Journal Aims
The Journal of Artificial Intelligence Applications in Logistics and Transportation (JAILT) is an international, peer-reviewed, open access journal published by Heurena, an independent open access academic publisher. It provides a multidisciplinary forum for research applying artificial intelligence, machine learning, and optimization to logistics, supply chain management, and transportation systems.
The journal welcomes work on machine learning and deep learning for demand forecasting; smart ports, maritime logistics, and container-terminal operations; intelligent transportation systems and urban mobility; supply chain network design and optimization; metaheuristics and operations research; autonomous vehicles, drones, and warehouse robotics; digital twins, IoT, and blockchain for logistics; reinforcement learning for planning and scheduling; and data-driven decision support for freight and passenger transport.
To support responsible scientific progress through rigorous peer review, transparent editorial practice, and broad dissemination of high-quality research on AI in logistics and transportation.
To become the trusted international forum linking AI research, logistics practice, and transportation technologies that address industrial and societal challenges.
Core Research Areas
Intelligent Engineering
- Applied artificial intelligence and machine learning
- Robotics, automation, autonomous systems, and control
- Optimization, modelling, simulation, and decision support
- Explainable, trustworthy, and responsible engineering AI
Digital Industry
- Industry 4.0 and smart manufacturing
- Internet of Things and industrial data systems
- Digital twins and cyber-physical systems
- Additive manufacturing and digital production
Sustainable Engineering
- Renewable energy systems
- Low-carbon and circular engineering
- Sustainable infrastructure and resilient design
- Energy efficiency and industrial sustainability
Materials and Applications
- Advanced materials and nanotechnology
- Engineering characterization and computational design
- Industrial case studies and technology validation
- Interdisciplinary engineering applications