Combined Working Time Model Generation and Personnel Scheduling

Moser, Michaela; Nissen, Volker

doi:10.1007/978-3-642-12494-5_19

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these reasons, it is important to develop automated methods that can efficiently generate schedules that satisfy the given constraints. Automated methods to generate schedules have been studied and applied in many industries, including transportation [1,2,3], manufacturing [4,5,6], retail [7,8], and military [9,10]. Of special interest to a clinician scheduling problem are the approaches to scheduling nurses, who often work in shifts.…”

Section: Introductionmentioning

confidence: 99%

A flexible integer linear programming formulation for scheduling clinician on-call service in hospitals

Landsman¹,

Ma²,

Knight³

et al. 2019

Preprint

View full text Add to dashboard Cite

Scheduling of personnel in a hospital environment is vital to improving the service provided to patients and balancing the workload assigned to clinicians. Many approaches have been tried and successfully applied to generate efficient schedules in such settings. However, due to the computational complexity of the scheduling problem in general, most approaches resort to heuristics to find a non-optimal solution in a reasonable amount of time. We designed an integer linear programming formulation to find an optimal schedule in a clinical division of a hospital. Our formulation mitigates issues related to computational complexity by minimizing the set of constraints, yet retains sufficient flexibility so that it can be adapted to a variety of clinical divisions.We then conducted a case study for our approach using data from the Infectious Diseases division at St. Michael's Hospital in Toronto, Canada. We analyzed and compared the results of our approach to manually-created schedules at the hospital, and found improved adherence to departmental constraints and clinician preferences. We used simulated data to examine the sensitivity of the runtime of our linear program for various parameters and observed reassuring results, signifying the practicality and generalizability of our approach in different real-world scenarios.

show abstract