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A hybrid heuristic approach to master surgery scheduling with downstream resource constraints and dividable operating room blocks

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  • Kamran Kianfar

    (University of Isfahan)

  • Arezoo Atighehchian

    (University of Isfahan)

Abstract

This study considers a combination of strategic and tactical levels of operating room planning where two types of full-day and half-day blocks are considered. Constraints on available beds in hospital wards and different durations of stay for elective patients in the ward are the main assumptions of the problem. The aim is to minimize overtime and idleness of operating rooms, maximize surgeons' satisfaction and minimize the number of unscheduled surgeries in the master surgery schedule. We propose a mixed integer programming model as well as a novel heuristic algorithm by combining simulated annealing meta-heuristic and linear programming models. Real data from a teaching-educational hospital with 20 operating rooms and 47 surgeons’ groups as well as some random problem instances are used in the experiments. The results indicate high performance of the proposed heuristic algorithm in generating near-optimal Pareto solutions compared with the mathematical model and a local search algorithm from the literature. Sensitivity analysis is done on some parameters of the problem like overtime cost of operating rooms, maximum allowable overtime, available beds in the ward, and the number of attendance days preferred by each surgeon. The results from our case study show that 6% of operating room costs are related to the fact that some surgeons are unwilling to perform surgeries on some days of a week. Also, adding 20% to the capacity of ward beds results in 3% and 10% decrease in unscheduled surgeries and operating rooms idleness, respectively.

Suggested Citation

  • Kamran Kianfar & Arezoo Atighehchian, 2023. "A hybrid heuristic approach to master surgery scheduling with downstream resource constraints and dividable operating room blocks," Annals of Operations Research, Springer, vol. 328(1), pages 727-754, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-023-05395-2
    DOI: 10.1007/s10479-023-05395-2
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    References listed on IDEAS

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    Cited by:

    1. Yu Pu & Fang Li & Shahin Rahimifard, 2024. "Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments," Sustainability, MDPI, vol. 16(8), pages 1-26, April.

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