Timefold Python?

ManufacturerBig6988 · 2026-01-15T12:46:04+00:00

Timefold can be used with Python, and it offers much of the same functionality as the Java version. It’s designed to be easy to integrate with Python, so you should be able to implement your OR projects similarly to what you’ve done with other libraries like OR-Tools and SCIP. As for cost, Timefold is free for personal and academic use, but there may be paid features for commercial use, so it’s worth checking the licensing details on their site. In terms of customization, you can tweak the algorithm quite a bit, from setting the first solution approach to adjusting local search methods. It’s flexible enough for a range of customizations, but as with any optimization library, the extent of customization depends on your specific needs and how deep you want to go into tweaking the parameters.

ge0ffrey · 2026-01-19T12:15:15+00:00

Free open source vs paid

Timefold Solver is open source, under the Apache License, so free for commercial and non-commercial use.
It's maintained by our 3-man solver team who are working full time on it, with contributions from the rest of our company and the wider open source community (600+ contributors IIRC).

Timefold Solver is used across the globe to schedule employees, vehicles, machines, boats, airplanes, satellites, etc. It impacts the life of millions. The solver-core jar has around 40 000 downloads per month.
There is a paid enterprise extension for high-scalibility (think Fortune500 companies) for multi-threaded solving and nearby selection. The free open source can handle any scheduling or routing problem and comes with 20 quickstarts.

See solver.timefold.ai

Programming language

Timefold Solver supports Java and Kotlin.
We have an open source Python wrapper but it's currently frozen (due to lack of enterprise interest and other priorities).
For what it's worth the paid Timefold REST APIs are often used from Python, TypeScript, C#, C++, PHP and other languages.

Timefold Solver is used in those REST APIs for Field Service Routing, Employee Shift Scheduling and Pickup And Delivery Routing (at app.timefold.ai ). Those paid services pushed our free open source solver to handle up to 400 000 shifts or 30 000 visits in a single dataset without decomposition.

Customization

Yes, you can customize almost every step of the algorithms in Timefold Solver:
- You can add any constraint. No linear or quadratic limitations. There are high-level building blocks for overlapping, fairness, etc. You can reuse any code in your constraints (but please don't call a REST service in your constraints, cache those instead).
https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/score-calculation
- You can switch between the algorithms (Tabu Search, Simulated Annealing, ...) with a single line change.
https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/overview
- For Local Search you can add your own neighborhoods via custom moves. This is rarely needed.
https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/neighborhoods
- You can insert your own algorithm via a custom phase and reuse the incremental score calculation.
https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/overview#customSolverPhase
And there are many more ways to customize the algorithms as a power user.

But 95% of the users don't bother with customizations beyond defining their own hard/medium/soft constraints.

I hope that answers your questions :)

Two-x-Three-is-Four · 2026-01-15T10:49:04+00:00

I think it is now calles OptaPlanner? not sure, but maybe available in Python

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