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Robust and adaptive optimal healthcare staff scheduling


Traditional deterministic scheduling methods can cause inefficient assignments and costly staff turnover due to poor schedules that are not robust to unpredictable demand. Given significant advances in robust optimization methods over the past several years, there is an opportunity to contribute to more effective uses of operations research in healthcare.



The project’s objective was to develop and test several different robust optimization approaches to staff-scheduling under uncertain demand. This was done by using an integer programming model as a test-bed to maximize employee satisfaction with scheduling assignments.


Project deliverables completed to-date include robust optimization model development and analysis under a variety of conditions, solution comparison to deterministic results, and a journal-ready paper on results and implications.

Our findings indicate that the robust optimization approach performs better than traditional scheduling processes when faced with unpredictable demand. Future work will focus on fuller implementation of the mathematical model and impact evaluation.

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