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An appointment scheduling framework to balance the production of blood units from donation

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  • Baş, Seda
  • Carello, Giuliana
  • Lanzarone, Ettore
  • Yalçındağ, Semih

Abstract

Blood is fundamental in several care treatments and surgeries, and plays a crucial role in the health care system. It is a limited resource, as it can be produced only by donors and its shelf life is short; thus, the blood donation (BD) system aims at providing adequate supply of blood units to transfusion centers and hospitals. An effective collection of blood units from donors is fundamental for adequately feeding the entire BD system and optimizing blood usage. However, despite its relevance, donation scheduling is only marginally addressed in the literature. In this paper we consider the Blood Donation Appointment Scheduling (BDAS) problem, aiming at balancing the production of the different blood types among days in order to provide a quite constant feeding of blood units to the BD system. We propose a framework for the appointment reservation that accounts for both booked donors and donors arriving without a reservation. It consists of an offline Mixed Integer Linear Programming (MILP) model for preallocating time slots to blood types, and an online prioritization policy to assign a preallocated slot when the donor calls to make the reservation.

Suggested Citation

  • Baş, Seda & Carello, Giuliana & Lanzarone, Ettore & Yalçındağ, Semih, 2018. "An appointment scheduling framework to balance the production of blood units from donation," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1124-1143.
    • Handle: RePEc:eee:ejores:v:265:y:2018:i:3:p:1124-1143
    DOI: 10.1016/j.ejor.2017.08.054
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    References listed on IDEAS

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    1. Nan Liu, 2016. "Optimal Choice for Appointment Scheduling Window under Patient No-Show Behavior," Production and Operations Management, Production and Operations Management Society, vol. 25(1), pages 128-142, January.
    2. Bjorn Berg & Brian T. Denton, 2012. "Appointment Planning and Scheduling in Outpatient Procedure Centers," International Series in Operations Research & Management Science, in: Randolph Hall (ed.), Handbook of Healthcare System Scheduling, chapter 0, pages 131-154, Springer.
    3. Yigal Gerchak & Diwakar Gupta & Mordechai Henig, 1996. "Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery," Management Science, INFORMS, vol. 42(3), pages 321-334, March.
    4. Maartje Zonderland & Richard Boucherie & Nelly Litvak & Carmen Vleggeert-Lankamp, 2010. "Planning and scheduling of semi-urgent surgeries," Health Care Management Science, Springer, vol. 13(3), pages 256-267, September.
    5. Belií«n, Jeroen & Demeulemeester, Erik, 2008. "A branch-and-price approach for integrating nurse and surgery scheduling," European Journal of Operational Research, Elsevier, vol. 189(3), pages 652-668, September.
    6. Han, Bin & Zhang, Wenjun & Lu, Xiwen & Lin, Yingzi, 2015. "On-line supply chain scheduling for single-machine and parallel-machine configurations with a single customer: Minimizing the makespan and delivery cost," European Journal of Operational Research, Elsevier, vol. 244(3), pages 704-714.
    7. Andres F. Osorio & Sally C. Brailsford & Honora K. Smith, 2015. "A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7191-7212, December.
    8. Edgar Alfonso & Xiaolan Xie & Vincent Augusto & Olivier Garraud, 2012. "Modeling and simulation of blood collection systems," Health Care Management Science, Springer, vol. 15(1), pages 63-78, March.
    9. Randolph Hall, 2012. "Bed Assignment and Bed Management," International Series in Operations Research & Management Science, in: Randolph Hall (ed.), Handbook of Healthcare System Scheduling, chapter 0, pages 177-200, Springer.
    10. Diwakar Gupta & Wen-Ya Wang, 2012. "Patient Appointments in Ambulatory Care," International Series in Operations Research & Management Science, in: Randolph Hall (ed.), Handbook of Healthcare System Scheduling, chapter 0, pages 65-104, Springer.
    11. Framinan, Jose M. & Ruiz, Rubén, 2010. "Architecture of manufacturing scheduling systems: Literature review and an integrated proposal," European Journal of Operational Research, Elsevier, vol. 205(2), pages 237-246, September.
    12. Nan Liu & Serhan Ziya, 2014. "Panel Size and Overbooking Decisions for Appointment-Based Services under Patient No-Shows," Production and Operations Management, Production and Operations Management Society, vol. 23(12), pages 2209-2223, December.
    13. Lawrence W. Robinson & Rachel R. Chen, 2010. "A Comparison of Traditional and Open-Access Policies for Appointment Scheduling," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 330-346, June.
    14. Erwin W. Hans & Peter T. Vanberkel, 2012. "Operating Theatre Planning and Scheduling," International Series in Operations Research & Management Science, in: Randolph Hall (ed.), Handbook of Healthcare System Scheduling, chapter 0, pages 105-130, Springer.
    15. Şahinyazan, Feyza Güliz & Kara, Bahar Y. & Taner, Mehmet Rüştü, 2015. "Selective vehicle routing for a mobile blood donation system," European Journal of Operational Research, Elsevier, vol. 245(1), pages 22-34.
    16. Samorani, Michele & LaGanga, Linda R., 2015. "Outpatient appointment scheduling given individual day-dependent no-show predictions," European Journal of Operational Research, Elsevier, vol. 240(1), pages 245-257.
    17. Jonsson, Patrik & Kjellsdotter Ivert, Linea, 2015. "Improving performance with sophisticated master production scheduling," International Journal of Production Economics, Elsevier, vol. 168(C), pages 118-130.
    18. Beliën, Jeroen & Forcé, Hein, 2012. "Supply chain management of blood products: A literature review," European Journal of Operational Research, Elsevier, vol. 217(1), pages 1-16.
    19. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    20. Puranam, Kartikeya & Novak, David C. & Lucas, Marilyn T. & Fung, Mark, 2017. "Managing blood inventory with multiple independent sources of supply," European Journal of Operational Research, Elsevier, vol. 259(2), pages 500-511.
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    Cited by:

    1. Semih Yalçındağ & Seda Baş Güre & Giuliana Carello & Ettore Lanzarone, 2020. "A stochastic risk-averse framework for blood donation appointment scheduling under uncertain donor arrivals," Health Care Management Science, Springer, vol. 23(4), pages 535-555, December.
    2. Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.

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