Academic CV

Breno Alves Beirigo

Academic appointments, education, publications, teaching, supervision, and service.

Assistant Professor of Stochastic Operations Research
University of Twente · Industrial Engineering and Business Information Systems
b.alvesbeirigo@utwente.nl

Academic appointments

Sep 2022–present

University of Twente

Assistant Professor of Stochastic Operations Research, Faculty of Behavioural, Management and Social Sciences, IEBIS.

Nov 2020–2022

Delft University of Technology

Postdoctoral Researcher in Transport Engineering and Logistics. Research on stochastic and dynamic integrated water–land transportation in the NWO TRiLOGy project.

Nov 2016–2020

Delft University of Technology

PhD Researcher in Transport Engineering and Logistics. Learning- and optimization-based dynamic fleet management for autonomous mobility-on-demand in the NWO i-CAVE project.

Apr 2015–Aug 2016

CEFET-MG, Brazil

Lecturer in Informatics. Teaching in programming, web development, automation, and computer science.

Education and qualifications

2025

University Teaching Qualification

University of Twente, completed June 2025.

2021

PhD in Operations Research

Delft University of Technology. Dynamic Fleet Management for Autonomous Vehicles: Learning- and Optimization-Based Strategies.

2016

MSc in Computer Science

Federal University of Viçosa. Thesis on single- and bi-objective parallel heuristics for the travel planning problem.

2014

BSc in Computer Science

Federal University of Viçosa. Silver Medal Presidente Bernardes for academic performance.

2012–2013

Exchange study

Advanced Sensor Applications, Hanze Institute of Technology, the Netherlands.

Peer-reviewed publications

  1. C. Karademir, B. A. Beirigo, and B. Atasoy. “A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system.” European Journal of Operational Research 322(2), 480–499 (2025). DOI.
  2. J. J. A. Kortekaas, B. A. Beirigo, and F. Schulte. “Beyond cargo hitching: Combined people and freight transport using dynamically configurable autonomous vehicles.” In Computational Logistics, 381–395 (2023). DOI.
  3. B. A. Beirigo, R. R. Negenborn, J. Alonso-Mora, and F. Schulte. “A business class for autonomous mobility-on-demand: Modeling service quality contracts in dynamic ridesharing systems.” Transportation Research Part C: Emerging Technologies 136, 103520 (2022). DOI.
  4. B. A. Beirigo, F. Schulte, and R. R. Negenborn. “A learning-based optimization approach for autonomous ridesharing platforms with service-level contracts and on-demand hiring of idle vehicles.” Transportation Science 56(3), 677–703 (2021). DOI.
  5. M. van der Tholen, B. A. Beirigo, J. Jovanova, and F. Schulte. “The share-a-ride problem with integrated routing and design decisions: The case of mixed-purpose shared autonomous vehicles.” In Computational Logistics (2021). DOI.
  6. B. A. Beirigo, F. Schulte, and R. R. Negenborn. “Overcoming mobility poverty with shared autonomous vehicles: A learning-based optimization approach for Rotterdam Zuid.” In Computational Logistics (2020). DOI.
  7. B. A. Beirigo, F. Schulte, and R. R. Negenborn. “Dual-mode vehicle routing in mixed autonomous and non-autonomous zone networks.” In IEEE Intelligent Transportation Systems Conference (2018). DOI.
  8. B. A. Beirigo, F. Schulte, and R. R. Negenborn. “Integrating people and freight transportation using shared autonomous vehicles with compartments.” IFAC-PapersOnLine 51, 392–397 (2018). DOI.
  9. B. A. Beirigo and A. G. dos Santos. “Application of NSGA-II framework to the travel planning problem using real-world travel data.” In IEEE Congress on Evolutionary Computation (2016). DOI.
  10. B. A. Beirigo and A. G. dos Santos. “A parallel heuristic for the travel planning problem.” In 15th International Conference on Intelligent Systems Design and Applications, 283–288 (2015). DOI.
  11. B. A. Beirigo, V. de Oliveira Matos, J. E. C. Arroyo, and L. B. Gonçalves. “Genetic algorithm based approach for cluster formation in wireless sensor networks.” In XXXVIII Conferencia Latinoamericana en Informática, 1–8 (2012). DOI.

Selected publications and summaries · University of Twente research profile · Google Scholar

Doctoral thesis

Dynamic Fleet Management for Autonomous Vehicles: Learning- and Optimization-Based Strategies. Delft University of Technology, 2021. DOI.

Funded and approved projects

  • Reducing the Energy Cost of AI in High-Stakes Systems, Energy Efficient Computing call, funded 2026 (€38,000). PIs: Fernando Castor and Marcos Machado. Contributor for logistics and transportation applications.
  • Supplying the Chip Sector: The Role of Industrial Engineering, ChipTech Talent / Project Beethoven, approved 2026. Proposal originator and project lead.
  • TRiLOGy, NWO research program on integrated water–land transportation, postdoctoral researcher, 2020–2022.
  • i-CAVE, NWO research program on autonomous transportation, doctoral researcher, 2016–2020.

Teaching and educational leadership

  • Coordinator, IEM Module 1: Introduction to Industrial Engineering and Management.
  • Data Analysis and Computational Thinking, BSc, lecturer and coordinator.
  • Warehousing, MSc, lecturer and coordinator.
  • Transportation and Logistics Management, MSc, lecturer and coordinator.
  • Data Analysis and Programming, pre-MSc, lecturer and coordinator.
  • Introduction to Data Analysis and Programming with Excel and VBA, BSc, lecturer and coordinator.
  • CEFET-MG, BSc Mechatronics Engineering: Android Development; Automation via Web; Laboratory of Programming Languages II; Programming Training.
  • CEFET-MG, Information Technology technical program: Advanced Web Applications; Computer Fundamentals.
  • Federal University of Viçosa, teaching assistant: Laboratory of Programming Languages.

Courses and teaching

Supervision

  • Cigdem Karademir: predictive fleet management for stochastic and dynamic integrated water–land transportation.
  • Abolfazl Maleki: decision-making under uncertainty in last-mile medical-drone delivery and dynamic collaboration among drones and complementary delivery modes.
  • BSc and MSc research supervision in transportation, logistics, warehousing, routing, forecasting, inventory, and logistics analytics.
  • Creator and maintainer of the public Thesis Toolkit.

Academic service

  • Invited-session chair, “Data-driven Methods for Transport Problems,” IFORS 2026, Vienna.
  • Session chair and organizer, “Sustainable Multimodal Transportation,” IFORS 2023.
  • Session-chair contributions at ICCL 2020 and IFAC CTS 2018.
  • Ad hoc referee, European Journal of Operational Research, 2025–2026.
  • Ad hoc referee, Transportation Research Part C: Emerging Technologies, 2024.
  • Contributor to the BMS AI Learning Community, 2026.

Academic service record

Selected presentations

  • “A Learning-Based Energy-Aware Fleet Management Approach for Water Mobility Systems,” invited session, IFORS 2026, Vienna.
  • Co-author, “Optimizing Medical Drone Delivery Under Uncertainty,” IFORS 2026, Vienna.
  • “A deep reinforcement learning approach for on-demand ride-pooling in high-capacity water transportation systems,” IFORS 2023, Santiago.

Methods and tools

Mathematical optimization · mixed-integer programming · heuristics and metaheuristics · approximate dynamic programming · reinforcement learning · simulation · experimental design · Python · Gurobi · Java · C++ · JavaScript · Quarto · Git and GitHub.

Profiles and contact

Breno Alves Beirigo
University of Twente · Ravelijn 4418
b.alvesbeirigo@utwente.nl