Complex challenging decisions: delivering to customers while balancing timeliness and environmental cost; regulating peer-to-peer markets while fostering infrastructure investment; transitioning to electric mobility while ensuring fairness in the uncertain future. The STAR Lab team researches how technology – information and computation – can help people make better decisions in these kinds of complex situations.

Research

The TU Delft STAR Lab focuses on individuals and groups who face many options or complicated implications. We research how bringing together data and models, peoples' preferences, and AI reasoning can facilitate outcomes better for society. We make impact through partnering with companies, universities, municipalities, and government departments.

News

Accepted paper

Journal of Air Transport Management, "Fleet Planning Under Demand and Fuel Price Uncertainty Using Actor-Critic Reinforcement Learning" (pdf)

Accepted paper

IFAC 2023, "Robust Optimal Control With Inexact State Measurements and Adjustable Uncertainty Sets"

Accepted paper

MT-ITS 2023, "Augmenting Ridership Data with Social Media Data to Analyse the Long-term Effect of COVID-19 on Public Transport"

Accepted paper

ICAPS 2023, "Parallel Batch Processing for the Coating Problem"

Accepted paper

ICAPS 2023, "Solving the Multi-Choice Two Dimensional Shelf Strip Packing Problem with Time Windows"

New STAR Lab doctoral student

Sami Ullah

Published paper

PLOS ONE, "Optimal Training of Integer-Valued Neural Networks with Mixed Integer Programming" (pdf)

Accepted paper

CPAIOR 2023, "Predicting the Optimal Period for Cyclic Hoist Scheduling Problems" (pdf)

Editorial board

STAR Lab Director joins the editorial board of Urban Planning

Conference

STAR Lab Director will serve as Area Chair for ECAI 2023

Accepted paper

AAMAS 2023, "Fair Pricing for Time-Flexible Smart Energy Markets"

Master's graduate

Tom McDonald, MSc, graduated on "Mixed Integer (Non-)Linear Programming Formulations of Graph Neural Networks"

Seminar

Imperial College London, "Linear and Bi-Linear Mixed Integer Formulations of Graph Neural Networks"

Conference

STAR Lab Director will serve as General Co-Chair for BNAIC/BeNeLearn 2023

STAR Lab student wins award

Dirk van Bokkem, MSc, takes second place of the 2022 KNVI/KIVI Scriptieprijzen voor Informatica en Informatiekunde

Invited keynote

STAR Lab Director will speak at the ELLIIT Focus Period Workshop on Hybrid AI

Accepted paper

Flexible Services and Manufacturing, "Berth Planning and Real-Time Disruption Recovery: A Simulation Study for a Tidal Port" (pdf)

Accepted paper

BNAIC 2022, "Machine Learning for the Cyclic Hoist Scheduling Problem" (pdf)

Conference

STAR Lab Director will serve as Proceedings and Video Co-Chair for RecSys 2023

Accepted paper

BNAIC 2022, "Optimisation of Annual Planned Rail Maintenance" (journal abstract) (pdf)

New STAR Lab doctoral student

Tilman Hinnerichs

Accepted paper

IAAI 2023, "Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation" (preprint)

Accepted paper

NeurIPS 2022, "Learning to Branch with Tree MDPs" (preprint)

Master's graduate

Wouter Morssink, MSc, graduated on "Automatically Designing Diverse Golf Course Routings"

Projects

AIFES
AIFES
AiNed (2023)

AI for the future energy system

Optimisation

Uncertainty


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TULIPS logo
TULIPS
EU Green Deal (2022-26)

Sustainable landside transport

Optimisation

Simulation


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

Data-driven building optimisation

Optimisation

Uncertainty


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Voestalpine logo
Voestalpine
Industrial (2021-22)

Job planning and sequencing

Optimisation


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E-pi logo
Epistemic AI
Horizon 2020 (2021-25)

Redefining the basis for AI

Uncertainty


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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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