incubator kie optaplanner

It solves complex combinatorial optimization problemsemployee scheduling, vehicle routing, bin packing, task assignment, and timetablingthat are computationally intractable with brute-force approaches.

By combining metaheuristic algorithms (Tabu Search, Simulated Annealing, Late Acceptance) with a declarative constraint model, OptaPlanner lets developers specify what a good solution looks like rather than programming how to find it.

The framework integrates natively with Java EE and Spring Boot ecosystems, making it a practical choice for enterprise teams building optimization features into existing applications.

Developers define their planning domain modelvehicles, employees, time slots, locationsalong with constraint rules specifying hard constraints (no double-booking) and soft constraints (minimize overtime, prefer seniority).

OptaPlanner then searches the solution space efficiently, returning near-optimal solutions within configurable time limits that are practical for real-time or near-real-time use cases.

Organizations running complex logistics operations, hospital staff scheduling, field service management, or educational timetabling at scale use OptaPlanner to automate decisions that would otherwise require manual coordination across dozens of competing constraints.

The project is backed by Red Hat (now IBM) as part of the Drools and jBPM business rules ecosystem, and now continues development as Timefold Solver.