Advances in Data-Driven Decisions on Transportation and Logistics

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 1143

Special Issue Editors

Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518131, China
Interests: logistics informatization; intelligent computing; operational research
Department of Management Science & Engineering, College of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Interests: city logistics; data-driven optimization; logistics informatization
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Guest Editor
Department of Mechanical Engineering, Indian Institute of Technology (IIT BHU), Varanasi, India
Interests: e-commerce logistics; evolutionary algorithm; optimization technique
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Special Issue Information

Dear Colleagues,

In the era of big data, everything can be digitized, which brings great opportunities and challenges to scientific decision making in transportation and logistics. Moreover, the attention of academia and industry has been attracted due to the high value of data-driven decisions in these industries.

A large amount of valuable data from the transportation and logistics industries are generated on a continuous basis; however, these data are rarely used for scientific decision making. More data are needed to help people make the right decisions for travel, such as designing a logistics network, last mile delivery, and so on. However, traditional decision-making methods have encountered great challenges in the digital era. At present, with the help of big data and other advanced technologies (such as artificial intelligence, deep learning, etc.)  decision makers can make full use of the data generated and collected to assist scientific decision making in improving the service level and operational efficiency.

This Special Issue is dedicated to the study of data-driven decisions in transportation and logistics, aiming to promote an increasing number of researchers to explore new theories, concepts model and algorithms of data-driven decisions in transportation and logistics. Original research and review articles are welcome.

Dr. Yandong He
Dr. Fuli Zhou
Dr. Saurabh Pratap
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimal decision on personal travel service
  • data-driven logistics network design
  • optimization algorithm in transportation and logistics
  • data-driven decision in Mobile as a Service (MaaS)
  • data-driven algorithm design
  • data-driven model and algorithm in transportation and logistics
  • advances in data-driven decisions
  • operational research and machine learning in transportation and logistics

Published Papers

There is no accepted submissions to this special issue at this moment.
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