Nurse scheduling is a complex problem. The act of assigning each nurse to a specific shift for each day of a scheduling horizon, while ensuring to fulfill the demand of the operating rooms (ORs) is very time consuming. Current methods used in practice often develop solutions that are suboptimal, resulting in low nurse utilization, low nurse satisfaction, patient delays, or overtime pay for nurses. In this paper we propose an approach that utilizes simulation, simulation-based optimization, and mathematical programming as tools for solving complex nurse scheduling problems. We discuss how the simulation-based optimization generates multiple alternative shift plans, defined as the number of nurses required to work each shift. The simulation, which replicates the processes within an OR wing of a hospital, uses each shift plan for an extended period of time with variable patient arrival and surgery time. The results of each run of the simulation provide performance metrics of cost and patient lateness to the simulation-based optimization model, a way to compare the alternative shift plans. Next, an assignment model developed using mathematical programming uses a chosen shift plan as an input and aim to assign each specific nurse to the shifts described by that plan. The objective of this model is to maximize the percentage of time that nurses have the same specialty required by the surgeries and also maximize nurse satisfaction. Different objective functions, parameter weights, and time horizons are applied to the model to evaluate its ability to fit the needs of different ORs. This also allows for alternative shift assignments to be compared against each other, giving the scheduler options when developing the final schedule. The result is a scheduling tool that attempts to minimize cost, maximize specialty match, and maximize satisfaction.

Library of Congress Subject Headings

Manpower planning--Data processing; Health services administration--Data processing; Mathematical optimization

Publication Date


Document Type


Student Type


Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)


Michael E. Kuhl

Advisor/Committee Member

Rubén Proaño


Physical copy available from RIT's Wallace Library at HF5549.5.M3 T66 2015


RIT – Main Campus

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