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Case mix classification and a benchmark set for surgery scheduling

Author

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  • Gréanne Leeftink

    (University of Twente)

  • Erwin W. Hans

    (University of Twente)

Abstract

Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of “instance proximity” to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

Suggested Citation

  • Gréanne Leeftink & Erwin W. Hans, 2018. "Case mix classification and a benchmark set for surgery scheduling," Journal of Scheduling, Springer, vol. 21(1), pages 17-33, February.
    • Handle: RePEc:spr:jsched:v:21:y:2018:i:1:d:10.1007_s10951-017-0539-8
    DOI: 10.1007/s10951-017-0539-8
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    Cited by:

    1. Sean Harris & David Claudio, 2022. "Current Trends in Operating Room Scheduling 2015 to 2020: a Literature Review," SN Operations Research Forum, Springer, vol. 3(1), pages 1-42, March.
    2. A, Augustin & P, Jouvet & N, Lahrichi & A, Lodi & LM, Rousseau, 2022. "A data-driven approach to include availability of ICU beds in the planning of the operating room," Omega, Elsevier, vol. 109(C).
    3. Roshanaei, Vahid & Booth, Kyle E.C. & Aleman, Dionne M. & Urbach, David R. & Beck, J. Christopher, 2020. "Branch-and-check methods for multi-level operating room planning and scheduling," International Journal of Production Economics, Elsevier, vol. 220(C).
    4. McRae, Sebastian & Brunner, Jens O., 2020. "Assessing the impact of uncertainty and the level of aggregation in case mix planning," Omega, Elsevier, vol. 97(C).
    5. Lien Wang & Erik Demeulemeester & Nancy Vansteenkiste & Frank E. Rademakers, 2022. "On the use of partitioning for scheduling of surgeries in the inpatient surgical department," Health Care Management Science, Springer, vol. 25(4), pages 526-550, December.
    6. Sebastian McRae & Jens O. Brunner & Jonathan F. Bard, 2020. "Analyzing economies of scale and scope in hospitals by use of case mix planning," Health Care Management Science, Springer, vol. 23(1), pages 80-101, March.
    7. Vandenberghe, Mathieu & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Bruneel, Herwig, 2019. "Surgery sequencing to minimize the expected maximum waiting time of emergent patients," European Journal of Operational Research, Elsevier, vol. 275(3), pages 971-982.
    8. Omolbanin Mashkani & Andreas T. Ernst & Dhananjay Thiruvady & Hanyu Gu, 2023. "Minimizing patients total clinical condition deterioration in operating theatre departments," Annals of Operations Research, Springer, vol. 328(1), pages 821-857, September.
    9. Aringhieri, Roberto & Duma, Davide & Landa, Paolo & Mancini, Simona, 2022. "Combining workload balance and patient priority maximisation in operating room planning through hierarchical multi-objective optimisation," European Journal of Operational Research, Elsevier, vol. 298(2), pages 627-643.

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