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Umair Hasan

January 25, 2023

Optimizing healthcare operations with Google OR-tools: A detailed case study in nurse scheduling

Google OR-Tools is a software suite for optimization and constraint programming. It includes several optimization algorithms such as linear programming, mixed-integer programming, and constraint programming. These algorithms can be used to solve a wide range of problems, including scheduling problems, such as nurse scheduling.

Challenges in the nurse scheduling system

In nurse scheduling, the goal is to assign nurses to shifts while satisfying a number of constraints, such as the minimum number of nurses required for each shift, the maximum number of consecutive shifts a nurse can work, and the nurses’ availability. OR-Tools can be used to model and solve this problem by representing the nurses and shifts as variables and the constraints as constraints on these variables.


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OR-Tools can also be used to solve other types of scheduling problems, such as vehicle routing, production scheduling, and sports schedules. Additionally, OR-Tools can be used to solve other types of optimization problems, such as portfolio optimization and resource allocation.

Google OR-Tools is open-source, cross-platform, and can be easily integrated with other programming languages like python, Java, C++, C# etc.

Read more about: How programming languages assist data analysts in reducing analysis bugs

Here’s an example of how you can use OR-Tools to solve a nurse scheduling problem: 


Example Code 


In this example, num_nurses and num_shifts are the number of nurses and the number of shifts, respectively. The variables shifts [i, j] are binary variables that represent whether nurse i is assigned to shift j. The constraints ensure that each shift is covered, each nurse works no more than max_shifts_per_week, and each nurse has at least one day off per week. The objective is to minimize the number of shifts worked.   

The above solution is a simplified problem, and based on the specific requirements of your problem, you may need to add more constraints or objective functions. This is a basic illustration of how or-tools could be used in nurse scheduling, and you can use it as a starting point to create your specific problem solution.  



It’s important to note that the nurse scheduling problem can be complex, and it’s likely that the example provided above will need to be modified and expanded upon to meet the specific requirements of your problem. However, the example serves as a starting point for how OR-Tools can be used to solve this type of problem.  

 Another important aspect to consider when using OR-Tools for nurse scheduling is that it’s a mathematical optimization approach and might not consider some constraint that are specific to the problem.

Furthermore, you should also consider the limitations of the mathematical models, such as robustness and scalability, when comparing them with other methods.  

 In any case, Google OR-Tools can be a valuable tool for solving nurse scheduling problems and can help you optimize your schedule and ensure that your nurses are working efficiently and effectively. 


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Umair Hasan

I am an Associate Data Engineer at Data Science Dojo, who uses tools on Azure Platform to transform and egress the data to bear fruitful outcome.
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