Complex challenging decisions: the STAR Lab team explores trustworthy fundamental AI to help people make better decisions in situations from sustainable logistics, to robust investment planning, to fair energy markets.

Research

The TU Delft STAR Lab confronts a fundamental question of the AI era: how can machine intelligence assist individuals and groups who face complex decision trade-offs? We research how bringing together data and models, human preferences, and AI reasoning can facilitate outcomes better for society. We make impact through partnering with companies, universities, municipalities and government departments.

Projects

FMaaS
FMaaS
NWO (2024-29)

Freight Management as a Service

Optimisation

Uncertainty


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AIFES
AIFES
AiNed (2023)

AI for the future energy system

Optimisation

Uncertainty


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Plug-In
Plug-In
Dutch Ministry EZK (2022-26)

HGV energy hubs

Optimisation

Uncertainty


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TULIPS logo
TULIPS
EU Green Deal (2022-26)

Sustainable landside transport

Optimisation

Simulation


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E-pi logo
Epistemic AI
Horizon 2020 (2021-26)

Redefining the basis for AI

Uncertainty


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B4B logo
Brains for Buildings
Dutch Ministry EZK (2021-25)

Data-driven building optimisation

Uncertainty

Optimisation


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Voestalpine logo
Voestalpine
Industrial (2021-22)

Job planning and sequencing

Optimisation


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OPTIMAL
OPTIMAL
NWO (2020-25)

Machine learning for optimisation

Optimisation


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TAILOR logo
TAILOR
EU Horizon 2020 (2020-24)

Trustworthy AI

Simulation

Optimisation


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SAM-FMS
SAM-FMS
NWO (2020-24)

Scheduling cyber-physical systems

Optimisation


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HaPSISH
HaPSISH
NWO (2016-20)

Energy system integration

Optimisation

Uncertainty


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DCSMART
DCSMART
EU Horizon 2020 (2016-19)

Energy system integration

Simulation

Uncertainty


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News

Accepted paper

ECC 2024, "Robust Optimal Control With Binary Adjustable Uncertainties"

Accepted paper

CPAIOR 2024, "Improving Metaheuristic Efficiency for Stochastic Optimization Problems by Sequential Predictive Sampling"

Master's graduate

Wytze Elhorst, MSc, graduated on "Exploring the Multi-Objective Dial-A-Ride Problem: An Analysis of Genetic Algorithms and MIP"

Accepted paper

International Shipbuilding Progress, "Multi-Fidelity Kriging Extrapolation Together with CFD for the Design of the Cross-Section of a Falling Lifeboat" (pdf)

Accepted paper

AAMAS 2024, "Bayesian Ensembles for Exploration in Deep Q-Learning"

Master's graduate

Marvin Kleijweg, MSc, graduated on "Encouraging Circular Wood-Based Building Practices in Amsterdam"

Master's graduate

Lucas Veeger, MSc, graduated on "CCS Reservoir Simulation using Graph Neural Networks"

Accepted paper

Transportation Research Board Part C, "A Data-driven Time-Dependent Routing and Scheduling for Activity-Based Freight Transport Modelling" (pdf)

Master's graduate

Katja Schmahl, MSc, graduated on "Railway Maintenance Scheduling"

Accetped paper

NEXT'23, "RSimGNN for long-term CO2 saturation predictions for CCS Reservoir Simulation"

New STAR Lab doctoral student

Mahsa Movaghar joins the STAR Lab

Master's graduate

Zhongbo Yao, MSc, graduated on "Optimization of Strawberry Supply Chain from the Perspective of Producers"

Accepted paper

IEEE Access, "Sequence- and Time-dependent Maintenance Scheduling in Twice Re-entrant Flow Shops" (pdf)

Master's graduate

Robin Oosterbaan, MSc, graduated on "Determining the Optimal Route for a Tethered Manure Applying Robot"

Master's graduate

Gautham Venkataraman, MSc, graduated on "Reduce, Reuse, Recycle: On exploration of solution reuse in VRPTW"

Master's graduate

Angelos Zoumis, MSc, graduated on "How Can the Behaviour of Specialized Heuristic Solvers Assist Constraint Solvers for Optimization Problems?"

Accepted paper

Artificial Intelligence, "Maintenance Commitments: Conception, Semantics, and Coherence" (pdf)

Accepted paper

EWRL 2023, "Bayesian Deep Q-Learning via Sequential Monte Carlo" (pdf)