Boosting Efficiency, Sustainability and Resilience of Logistics Systems: Decision Intelligence with AI and OR
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Boosting Efficiency, Sustainability and Resilience of Logistics Systems: Decision Intelligence with AI and OR
Journal: Transportation Research Part C: Emerging Technologies
Publisher: Elsevier
Submission Deadline: 15 September 2026
Introduction
Logistics systems are undergoing rapid transformation with the increasing adoption of Artificial Intelligence (AI), Operations Research (OR), and advanced analytical methods. Rising demands for efficiency, safety, sustainability, and resilience — coupled with growing complexity of decision-making in logistics sectors such as maritime, aviation, rail, and urban logistics — necessitate advanced decision intelligence frameworks empowered by the latest AI and OR methodologies.
In this Special Issue, AI is understood broadly — encompassing machine learning, symbolic reasoning, generative AI, and multi-agent systems that support perception, reasoning, and decision-making. OR refers to the use of mathematical modelling, optimization, simulation, and quantitative analysis to support systematic and data-driven decision-making in complex systems.
AI and OR are viewed as complementary — AI provides the ability to learn, adapt, and generate insights or scenarios, while OR delivers rigorous optimization and decision-support frameworks.
Scope & Significance
While AI and OR have demonstrated great potential in accelerating and improving decision-making, many organizations still struggle to integrate them into existing operational processes. A significant gap remains in understanding how AI and OR integration aligns with traditional logistics workflows and their tangible impact on business performance — particularly during the transitional period of reshaping next-generation logistics systems.
This Special Issue focuses on integrating AI and OR techniques across diverse logistics systems. Submissions are expected to present methodological innovations establishing strong connections between AI and OR in addressing challenges in planning, operations, and risk assessment — supported by empirical evidence and/or case studies validating the proposed approaches.
List of Topic Areas
Manuscripts are invited on themes including, but not limited to:
AI and OR integration for logistics planning and operational decision-making
Machine learning and optimization for maritime, aviation, rail, and urban logistics
Generative AI and multi-agent systems in logistics decision support
Scheduling, routing, and resource allocation using AI-OR hybrid approaches
Risk assessment, resilience planning, and disruption management in logistics
Sustainability and green logistics — AI and OR driven decarbonization strategies
Last-mile delivery optimization and urban logistics intelligence
Dynamic routing, real-time decision support, and adaptive logistics systems
Supply chain resilience — AI-enabled risk modelling and contingency planning
Human-machine decision chains — integrating algorithmic recommendations with operational workflows
Safety-critical logistics systems — AI and OR for hazard identification and control
Empirical case studies validating AI and OR implementation in logistics operations
Autonomous logistics systems — decision intelligence for vehicles, drones, and robots
Port operations, container logistics, and shipping optimization
AI and OR in multimodal and intermodal transportation planning
Guest Editors
Prof. Hua Wang School of Automotive and Transportation Engineering, Hefei University of Technology, China Email: hwang191901@gmail.com
Prof. Chenhao Zhou School of Management, Northwestern Polytechnical University, China Email: zhouchenhao@nwpu.edu.cn
Assoc. Prof. Okan Arslan Department of Decision Sciences, HEC Montréal, Canada Email: okan.arslan@hec.ca
Assoc. Prof. Çağatay Iris School of Management, University of Liverpool, UK Email: c.iris@liverpool.ac.uk
Dr. Yun Hui Lin Faculty of Business Administration, University of Macau, China Email: linyhie@gmail.com
Key Deadlines
Manuscript Submission Deadline: 15 September 2026
Submission Guidelines
All submissions must be made via the Transportation Research Part C: Emerging Technologies (TRC) online submission system. Authors should indicate that the paper is submitted for consideration for publication in this Special Issue. When choosing the Manuscript Article Type during the submission procedure, select:
"VSI: Decision Intelligence in Logistics"
Otherwise your submission will be handled as a regular manuscript.
All submitted papers should address significant issues pertinent to the theme of this Special Issue and fall within the scope of TRC. Criteria for acceptance include originality, contribution, and scientific merit. All manuscripts must be written in English with high scientific writing standards.
All submissions must be original and must not be under review elsewhere at the time of submission. Acceptance for publication will be based on referees' and editors' recommendations following a detailed peer review process.
About the Journal
Transportation Research Part C: Emerging Technologies, published by Elsevier, is a premier international peer-reviewed journal with a CiteScore of 15.9 and Impact Factor of 7.9. It supports open access publishing and is dedicated to advancing research on the application of emerging technologies in transportation — providing a leading global platform for interdisciplinary scholarship exploring AI, automation, data science, and optimization across diverse transportation and logistics systems worldwide.
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