Intelligent Logistics Based on Big Data

Submission Deadline: 20 May 2020

IEEE Access invites manuscript submissions in the area of Intelligent Logistics Based on Big Data.

The advent of the era of big data and the rapid development of e-commerce have provided a new development direction for the modern logistics industry, prompting the logistics industry to think more about data. In addition, the operation mode has gradually changed from the traditional extensive mode to the intelligent logistics one, characterized by information, data, sharing and intelligence.

Intelligent logistics based on big data has significantly improved the intelligence level of warehousing, transportation and distribution, including the intelligent location of logistics outlets, the optimal configuration of transportation routes, the highest loading rate of transportation vehicles, and the optimal distribution of the last mile, which can be used to explore greater potential business value through massive logistics data analysis.

The goal of this Special Section in IEEE Access is to provide a specific opportunity to review the state-of-the-art of intelligent logistics in big data, and bring together researchers in the relevant areas to share the latest progress, novel methodologies and potential research topics.

The topics of interest include, but are not limited to:

  • Design and development of intelligent logistics system
  • Data collection and knowledge management for intelligent logistics based on Big Data
  • Analysis of intelligent logistics mode based on Big Data
  • Development of smart logistics systems using Big Data
  • Emergency logistics modeling and optimization based on Big Data
  • Optimal design of manufacturing/remanufacturing logistics network
  • Data-driven-based intelligent logistics management methods & technologies
  • Internet-of-things-based intelligent logistics design and optimization
  • Environment analysis of reverse logistics based on Big Data
  • Modeling of network design for intelligent logistics using Big Data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Zhiwu Li, Macau University of Science and Technology, Macau

Guest Editors:

    1. Guangdong Tian, Shandong University, China
    2. Di Wu, Hunan University, China
    3. MengChu Zhou, New Jersey Institute of Technology, Newark, USA
    4. Feng Chu, Univeristy of Paris-Saclay and University of Evry, France


Relevant IEEE Access Special Sections:

  1. Applications of Big Data in Social Sciences
  2. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  3. Urban Computing and Intelligence

IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: