timefold-solver
by
TimefoldAI

Description: 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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Summary Information

Updated 1 hour ago
Added to GitGenius on May 2nd, 2023
Created on March 28th, 2023
Open Issues & Pull Requests: 104 (+0)
Number of forks: 215
Total Stargazers: 1,710 (+0)
Total Subscribers: 23 (+0)

Issue Activity (beta)

Open issues: 91
New in 7 days: 5
Closed in 7 days: 1
Avg open age: 374 days
Stale 30+ days: 67
Stale 90+ days: 63

Recent activity

Opened in 7 days: 3
Closed in 7 days: 1
Comments in 7 days: 0
Events in 7 days: 18

Top labels

  • component/docs (50)
  • process/needs triage (42)
  • language/python (40)
  • build and release (23)
  • resolution/invalid (16)
  • component/migration (12)
  • resolution/wontfix (11)
  • component/neighborhoods (9)

Repository Insights (GitGenius)

Median issue/PR response: 0.0 hours
Mean response time: 158.3 days
90th percentile: 574.5 days
Tracked items: 460

Most active contributors

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.

According to GitGenius activity tracking, the repository shows strong maintenance patterns with a median issue and pull request response latency of 0.0 hours, though the mean response time of 3824.4 hours reflects the reality that some items take longer to resolve. The most frequently tracked issue label is component/docs with 43 occurrences, suggesting ongoing documentation work and improvements. The language/python label appears 40 times among tracked issues, indicating interest in Python support or integration despite the project's primary Java focus. The process/needs triage label also appears 40 times, showing consistent incoming issues requiring initial assessment.

The project's core contributor base includes triceo with 1209 tracked events, Christopher-Chianelli with 140 events, and ge0ffrey with 116 events, demonstrating concentrated expertise and active maintenance. The repository shares overlapping contributors with spring-projects/spring-boot, spring-projects/spring-framework, and timefoldai/timefold-quickstarts, indicating integration with the broader Spring ecosystem and educational resources.

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.

timefold-solver
by
TimefoldAITimefoldAI/timefold-solver

Repository Details

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