timefold solver

It tackles real-world scheduling and planning challenges where thousands of variables must be assigned subject to hard constraints (non-negotiable rules) and soft constraints (preferences to optimize), such as employee shift scheduling, vehicle routing, classroom timetabling, and maintenance job assignment.

The solver uses a constraint stream API that expresses business rules in readable Java or Kotlin code rather than mathematical formulations, making the constraint model accessible to developers who understand the business domain without requiring expertise in operations research.

Timefold's search engine applies Tabu Search, Simulated Annealing, and other metaheuristics to efficiently explore the solution space, returning near-optimal solutions within practical time limits even for problems too large for exact methods.

The solver also supports incremental solving, allowing it to re-optimize quickly when constraints or inputs change without restarting from scratch.

Organizations running complex logistics, healthcare staffing, manufacturing scheduling, or field service management at scale use Timefold Solver to automate planning decisions that would otherwise require dedicated dispatchers or schedulers.

Its Java/Kotlin ecosystem integration and Spring Boot support make it straightforward to embed in enterprise applications.

Timefold is the commercial and community successor to the Red Hat-backed OptaPlanner project, maintaining backward compatibility while extending the solver's capabilities and performance for demanding production scheduling workloads.