Machinery and other types of equipment in use (such as elevators, escalators, airplanes, trains, …) tend to deteriorate over time, requiring regular maintenance. Depending on how often each machine requires care, maintenance scheduling decides when and which skilled engineer visits each machine. Optimizing this planning problem with a constraint solver AI saves costs by reducing both too early and too late maintenance, while adhering to other constraints such as skill requirements, employee availability and the geographic location of machines.
OptaPlanner is the leading Open Source Java™ AI constraint solver to optimize maintenance scheduling, adhering to skill, capacity, SLA’s and other constraints.
OptaPlanner is a lightweight, embeddable planning engine. It enables normal 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 Artificial Intelligence 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.
Run the maintenance scheduling quickstart.
examples/runExamples.sh
examples/runExamples.bat
Read a
Quick Start
guide
or git clone
optaplanner-quickstarts.
Want to talk to the experts? Red Hat offers certified binaries with enterprise consulting. Contact optaplanner-info for more information.
Paul Brown
Michal Tomčo
Jiří Locker
Geoffrey De Smet
Walter Medvedeo
Christopher Chianelli
Christopher Chianelli
Julian Cui
Geoffrey De Smet
Geoffrey De Smet
Geoffrey De Smet
Geoffrey De Smet
Geoffrey De Smet
Geoffrey De Smet
Maintenance scheduling