Learn to build large language model applications: vector databases, langchain, fine tuning and prompt engineering. Learn more

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

Data Science Dojo
Umair Hasan

January 25

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.

Imagine you’re running a hospital. You have a lot of different things to worry about, like scheduling appointments, routing nurses, and managing inventory. You want to make sure that everything is running smoothly and efficiently, but that can be tough to do.

That’s where OR-Tools comes in. OR-Tools is a free and open-source software suite from Google that helps businesses solve complex optimization problems. It’s kind of like a magic wand for optimization.

OR-Tools can be used to optimize healthcare operations in a variety of ways. For example, it can help you:

  • Schedule appointments in a way that minimizes wait times for patients and maximizes efficiency for doctors.
  • Route nurses to patients in a way that minimizes travel time and maximizes the number of patients they can see.
  • Manage inventory levels to ensure that you always have enough supplies on hand, but not so many that they go to waste.

OR-Tools is a powerful tool, but it can be a bit complex to use. That’s why there are a lot of resources available to help you get started. There are tutorials, documentation, and even a community of users who are happy to help.

If you’re interested in using OR-Tools to optimize your healthcare operations, I encourage you to check out the resources below.

Here are some specific examples of how OR-Tools can be used to optimize healthcare operations:

  • Scheduling appointments: OR-Tools can be used to create a scheduling algorithm that takes into account factors such as patient preferences, doctor availability, and the complexity of the procedure. This can help to reduce wait times for patients and improve the efficiency of the clinic.
  • Routing nurses: OR-Tools can be used to create a routing algorithm that takes into account factors such as the location of patients, the skills of nurses, and the time of day. This can help to reduce travel time for nurses and maximize the number of patients they can see.
  • Managing inventory: OR-Tools can be used to create an inventory management system that takes into account factors such as the demand for supplies, the cost of supplies, and the lead time for supplies. This can help to ensure that the clinic has enough supplies on hand, but not so many that they go to waste.

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.


Improve your programming skills and learn all about R-Programming


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.  


Using OR-tools

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. 


DSD Sign
Written by 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.

Interested in writing for us? Apply here: Submit your guest post with us
Newsletters | Data Science Dojo
Up for a Weekly Dose of Data Science?

Subscribe to our weekly newsletter & stay up-to-date with current data science news, blogs, and resources.

Data Science Dojo | data science for everyone

Discover more from Data Science Dojo

Subscribe to get the latest updates on AI, Data Science, LLMs, and Machine Learning.