An analytics-driven optimization framework for nurse scheduling under uncertainty
Abstract
Nurse scheduling in hospitals is a complex optimization problem influenced by uncertain staff absences, regulatory constraints, and the need to ensure fair workload distribution. Traditional scheduling models often overlook the unpredictability of last-minute absences, leading to operational disruptions and compromised care quality. This study introduces an analytics-driven optimization framework that integrates predictive modeling with mathematical optimization to create more resilient nurse schedules. First, a mixed-integer linear programming (MILP) model incorporates historical absence data to compute shift-level criticality scores, enabling proactive reinforcement of high-risk shifts through targeted allocation of surplus nurses. Second, a real-time reassignment heuristic supported by a virtual overflow unit dynamically reallocates nurses during unforeseen absences to maintain coverage continuity. Third, a two-part hurdle model predicts shift-level absence probabilities, facilitating realistic robustness testing under simulated absenteeism scenarios. Using real hospital data, extensive computational experiments validate the framework’s effectiveness, demonstrating significant improvements in absence coverage and operational reliability compared to conventional scheduling methods.
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BibTeX
@article{chorfi2026analyticsdriven,
title = {{An analytics-driven optimization framework for nurse scheduling under uncertainty}},
author = {Hadil Chorfi and Yassine Ouazene and Yasmine Alaouchiche and Farouk Yalaoui},
journal = {Healthcare Analytics},
year = {2026},
month = nov,
doi = {10.1016/j.health.2026.100462},
url = {https://utt.hal.science/hal-05852348v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - An analytics-driven optimization framework for nurse scheduling under uncertainty
AU - Hadil Chorfi
AU - Yassine Ouazene
AU - Yasmine Alaouchiche
AU - Farouk Yalaoui
PY - 2026
DA - 2026/11/04
JO - Healthcare Analytics
DO - 10.1016/j.health.2026.100462
UR - https://utt.hal.science/hal-05852348v1
AB - Nurse scheduling in hospitals is a complex optimization problem influenced by uncertain staff absences, regulatory constraints, and the need to ensure fair workload distribution. Traditional scheduling models often overlook the unpredictability of last-minute absences, leading to operational disruptions and compromised care quality. This study introduces an analytics-driven optimization framework that integrates predictive modeling with mathematical optimization to create more resilient nurse schedules. First, a mixed-integer linear programming (MILP) model incorporates historical absence data to compute shift-level criticality scores, enabling proactive reinforcement of high-risk shifts through targeted allocation of surplus nurses. Second, a real-time reassignment heuristic supported by a virtual overflow unit dynamically reallocates nurses during unforeseen absences to maintain coverage continuity. Third, a two-part hurdle model predicts shift-level absence probabilities, facilitating realistic robustness testing under simulated absenteeism scenarios. Using real hospital data, extensive computational experiments validate the framework’s effectiveness, demonstrating significant improvements in absence coverage and operational reliability compared to conventional scheduling methods.
SN - 2772-4425
ER -