IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v270y2018i2p709-722.html
   My bibliography  Save this article

A mathematical programming model for optimizing the staff allocation in radiotherapy under uncertain demand

Author

Listed:
  • Vieira, Bruno
  • Demirtas, Derya
  • van de Kamer, Jeroen B.
  • Hans, Erwin W.
  • van Harten, Wim

Abstract

As the number of people diagnosed with cancer increases, demand for radiotherapy (RT) services has been continuously growing. In RT, delays in the start of treatment have shown to increase the risk of tumor progression in various cancer types, and patients experience greater psychological distress when subject to longer waiting times. The RT process, which involves imaging and treatment planning before treatment, is subject to complexities that hamper resource planning and control. On the demand side, the amount of workload in each operation depends on the highly variable patient inflow. On the supply side, radiation therapy technologists (RTTs) have multiple skills, rotation needs and partial availability, which makes the allocation of RTTs a complex task that often leads to situations of understaffing, jeopardizing the fulfillment of the patients' waiting time standards. In this paper, we propose a stochastic mixed-integer linear programming model that optimizes the allocation of RTTs to multiple operations in RT over a set of scenarios of patient inflow. The scenarios are generated from historical patient data, and the final RTT allocation covers the workload associated with all scenarios. The goal is to maximize the (expected) number of patients completing pre-treatment within the waiting time target. Results for a case study in the RT department of the Netherlands Cancer Institute show that, on average, the number of patients able to start treatment within the maximum waiting time standards may increase from 91.3% to 97.9% for subacute patients, and from 96.3% to 100.0% for regular patients.

