OptaPlanner is an AI constraint solver. It optimizes planning and scheduling problems, such as the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference Scheduling, Job Shop Scheduling, Bin Packing and many more. Every organization faces such challenges: assign a limited set of constrained resources (employees, assets, time and/or money) to provide products or services. OptaPlanner delivers more efficient plans, which reduce costs and improve service quality.
OptaPlanner is a lightweight, embeddable planning engine. It enables everyday Java™ programmers to solve optimization problems efficiently. It is also compatible with other JVM languages (such as Kotlin and Scala). Constraints apply on plain domain objects and can call existing code. There’s no need to input constraints as mathematical equations. Under the hood, OptaPlanner combines sophisticated AI optimization algorithms (such as Tabu Search, Simulated Annealing, Late Acceptance and other metaheuristics) with very efficient score calculation and other state-of-the-art constraint solving techniques.
OptaPlanner is open source software, released under the Apache License. It is written in 100% pure Java™, runs on any JVM and is available in the Maven Central repository too.
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examples/runExamples.sh
examples/runExamples.bat
To optimize a problem from Java™ code,
add the optaplanner-core jar and call Solver.solve():
// My domain specific class as input
MyRoster problem = ...;
SolverFactory<MyRoster> factory = SolverFactory
.createFromXmlResource(".../mySolverConfig.xml");
Solver<MyRoster> solver = factory.buildSolver();
// My domain specific class as output
MyRoster solution = solver.solve(problem);
for (MyShift shift : solution.getShifts()) {
// Each shift is now assigned to an employee
assertNotNull(shift.getEmployee());
}
Christopher Chianelli
Julian Cui
Radovan Synek
Musa Talluzi
Geoffrey De Smet
Musa Talluzi
Radovan Synek