From Learning to Optimization in Intelligent Logistics Systems
DETAILS
CALL FOR PAPERS
From Learning to Optimization in Intelligent Logistics Systems
Journal: Transportation Research Part E: Logistics and Transportation Review
Publisher: Elsevier
Submission Deadline: 30 October 2026
Introduction
Logistics systems are becoming increasingly intelligent — driven by advances in digitalization, automation, and data-driven management technologies. Freight and road transport, rail and maritime logistics, vehicle routing, intermodal and city/urban logistics are all undergoing rapid transformation.
Emerging paradigms — including collaboration, operational efficiency, real-time tracking, information sharing, crowdsourcing, green supply chains, new energy sources, and last-mile delivery — underscore the need for adaptive approaches that integrate data, manage uncertainty, and balance efficiency with sustainability. These developments call for innovative methods that enable robust planning, real-time decision-making, and intelligent optimization suited to the complexity of real-world logistics systems.
The area of learning and intelligent optimization brings together researchers from operations research and machine learning to create novel optimization methods that harness learning mechanisms to find high-quality solutions to relevant logistics problems. These learned solution approaches may outperform conventional ones by better capturing the characteristics of real-world problems — narrowing the gap between modeling and empirical research.
Scope & Significance
This Special Issue welcomes contributions that use learning approaches to strengthen optimization processes in intelligent transport and logistics contexts. Integration of learning and optimization can be achieved through:
Learning inputs for algorithms or models to speed up exact or heuristic approaches
Finding robust solutions under uncertainty
Embedding data directly into sequential decision-making
Methods such as predict-then-optimize, end-to-end learning, and sequential learning
This Special Issue is open for submissions from the ICCL & EUROMAR 2025 Conference as well as original papers not presented elsewhere.
Objectives
The Special Issue focuses on contributions that:
Integrate operations research and machine learning to offer novel optimization methods harnessing learning mechanisms for high-quality solutions to relevant problems
Impact intelligent logistics systems through strong and practical insights
Deliver on operational efficiency, sustainable operations, and data-driven decision-making support
List of Topic Areas
Manuscripts are invited on themes including, but not limited to:
Freight transportation — learning-based planning and optimization
Road transport and logistics — intelligent routing and scheduling
Rail transport and logistics — data-driven operations and management
Maritime transport and logistics — smart shipping and port operations
Urban and city logistics — last-mile delivery and crowd logistics
Routing problems — vehicle routing, dynamic routing, and real-time optimization
Crowd logistics — collaborative and platform-based delivery models
Intermodal logistics — integration and optimization across transport modes
Collaborative logistics — information sharing and joint operations
Reverse logistics — returns management and circular supply chains
Green supply chain with renewable energy — sustainability-oriented optimization
Internet of Things in smart logistics — real-time tracking and decision support
Shipping and port operations and port call optimization
Uncertainty modeling in logistics planning and control
Learning-based optimization in logistics
Machine learning methods that use optimization as a component
Guest Editors
Dr. Çağatay Iris University of Liverpool Management School, UK Email: c.iris@liverpool.ac.uk
Prof. Alice E. Smith University of Alabama, USA Email: alice.smith@ua.edu
Dr. Frederik Schulte Delft University of Technology, Netherlands Email: f.schulte@tudelft.nl
Prof. Jiangang Jin Shanghai Jiao Tong University, China Email: jiangang.jin@sjtu.edu.cn
Prof. Rosa G. González-Ramírez Universidad de los Andes Chile, Santiago, Chile Email: rgonzalez@uandes.cl
Key Deadlines
Manuscript Submission Deadline: 30 October 2026
Submission Guidelines
Submission process and papers must adhere to the standard author guidelines of Transportation Research Part E: Logistics and Transportation Review.
All submissions to this Special Issue should be submitted via the Transportation Research Part E online submission system. When submitting your paper, select Article Type:
"VSI: LearnOptLogis_Hans"
All submissions must be original and must not have been previously published or currently submitted for publication elsewhere.
For author guidelines, visit the official Transportation Research Part E journal page on the Elsevier ScienceDirect website.
About the Journal
Transportation Research Part E: Logistics and Transportation Review, published by Elsevier, is a leading international peer-reviewed journal with a CiteScore of 15.0 and Impact Factor of 8.8. It supports open access publishing and is dedicated to advancing research on logistics and transportation systems — providing a global platform for interdisciplinary scholarship exploring supply chain management, freight systems, urban logistics, and the integration of digital technologies into transportation and logistics operations.
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