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

Yaqiong Lv, Shangjia Xiang, Tianyi Zhu and Shuzhu Zhang
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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

Sustainability, 2020, vol. 12, issue 17, 1-12

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.

Keywords: smart logistics; FP-growth; data-driven design; layout planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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