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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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    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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