JBoss.orgCommunity Documentation
- 1. Planner introduction
- 1.1. What is OptaPlanner?
- 1.2. What is a planning problem?
- 1.2.1. A planning problem is NP-complete
- 1.2.2. A planning problem has (hard and soft) constraints
- 1.2.3. A planning problem has a huge search space
- 1.3. Download and run the examples
- 1.3.1. Get the release zip and run the examples
- 1.3.2. Run the examples in an IDE (IntelliJ, Eclipse, NetBeans)
- 1.3.3. Use OptaPlanner with Maven, Gradle, Ivy, Buildr or ANT
- 1.3.4. Build OptaPlanner from source
- 1.4. Status of OptaPlanner
- 1.5. Compatibility
- 1.6. Questions, issues and blog
- 2. Quick start
- 2.1. Cloud balancing tutorial
- 2.1.1. Problem statement
- 2.1.2. Problem size
- 2.1.3. Domain model diagram
- 2.1.4. Main method
- 2.1.5. Solver configuration
- 2.1.6. Domain model implementation
- 2.1.7. Score configuration
- 2.1.8. Beyond this tutorial
- 3. Use cases and examples
- 3.1. Examples overview
- 3.2. Basic examples
- 3.2.1. N queens
- 3.2.2. Cloud balancing
- 3.2.3. Traveling salesman (TSP - Traveling salesman problem)
- 3.2.4. Manners 2009
- 3.3. Real examples
- 3.3.1. Course timetabling (ITC 2007 track 3 - Curriculum course scheduling)
- 3.3.2. Machine reassignment (Google ROADEF 2012)
- 3.3.3. Vehicle routing
- 3.3.4. Hospital bed planning (PAS - Patient admission scheduling)
- 3.4. Difficult examples
- 3.4.1. Exam timetabling (ITC 2007 track 1 - Examination)
- 3.4.2. Employee rostering (INRC 2010 - Nurse rostering)
- 3.4.3. Sport scheduling (TTP - Traveling tournament problem)
- 4. Planner configuration
- 4.1. Overview
- 4.2. Solver configuration
- 4.2.1. Solver configuration by XML file
- 4.2.2. Solver configuration by Java API
- 4.3. Model your planning problem
- 4.3.1. Is this class a problem fact or planning entity?
- 4.3.2. Problem fact
- 4.3.3. Planning entity and planning variables
- 4.3.4. Planning value and planning value ranges
- 4.3.5. Planning problem and planning solution
- 4.4. Use the Solver
- 4.4.1. The Solver interface
- 4.4.2. Solving a problem
- 4.4.3. Environment mode: Are there bugs in my code?
- 4.4.4. Logging level: What is the Solver doing?
- 5. Score calculation
- 5.1. Score terminology
- 5.1.1. What is a score?
- 5.1.2. Score constraint signum (positive or negative)
- 5.1.3. Score constraint weight
- 5.1.4. Score level
- 5.1.5. Pareto scoring (AKA multi-objective optimization scoring)
- 5.1.6. Combining score techniques
- 5.1.7. The Score interface
- 5.2. Choose a Score definition
- 5.2.1. SimpleScore
- 5.2.2. HardSoftScore (recommended)
- 5.2.3. HardMediumSoftScore
- 5.2.4. BendableScore
- 5.2.5. Implementing a custom Score
- 5.3. Calculate the Score
- 5.3.1. Score calculation types
- 5.3.2. Simple Java score calculation
- 5.3.3. Incremental Java score calculation
- 5.3.4. Drools score calculation
- 5.3.5. Detecting invalid scores
- 5.4. Score calculation performance tricks
- 5.4.1. Overview
- 5.4.2. Average calculation count per second
- 5.4.3. Incremental score calculation (with delta's)
- 5.4.4. Avoid calling remote services during score calculation
- 5.4.5. Unused constraint
- 5.4.6. Build-in hard constraint
- 5.4.7. Other performance tricks
- 5.4.8. Score trap
- 5.4.9. stepLimit benchmark
- 5.5. Reusing the score calculation outside the Solver
- 6. Optimization algorithms
- 6.1. Search space size in the real world
- 6.2. Does Planner find the optimal solution?
- 6.3. Architecture overview
- 6.4. Optimization algorithms overview
- 6.5. Which optimization algorithms should I use?
