At Timefold, our mission has always been to simplify the complexity of planning optimization and make it accessible to a wider audience. We started this journey with our Java solver, empowering developers to tackle NP-hard problems with ease. Today, we are thrilled to announce a major milestone: the release of our Python solver.

Why Python?

Python is a powerful, versatile, and widely-used programming language, particularly popular among data scientists, analysts, and developers. Its simplicity and readability make it an ideal choice for translating real-world constraints into code. By introducing a Python solver, we are expanding our reach and making planning optimization accessible to a larger community of developers who prefer Python over Java.

Benefits of the Timefold Python Solver

1. Ease of Use

Our Python solver simplifies the process of planning optimization by allowing developers to write their planning model directly in Python. This eliminates the need for writing complex mathematical equations, making it easier to translate real-world constraints into code.

2. Accessibility

With Python being a common language in data science and machine learning communities, the Python solver opens up our technology to a new audience. Data scientists and analysts can now leverage the power of Timefold without needing to switch to a different programming language.

3. Integration with Existing Tools

Python’s extensive ecosystem includes a variety of libraries and frameworks that can seamlessly be put into a workflow with our solver. Whether you are using pandas for data manipulation, NumPy for numerical computations, or any other Python library, our solver fits right into your workflow.

4. Flexibility

The Python solver is designed to be flexible and adaptable, allowing you to model complex planning problems with ease. Whether you are dealing with employee scheduling, field service routing, or maintenance scheduling, our solver can handle it all.

Important note: Alpha Release

Please not that our Python solver is currently in Alpha. The API set is not stable and it will likely change until we reach the Beta Phase. We are actively seeking feedback to help us refine and improve the Python Solver.

Getting Started with the Python Solver

We understand that getting started with a new tool can be daunting, so we have prepared comprehensive documentation and tutorials to guide you through the process. Our documentation covers everything from installation and setup to advanced usage and integration with other tools.

Visit this URL to access our Python solver and start optimizing your planning problems today. Our team is dedicated to supporting you every step of the way, so don’t hesitate to reach out with any questions or feedback.

Join the Conversation

We believe in the power of community and are excited to engage with you. Share your experiences, ask questions, and connect with other users in our community forums. Follow us on social media for the latest updates, tips, and success stories.

Conclusion

The release of our Python solver marks a significant step forward in our mission to make planning optimization accessible to all. We are excited to see how our community will leverage this new tool to solve complex planning problems and drive innovation in their fields.

Install Timefold Python Solver on PyPi: https://pypi.org/project/timefold-solver/

About Timefold

Timefold is dedicated to providing cutting-edge solutions for planning optimization. Our solvers are designed to simplify complex NP-hard problems, making them accessible to developers across various industries. With a focus on innovation and community, we strive to be the go-to platform for planning optimization and education.

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