# 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, maintenance scheduling and other planning problems.

Repository: https://github.com/TimefoldAI/timefold-solver
Canonical: https://ross.abutalabs.com/products/timefold-solver
Homepage: http://solver.timefold.ai
Language: Java
License: Apache-2.0
License Family: permissive
Topics: ai, artificial-intelligence, cvrp, employee-scheduling, vehicle-routing, vehicle-routing-problem, vrp, vrptw, maintenance-scheduling, planning-algorithms, operations-research, resource-allocation, solver-library, java, optimization-algorithms, solver, kotlin, constraint-programming, constraint-solver
Last push: 2026-08-26T11:26:25+00:00

## Health v2 (maintenance only)
Score: 96/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 97, longevity 89
- inputs: {"age_days": 1254, "days_push": 7, "days_rel": 21, "gap_med": 26.0, "n_releases_24m": 31}
- flags: no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1765, forks 226 (observed 2026-08-28T04:05:33.466277+00:00)

## What it is
Timefold Solver is an open-source, embeddable constraint satisfaction and optimization engine for Java and Kotlin, continuing Red Hat's OptaPlanner. It solves combinatorial planning problems such as vehicle routing, employee rostering, task assignment, and timetabling using constraint-based scoring and heuristic search algorithms.

## Use cases
- solve vehicle routing problems for delivery fleets
- generate employee shift schedules respecting skills and labor rules
- assign tasks to workers or machines by priority and affinity
- build school or conference timetables
- schedule maintenance for facilities and machinery
- solve bin packing and resource allocation problems
- optimize job shop scheduling to reduce makespan

## When to choose
- you need an embeddable JVM optimization library for scheduling or routing
- your planning problem has hard and soft constraints with weighted scoring
- you want a mature, well-documented solver with active maintenance
- you're building custom planning models in Java or Kotlin

## When to avoid
- you need a managed REST API without writing code (use Timefold Platform models instead)
- your project is not on the JVM
- you need general-purpose ML or data science tooling rather than constraint optimization

## Facets
- artifact type: library
- maturity: active
- function: simulation, sdk, developer-tools
- domain: logistics, hr, performance
- platform: jvm
- tags: constraint-solver, vehicle-routing, employee-scheduling, timetabling, optimization, operations-research, optaplanner-successor, algorithms, automation

## Member repositories
- TimefoldAI/timefold-solver (main) score 96

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:33.466277+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:26:36.289251+00:00, confidence not recorded.
  - homepage: http://solver.timefold.ai (fetched 2026-08-29T11:05:03.601503+00:00, sha 5ba9cf87ac61)
  - site_page: https://docs.timefold.ai/timefold-solver/latest/introduction (fetched 2026-08-29T11:05:03.603764+00:00, sha 21b3856d820a)
  - site_page: https://docs.timefold.ai/field-service-routing/latest/introduction (fetched 2026-08-29T11:05:03.605602+00:00, sha f41742a0dcec)
  - site_page: https://docs.timefold.ai/employee-shift-scheduling/latest/introduction (fetched 2026-08-29T11:05:03.607358+00:00, sha 6c9e91575520)
  - site_page: https://docs.timefold.ai/pickup-delivery-routing/latest/introduction (fetched 2026-08-29T11:05:03.609233+00:00, sha a427ee8f5fe8)
  - site_page: https://docs.timefold.ai/job-scheduling/latest/introduction (fetched 2026-08-29T11:05:03.621599+00:00, sha e9e523ab746e)
  - site_page: https://timefold.ai/about-us (fetched 2026-08-29T11:05:03.654870+00:00, sha d00b814a1367)
  - site_page: https://docs.timefold.ai/ (fetched 2026-08-29T11:05:03.679754+00:00, sha 787f0704c33e)
- Data as of 2026-08-30T08:39:29.467469+00:00.