- 6.6. SolverPhase
- 6.7. Scope overview
- 6.8. Termination
- 6.8.1. TimeMillisSpendTermination
- 6.8.2. ScoreAttainedTermination
- 6.8.3. StepCountTermination
- 6.8.4. UnimprovedStepCountTermination
- 6.8.5. Combining multiple Terminations
- 6.8.6. Asynchronous termination from another thread
- 6.9. SolverEventListener
- 6.10. Custom SolverPhase
- 7. Move and neighborhood selection
- 7.1. Move and neighborhood introduction
- 7.1.1. What is a Move?
- 7.1.2. What is a MoveSelector?
- 7.1.3. Subselecting of entities, values and other moves
- 7.2. General Selector features
- 7.2.1. CacheType: Create moves ahead of time or Just In Time
- 7.2.2. SelectionOrder: original, sorted, random, shuffled or probabilistic
- 7.2.3. Recommended combinations of CacheType and SelectionOrder
- 7.2.4. Filtered selection
- 7.2.5. Sorted selection
- 7.2.6. Probabilistic selection
- 7.3. Generic MoveSelectors
- 7.3.1. changeMoveSelector
- 7.3.2. swapMoveSelector
- 7.3.3. pillarSwapMoveSelector
- 7.3.4. subChainChangeMoveSelector
- 7.3.5. subChainSwapMoveSelector
- 7.4. Combining multiple MoveSelectors
- 7.4.1. unionMoveSelector
- 7.4.2. cartesianProductMoveSelector
- 7.5. EntitySelector
- 7.6. ValueSelector
- 7.7. Custom moves
- 7.7.1. Which move types might be missing in my implementation?
- 7.7.2. Custom moves introduction
- 7.7.3. The interface Move
- 7.7.4. MoveListFactory: the easy way to generate custom moves
- 7.7.5. MoveIteratorFactory: generate custom moves just in time
- 7.7.6. Move generation through DRL
- 8. Construction heuristics
- 8.1. Overview
- 8.2. First Fit
- 8.2.1. Algorithm description
- 8.2.2. Configuration
- 8.3. First Fit Decreasing
- 8.3.1. Algorithm description
- 8.3.2. Configuration
- 8.4. Best Fit
- 8.4.1. Algorithm description
- 8.4.2. Configuration
- 8.5. Best Fit Decreasing
- 8.5.1. Algorithm description
- 8.5.2. Configuration
- 8.6. Cheapest insertion
- 8.6.1. Algorithm description
- 8.6.2. Configuration
- 8.7. Regret insertion
- 8.7.1. Algorithm description
- 8.7.2. Configuration
- 9. Local search
- 9.1. Overview
- 9.2. Hill climbing (simple local search)
- 9.2.1. Algorithm description
- 9.3. Tabu search
- 9.3.1. Algorithm description
- 9.4. Simulated annealing
- 9.4.1. Algorithm description
- 9.5. Late acceptance
- 9.5.1. Algorithm description
- 9.6. About neighborhoods, moves and steps
- 9.6.1. Move generation tips
- 9.6.2. A step
- 9.6.3. Getting stuck in local optima
- 9.7. Deciding the next step
- 9.7.1. Acceptor
- 9.7.2. Forager
- 9.8. Using a custom Termination, MoveSelector, EntitySelector, ValueSelector or Acceptor
- 10. Evolutionary algorithms
- 10.1. Overview
- 10.2. Evolutionary Strategies
- 10.3. Genetic algorithms
- 11. Hyperheuristics
- 11.1. Overview
- 12. Exact methods
- 12.1. Overview
- 12.2. Brute Force
- 12.2.1. Algorithm description
- 12.2.2. Configuration
- 12.3. Depth-first search
- 12.3.1. Algorithm description
- 12.3.2. Configuration
- 13. Benchmarking and tweaking
- 13.1. Finding the best Solver configuration
- 13.2. Doing a benchmark
- 13.2.1. Adding the extra dependency
- 13.2.2. Building and running a PlannerBenchmark
- 13.2.3. ProblemIO: input and output of Solution files
- 13.2.4. Writing the output solution of the benchmark runs
- 13.2.5. Warming up the HotSpot compiler
- 13.3. Benchmark report
- 13.3.1. HTML report
- 13.3.2. Summary statistics
- 13.3.3. Statistic per data set (graph and CSV)
- 13.3.4. Ranking the Solvers
- 13.4. Advanced benchmarking
- 13.4.1. Benchmarking performance tricks
- 13.4.2. Template based benchmarking and matrix benchmarking
- 14. Repeated planning
- 14.1. Introduction to repeated planning
- 14.2. Backup planning
- 14.3. Continuous planning (windowed planning)
- 14.3.1. Immovable planning entities
- 14.4. Real-time planning (event based planning)