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 1306
Special Issue Editors
Interests: logistics informatization; intelligent computing; operational research
Interests: city logistics; data-driven optimization; logistics informatization
Special Issues, Collections and Topics in MDPI journals
Interests: e-commerce logistics; evolutionary algorithm; optimization technique
Special Issues, Collections and Topics in MDPI journals
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
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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
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