Aims & Scope
ICILOM 2027 focuses on intelligent logistics and operations systems that sense, predict, optimize, adapt, and support coordinated decisions across physical and organizational networks. The conference welcomes methodological advances, validated system designs, and empirical research on the performance, sustainability, resilience, and responsible management of intelligent operations.
Track 1. AI and Decision Intelligence for Logistics and Operations
Turning operational data into predictions, recommendations, and actionable decisions. This track emphasizes AI methods and the quality of the decisions they support.
- Machine learning for demand, lead-time, ETA, and disruption prediction
- Prescriptive analytics and decision-focused learning
- Reinforcement learning for inventory, routing, and scheduling
- Causal inference and uncertainty-aware operational decisions
- Generative AI, foundation models, and logistics knowledge systems
- Agentic AI for planning and exception management
Track 2. Digital Twins, Data, and Connected Logistics Systems
Connecting operational data, physical logistics networks, and digital models to support visibility, experimentation, and decisions. Research may address data quality, interoperability, sensing, or validated digital twins.
- Digital twins of warehouses, ports, airports, and logistics networks
- IoT sensing, computer vision, and operational data fusion
- Logistics data engineering, data quality, and interoperability
- Real-time visibility and event-driven logistics systems
- Edge intelligence and integration with operational systems
- Digital twin calibration, validation, and simulation-to-reality transfer
Track 3. Smart Transportation, Autonomous Logistics, and Human–Robot Collaboration
Intelligent transport planning and control, autonomous logistics operations, and effective collaboration between people and automated systems. Contributions should address operational performance, coordination, or safety.
- Intelligent freight transportation and multimodal transport systems
- Connected vehicles, smart corridors, and last-mile delivery
- Autonomous mobile robots, robotic picking, and material handling
- Autonomous yard, port, airport, and delivery operations
- Multi-robot coordination and fleet task allocation
- Human–robot collaboration, ergonomics, safety, and remote supervision
Track 4. Optimization, Simulation, and Adaptive Operations
Mathematical optimization, simulation, and adaptive planning for logistics and operations decisions. This track welcomes methodological advances and carefully evaluated applications; AI is not a requirement.
- Vehicle routing, facility location, and logistics network design
- Integrated inventory, transportation, scheduling, and capacity planning
- Discrete-event, agent-based, and hybrid simulation
- Simulation optimization and scenario-based decision support
- Stochastic, robust, online, and multi-objective optimization
- Hybrid AI–operations research and adaptive resource allocation
Track 5. Resilient, Sustainable, and Circular Logistics
Planning and managing logistics systems that withstand disruptions, reduce environmental impacts, and support circular resource flows. Contributions should make assumptions and operational trade-offs explicit.
- Supply chain risk, disruption response, and recovery planning
- Carbon-aware routing and sustainable logistics network design
- Electric fleet operations, charging, and alternative fuels
- Reverse logistics, closed-loop networks, and circular supply chains
- Climate-related operational risk and logistics resilience
- Trade-offs among cost, service, emissions, circularity, and resilience
Track 6. Management, Strategy, and Governance of Intelligent Operations
Managing organizational strategy, people, investments, and governance in intelligent operations. Empirical and conceptual research should explain its contribution to the implementation, oversight, or value of intelligent systems.
- Operations strategy and digital transformation of logistics organizations
- Technology adoption, organizational readiness, and change management
- Human–AI decision-making, skills, and workforce redesign
- Explainability, trust, accountability, and managerial oversight
- Data governance, privacy, and cybersecurity in connected operations
- Business models, technology investment, and performance measurement
Scope and Contribution
Submissions should clearly explain:
- Intelligent capability: how the work advances sensing, prediction, decision-making, adaptation, coordination, or the management and governance of intelligent systems.
- Research contribution: the new method, model, system design, empirical evidence, or managerial insight.
- Evaluation: appropriate comparisons or evidence concerning cost, service, safety, sustainability, resilience, or decision quality.
We welcome operations research, simulation, theoretical contributions, and empirical management studies as well as AI-based methods. Evaluation should fit the research design; access to company data is not required. Select the primary track that best represents the central contribution.
Submission Categories
Full papers: proposed length 6–10 pages including references, tables and figures. Presentation abstracts: 200–250 words, for oral or poster consideration. Abstract acceptance alone does not qualify a contribution for full-paper proceedings. Select one primary track; interdisciplinary work is welcome when its logistics or operations contribution is clear.