Background: Emerging artificial intelligence (AI) techniques married with increasingly available big data render unprecedented opportunities for new paradigms and approaches in planning, designing, and operating logistics and supply chain (LSC) systems. Emerging sensing and tracking technologies provide huge amounts of data throughout the life cycles of various products and services that were never seen before. Meanwhile, the exploding data availability imposes new challenges in adequately understanding and utilizing them to improve existing engineering practices. Remarkably, the enormous investment in clean energy eco-systems in recent years has come with new big data technologies and sources throughout associated LSC activities. The rapid rising of the new data sources with much higher granularity and much richer information demands defining and tackling research problems with new approaches to improve the sustainability of LSC systems.
Objectives: AI provides new venues for modeling and solving complicated problems with complex settings and enormous high-dimensionally data that may not be easily tackled or even defined with analytical modeling and mathematical programming alone. This special issue aims to unveil the potential of AI methods and explore new research paradigms and relevant applications with AI methods in the space of clean LSC. The outcomes of this special issue expect to bring new AI methodologies, applications, and perspectives to researchers and practitioners working on clean LSC systems.
Themes: This special issue will consider submissions in general themes on AI in cleaner LSC systems, including but not limited to the following list:
- Develop new AI methods (e.g., leveraging physics and behaviors) tailored for clean LSC applications;
- Define and model new clean LSC problems and solve them with AI methods;
- Use AI to address new problems where emerging transportation technologies and services are used to boost clean LSC systems, e.g., electric vehicles, automated vehicles, connected vehicles, shared mobility, drones, etc.;
- Use or collect new clean LSC data and analyze them with AI methods;
- Analyze the roles and impacts of clean LSC during the pandemic and post-pandemic periods with AI methods;
- Survey recent developments of AI in clean LSC.
Submission:
Manuscripts should be submitted via the submission site for Cleaner Logistics and Supply Chain: https://www.editorialmanager.com/clscn/default.aspx
The special issue will be set up in Editorial Manager (EM). Please choose “VSI: AI in CLSCN” from the “Article Type Name” dropdown menu when submitting your manuscripts.
Manuscripts should not have been previously published nor be currently under consideration for publication elsewhere. For Guide for Authors, please refer to the webpage: https://www.elsevier.com/journals/cleaner-logistics-and-supply-chain/2772-3909/guide-for-authors
Deadline
The submission deadline is 31/12/2021.
Special Issue Editors
Managing Guest Editor
Xiaopeng Li, Associate Professor, University of South Florida, USA, [email protected]
Guest Editors
Mohammad Marufuzzaman, Associate Professor, Mississippi State University, USA, [email protected]
Xiaobo Qu, Chair Professor, Chalmer University of Technology, Sweden, [email protected]
Abdul Rawoof Pinjari, Associate Professor, Indian Institute of Science, India, [email protected]
