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Optimization Helps Scheduling Nursing Staff at the Long-Term Care Homes of the City of Toronto

Author

Listed:
  • Manion Anderson

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Merve Bodur

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Scott Rathwell

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Vahid Sarhangian

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

Abstract

The City of Toronto Long-Term Care Homes and Services (LTCH&S) division is one of the largest providers of long-term care in the Canadian province of Ontario, providing care to 2,640 residents at 10 homes across Toronto. Our collaboration with LTCH&S was initiated to facilitate the increasingly challenging task of scheduling nursing staff and reduce the high absenteeism rate observed among the part-time nurses. We developed a spreadsheet-based scheduling tool to automate the generation of schedules and incorporate nurses’ preferences for different shifts into the schedules. At the core of the scheduling tool is a hierarchical optimization model that generates a feasible schedule with the highest total preference score while satisfying the maximum possible demand. Feasible schedules had to abide by a set of complex seniority requirements, which prioritized more-senior nurses when allocating the available shifts. Our scheduling tool was implemented in a 391-bed home in Toronto. The tool allowed nursing managers to generate feasible schedules within a fraction of an hour, in contrast to the status quo manual approach, which could take up to tens of hours. In addition, the schedules successfully accounted for preferences with, on average, above 94% of the allocated shifts ranked as most preferred.

Suggested Citation

  • Manion Anderson & Merve Bodur & Scott Rathwell & Vahid Sarhangian, 2023. "Optimization Helps Scheduling Nursing Staff at the Long-Term Care Homes of the City of Toronto," Interfaces, INFORMS, vol. 53(2), pages 133-154, March.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:2:p:133-154
    DOI: 10.1287/inte.2022.1132
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    References listed on IDEAS

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    2. Shahab Sadri & Arsalan Paleshi & Lihui Bai & Monica Gentili, 2024. "A Simulation Study for a Safe Reopening and Operation of the Trager Institute Optimal Aging Clinic During the COVID-19 Pandemic," Interfaces, INFORMS, vol. 54(2), pages 188-204, March.

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