Suggested Citation

  • Vieira, Bruno & Demirtas, Derya & van de Kamer, Jeroen B. & Hans, Erwin W. & van Harten, Wim, 2018. "A mathematical programming model for optimizing the staff allocation in radiotherapy under uncertain demand," European Journal of Operational Research, Elsevier, vol. 270(2), pages 709-722.
    • Handle: RePEc:eee:ejores:v:270:y:2018:i:2:p:709-722
    DOI: 10.1016/j.ejor.2018.03.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718302698
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.03.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    2. Peter Hulshof & Richard Boucherie & Erwin Hans & Johann Hurink, 2013. "Tactical resource allocation and elective patient admission planning in care processes," Health Care Management Science, Springer, vol. 16(2), pages 152-166, June.
    3. Conforti, D. & Guerriero, F. & Guido, R., 2010. "Non-block scheduling with priority for radiotherapy treatments," European Journal of Operational Research, Elsevier, vol. 201(1), pages 289-296, February.
    4. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    5. Antoine Legrain & Marie-Andrée Fortin & Nadia Lahrichi & Louis-Martin Rousseau, 2015. "Online stochastic optimization of radiotherapy patient scheduling," Health Care Management Science, Springer, vol. 18(2), pages 110-123, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bruno Vieira & Derya Demirtas & Jeroen B. Kamer & Erwin W. Hans & Louis-Martin Rousseau & Nadia Lahrichi & Wim H. Harten, 2020. "Radiotherapy treatment scheduling considering time window preferences," Health Care Management Science, Springer, vol. 23(4), pages 520-534, December.
    2. Dina Bentayeb & Nadia Lahrichi & Louis-Martin Rousseau, 2023. "On integrating patient appointment grids and technologist schedules in a radiology center," Health Care Management Science, Springer, vol. 26(1), pages 62-78, March.
    3. Andrés Miniguano-Trujillo & Fernanda Salazar & Ramiro Torres & Patricio Arias & Koraima Sotomayor, 2021. "An integer programming model to assign patients based on mental health impact for tele-psychotherapy intervention during the Covid–19 emergency," Health Care Management Science, Springer, vol. 24(2), pages 286-304, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bruno Vieira & Derya Demirtas & Jeroen B. Kamer & Erwin W. Hans & Louis-Martin Rousseau & Nadia Lahrichi & Wim H. Harten, 2020. "Radiotherapy treatment scheduling considering time window preferences," Health Care Management Science, Springer, vol. 23(4), pages 520-534, December.
    2. Kaining Shao & Wenjuan Fan & Zishu Yang & Shanlin Yang & Panos M. Pardalos, 2022. "A column generation approach for patient scheduling with setup time and deteriorating treatment duration," Operational Research, Springer, vol. 22(3), pages 2555-2586, July.
    3. Tu-San Pham & Louis-Martin Rousseau & Patrick Causmaecker, 2022. "A two-phase approach for the Radiotherapy Scheduling Problem," Health Care Management Science, Springer, vol. 25(2), pages 191-207, June.
    4. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    5. Petra Vogl & Roland Braune & Karl F. Doerner, 2019. "Scheduling recurring radiotherapy appointments in an ion beam facility," Journal of Scheduling, Springer, vol. 22(2), pages 137-154, April.
    6. Shao, Kaining & Fan, Wenjuan & Lan, Shaowen & Kong, Min & Yang, Shanlin, 2023. "A column generation-based heuristic for brachytherapy patient scheduling with multiple treatment sessions considering radioactive source decay and time constraints," Omega, Elsevier, vol. 118(C).
    7. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    8. Camila Ramos & Alejandro Cataldo & Juan–Carlos Ferrer, 2020. "Appointment and patient scheduling in chemotherapy: a case study in Chilean hospitals," Annals of Operations Research, Springer, vol. 286(1), pages 411-439, March.
    9. Siqiao Li & Ger Koole & Xiaolan Xie, 2020. "An adaptive priority policy for radiotherapy scheduling," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 154-180, March.
    10. Dina Bentayeb & Nadia Lahrichi & Louis-Martin Rousseau, 2019. "Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center," Health Care Management Science, Springer, vol. 22(4), pages 768-782, December.
    11. Timothy C. Y. Chan & Daniel Letourneau & Benjamin G. Potter, 2022. "Sparse flexible design: a machine learning approach," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 1066-1116, December.
    12. Ridvan Gedik & Shengfan Zhang & Chase Rainwater, 2017. "Strategic level proton therapy patient admission planning: a Markov decision process modeling approach," Health Care Management Science, Springer, vol. 20(2), pages 286-302, June.
    13. Xiang Ma & Antoine Sauré & Martin L. Puterman & Marianne Taylor & Scott Tyldesley, 2016. "Capacity planning and appointment scheduling for new patient oncology consults," Health Care Management Science, Springer, vol. 19(4), pages 347-361, December.
    14. Yasin Gocgun, 2018. "Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy," Health Care Management Science, Springer, vol. 21(3), pages 317-325, September.
    15. Antoine Legrain & Marie-Andrée Fortin & Nadia Lahrichi & Louis-Martin Rousseau, 2015. "Online stochastic optimization of radiotherapy patient scheduling," Health Care Management Science, Springer, vol. 18(2), pages 110-123, June.
    16. Roland Braune & Walter J. Gutjahr & Petra Vogl, 2022. "Stochastic radiotherapy appointment scheduling," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(4), pages 1239-1277, December.
    17. Tu San Pham & Antoine Legrain & Patrick De Causmaecker & Louis-Martin Rousseau, 2023. "A Prediction-Based Approach for Online Dynamic Appointment Scheduling: A Case Study in Radiotherapy Treatment," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 844-868, July.
    18. Ming Lv & Yi Li & Bo Kou & Zhili Zhou, 2017. "Integer programming for improving radiotherapy treatment efficiency," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-9, July.
    19. Tugba Cayirli & Pinar Dursun & Evrim D. Gunes, 2019. "An integrated analysis of capacity allocation and patient scheduling in presence of seasonal walk-ins," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 524-561, June.
    20. repec:ipg:wpaper:2013-014 is not listed on IDEAS
    21. Peter J. H. Hulshof & Martijn R. K. Mes & Richard J. Boucherie & Erwin W. Hans, 2016. "Patient admission planning using Approximate Dynamic Programming," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 30-61, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:270:y:2018:i:2:p:709-722. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.