dr.ir. A. Braaksma (Aleida)

Assistant Professor
EEMCS-AM-MOR
Zilverling 4013
a.braaksma@utwente.nl
+31534896471
Business card

Aleida Braaksma received her M.Sc. degree in Applied Mathematics from the University of Twente (UT) in 2010. Her master thesis project, for which she received multiple thesis awards, marked the start of her career in healthcare operations research. In 2015, she received the Ph.D. degree for the dissertation ‘Timely and Efficient planning of TReatments through Intelligent Scheduling’. She performed her Ph.D. project at CHOIR (Center for Healthcare Operations Improvement and Research, UT) and the Academic Medical Center (AMC) Amsterdam. Subsequently, Aleida spent two years working as a postdoctoral fellow in healthcare operations research at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) in Boston, United States. Currently, she is an assistant professor at the Stochastic Operations Research group of the UT and within CHOIR. Her research interests are in planning and scheduling in healthcare with a specific focus on decision making under uncertainty.

Expertise

  • Nursing and Health Professions

    • Patient
    • Time
    • University Hospital
    • Hospital
    • Outpatient Department

  • Computer Science

    • Models
    • Capacity Planning
    • Service

Organisations

Publications

2024
Optimisation of medication reconciliation using queueing theory: A computer experiment, 1-8 (E-pub ahead of print/First online). Kruik-Kollöffel, W. J., Moltman, G. A. W., Wu, M. D., Braaksma, A., Karapinar, F. & Boucherie, R. J.https://doi.org/10.1007/s11096-024-01722-0
2023
Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients, 501-515. Braaksma, A., Copenhaver, M. S., Zenteno, A. C., Ugarph, E., Levi, R., Daily, B. J., Orcutt, B., Turcotte, K. M. & Dunn, P. F.https://doi.org/10.1007/s10729-023-09638-3A Sampling-Based Method for Gittins Index Approximation, 1-31. ArXiv.org. Baas, S., Boucherie, R. J. & Braaksma, A.https://doi.org/10.48550/arXiv.2307.11713Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy, Article 102801, 1-21. Dijkstra, S., Baas, S., Braaksma, A. & Boucherie, R. J.https://doi.org/10.1016/j.omega.2022.102801Appointment scheduling for medical diagnostic centers considering time-sensitive pharmaceuticals:: A dynamic robust optimization approach, 1018-1031. Namakshenas, M., Mahdavi Mazdeh, M., Braaksma, A. & Heydari, M.https://doi.org/10.1016/j.ejor.2022.06.037
2022
Minimising total earliness and tardiness with periodically supplied non-renewable resource profiles, 3170-3181. Namakshenas, M., Braaksma, A. & Mahdavi Mazdeh, M.https://doi.org/10.1080/00207543.2021.1912430
2021
Operations Research: Introduction to Models and Methods. World Scientific Press. Boucherie, R. J., Braaksma, A. & Tijms, H.https://doi.org/10.1142/12343Making an Impact on Healthcare LogisticsIn Handbook of Healthcare Logistics: Bridging the Gap between Theory and Practice (pp. 1-13). Springer. Zonderland, M. E., Leeftink, G., Braaksma, A., Boucherie, R., Hans, E. W. & Kortbeek, N.https://doi.org/10.1007/978-3-030-60212-3_1Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units, 402-419. Baas, S., Dijkstra, S., Braaksma, A., van Rooij, P., Snijders, F. J., Tiemessen, L. & Boucherie, R.https://doi.org/10.1007/s10729-021-09553-5Robust Surgery Scheduling: A Model-Based OverviewIn Handbook of Healthcare Logistics: Bridging the Gap between Theory and Practice (pp. 37-56). Springer. Otten, M., Bos, J., Braaksma, A. & Boucherie, R. J.https://doi.org/10.1007/978-3-030-60212-3_4

Research profiles

Address

University of Twente

Zilverling (building no. 11), room 4013
Hallenweg 19
7522 NH Enschede
Netherlands

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