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TimefoldAI/timefold-solver

The open source Solver AI for Java and Kotlin to optimize scheduling and routing. Solve the vehicle routing problem, employee rostering, task assignment,...

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TimefoldAI/timefold-solver has 1.5k+ stars and 200+ forks on GitHub, is written in Java, and was last pushed on Oct 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 37 minutes ago
Type:Library / SDKCategory(s):Scientific Computing & MathML Frameworks & LibrariesScience & Industry
Added to GitGenius on May 2nd, 2023
Created on March 28th, 2023
Open Issues & Pull Requests: 117 (+0)
GitHub issues: Enabled
Number of forks: 239
Total Stargazers: 1,834 (+0)
Total Subscribers: 23 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 1.5 hours
Mean response time: 59.6 days
90th percentile: 203.3 days
Tracked items: 344

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Maintainer activity

13 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

About 15% of issues opened in the past year have never received a reply. 87% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "component/docs" is answered fastest, typically in under an hour, while "language/python" waits about 10 days. 44% of tracked open issues have had no activity in three months. 61% of issues opened in the past year have been closed, leaving a working backlog.

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Issue Activity (beta)

Open issues: 100
New in 7 days: 6
Closed in 7 days: 2
Avg open age: 346 days
Stale 30+ days: 76
Stale 90+ days: 70

Recent activity

Opened in 7 days: 6
Closed in 7 days: 2
Comments in 7 days: 0
Events in 7 days: 11

Top labels

  • component/docs (57)
  • process/needs triage (48)
  • language/python (40)
  • build and release (23)
  • component/service (17)
  • resolution/invalid (16)
  • component/migration (12)
  • resolution/wontfix (11)

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Detailed Description

Timefold Solver is an open source constraint solver library for Java and Kotlin designed to optimize complex planning and scheduling problems. The project addresses real-world operational challenges such as vehicle routing, employee rostering, maintenance scheduling, task assignment, school timetabling, cloud optimization, conference scheduling, and job shop scheduling. Developed by the original OptaPlanner team, the solver applies artificial intelligence and constraint programming techniques to find efficient solutions to combinatorial optimization problems that would be computationally expensive or impossible to solve through brute force methods.

The repository is written primarily in Java and requires JDK 21 or higher for building from source. It is distributed through Maven Central, making it straightforward for developers to integrate into their projects. The codebase supports both Java and Kotlin, providing flexibility for teams using either language. The project maintains active community engagement through GitHub Discussions and a dedicated Discord server, indicating a commitment to user support and collaboration.

Timefold Solver is available in three editions: the Community Edition under Apache-2.0 license in this repository, plus commercial Plus and Enterprise editions. The Community Edition is a derivative work of OptaPlanner, forked on April 20, 2023, with every source file modified while maintaining the original Apache-2.0 licensing from its predecessor. This heritage provides a stable foundation built on years of optimization solver development.

The solver addresses the Vehicle Routing Problem, Capacitated Vehicle Routing Problem, and Vehicle Routing Problem with Time Windows, among other classic operations research challenges. It enables developers to model complex constraints and objectives, then leverages optimization algorithms to discover high-quality solutions efficiently. The project's classification across machine learning, optimization algorithms, constraint solving, and decision-making domains reflects its sophisticated approach to planning problems that require balancing multiple competing objectives and constraints.