12(17), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7034-:d:405732
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    References listed on IDEAS

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    1. Julian Pinto & Manuel Morales & Mariia Fedoruk & Marina Kovaleva & Arnaud Diemer, 2019. "Servitization in Support of Sustainable Cities: What Are Steel’s Contributions and Challenges?," Post-Print hal-02127800, HAL.
    2. Julian T. M. Pinto & Manuel E. Morales & Mariia Fedoruk & Marina Kovaleva & Arnaud Diemer, 2019. "Servitization in Support of Sustainable Cities: What Are Steel’s Contributions and Challenges?," Sustainability, MDPI, vol. 11(3), pages 1-18, February.
    3. Kuo, R.J. & Pai, C.M. & Lin, R.H. & Chu, H.C., 2015. "The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 958-972.
    4. Hsieh, Ling-Feng & Huang, Yi-Chen, 2011. "New batch construction heuristics to optimise the performance of order picking systems," International Journal of Production Economics, Elsevier, vol. 131(2), pages 618-630, June.
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    Cited by:

    1. Sebastjan Lazar & Dorota Klimecka-Tatar & Matevz Obrecht, 2021. "Sustainability Orientation and Focus in Logistics and Supply Chains," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    2. Mustafa Qahtan Alsudani & Mustafa Musa Jaber & Mohammed Hasan Ali & Sura Khalil Abd & Ahmed Alkhayyat & Z. H. Kareem & Ahmed Rashid Mohhan, 2023. "RETRACTED ARTICLE: Smart logistics with IoT-based enterprise management system using global manufacturing," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-31, March.

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