Complex and challenging decisions: the STAR Lab team explores trustworthy fundamental AI to help people make better decisions in situations from sustainable logistics, to robust planning under uncertainty, to fair energy markets.
Complex and challenging decisions: the STAR Lab team explores trustworthy fundamental AI to help people make better decisions in situations from sustainable logistics, to robust planning under uncertainty, to fair energy markets.
Sustainable terminal services
Optimisation
Uncertainty
Simulation
Data-driven building optimisation
Uncertainty
Optimisation
Matthijs Vossen, MSc, graduated on "Harmonising Combined Nomenclature Trade Data for Longitudinal Analysis: The Lukaszuk-Torun Method"
LOGMS 2026, "A Reinforcement Learning Approach for the Dynamic Berth Allocation Problem"
Daniel Chou Rainho, MSc, graduated on "Improving Inland and Short-Sea Vessel Scheduling using Constraint Optimization"
Dr Eghonghon Eigbe graduated on "Optimising Discrete Problems: Decision diagrams and context-aware heuristics"
Dr Francisco Simoes, EFFILOAT project
IFORS 2026, "Incentive-based Coordinated Approach for Decentralized Multimodal Freight Platforms"
IFORS 2026, "Solving the Dynamic Berth Allocation Problem Using Reinforcement Learning and Graph Neural Networks"
TSL 2026, "Optimizing Real-time Freight Bundling via Deep Learning-Accelerated Heuristics"
STAR Lab Director will co-chair Data Science Meets Optimization workshop at IJCAI'26
Tijn Schreuder, MSc, graduated cum laude on "Neural Network Surrogate-Assisted Optimization of Prestressed Slab-Type Bridges"
Philippe Van Mastrigt, MSc, graduated on "Incorporating Uncertainty into Supply Chain Life Cycle Optimization"
MABS 2025 workshop post-proceedings, "An Agent-Based Model of Administrative Corruption in Hierarchical Organisations" (pdf)
European Journal on Artificial Intelligence, "Neuro-Symbolic Enterprise Optimisation"
