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OR Practice---Catch-Up Scheduling for Childhood Vaccination

Author

Listed:
  • Faramroze G. Engineer

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Pınar Keskinocak

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Larry K. Pickering

    (Centers for Disease Control and Prevention, Atlanta, Georgia 30333)

Abstract

In this paper, we outline the development of the core optimization technology used within a decision support tool to help providers and caretakers in constructing catch-up schedules for childhood immunization. These schedules ensure that a child continues to receive timely coverage against vaccine-preventable diseases in the likely event that one or more doses have been delayed. This project was undertaken as part of a collaborative effort between the Centers for Disease Control and Prevention (CDC) and Georgia Institute of Technology. Our aim is to develop a decision support tool that removes from the task of constructing catch-up schedules the tedious combinatorial aspects, while maintaining a level of generality that allows easy accommodation for changes in the existing rules and adding new vaccines to the schedule lineup. We show that the catch-up scheduling problem is NP-hard, and we develop a dynamic programming algorithm that exploits the typical size and structure of the problem to construct optimized schedules almost at the click of a button. In using an optimization-based algorithm, our approach is unique not only in methodology but also in the information, strategy, and advice we can offer to the user. The tool is being advocated by both the CDC and the American Academy of Pediatrics (AAP) as a means of encouraging caretakers and providers to take a more proactive role in ensuring timely vaccination coverage for children, as well as ensuring the accuracy and quality of a catch-up regime.

Suggested Citation

  • Faramroze G. Engineer & Pınar Keskinocak & Larry K. Pickering, 2009. "OR Practice---Catch-Up Scheduling for Childhood Vaccination," Operations Research, INFORMS, vol. 57(6), pages 1307-1319, December.
    • Handle: RePEc:inm:oropre:v:57:y:2009:i:6:p:1307-1319
    DOI: 10.1287/opre.1090.0756
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    References listed on IDEAS

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    1. W. Kubiak & J. Błażewicz & M. Dror & N. Katoh & H. Rock, 1998. "Resource Constrained Chain Scheduling of UET Jobs on Two Machines," Operations Research, INFORMS, vol. 46(5), pages 742-746, October.
    2. Peter Brucker & Johann Hurink & Wieslaw Kubiak, 1999. "Scheduling identical jobs with chain precedence constraints on two uniform machines," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 49(2), pages 211-219, April.
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    Cited by:

    1. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "Literature review: The vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 268(1), pages 174-192.
    2. Casey Chung & Milind Dawande & Divakar Rajamani & Chelliah Sriskandarajah, 2011. "A Short-Range Scheduling Model for Blockbuster's Order-Processing Operation," Interfaces, INFORMS, vol. 41(5), pages 466-484, October.
    3. Hannah K. Smalley & Pinar Keskinocak & Faramroze G. Engineer & Larry K. Pickering, 2011. "Universal Tool for Vaccine Scheduling: Applications for Children and Adults," Interfaces, INFORMS, vol. 41(5), pages 436-454, October.
    4. Aswani, Anil & Kaminsky, Philip & Mintz, Yonatan & Flowers, Elena & Fukuoka, Yoshimi, 2019. "Behavioral modeling in weight loss interventions," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1058-1072.
    5. Muckstadt, John A. & Klein, Michael G. & Jackson, Peter L. & Gougelet, Robert M. & Hupert, Nathaniel, 2023. "Efficient and effective large-scale vaccine distribution," International Journal of Production Economics, Elsevier, vol. 262(C).

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