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Using a Custom Termination, MoveSelector, EntitySelector, ValueSelector or Acceptor 11. Hill Climbing (Simple Local Search) 10.3.1. MoveIteratorFactory: Generate Custom Moves Just in Time 8. MoveListFactory: the Easy Way to Generate Custom Moves 7.7.5. Which Move Types Might be Missing in my Implementation? 7.7.2. Recommended Combinations of CacheType and SelectionOrder 7.6.4. SelectionOrder: Original, Sorted, Random, Shuffled or Probabilistic 7.6.3. CacheType: Create Moves Ahead of Time or Just In Time 7.6.2. subChainSwapMoveSelector (chained variables only) 7.3. subChainChangeMoveSelector (chained variables only) 7.2.7. tailChainSwapMoveSelector or 2-opt (chained variables only) 7.2.6. Subselecting of Entities, Values and Other Moves 7.2. Move and Neighborhood Introduction 7.1.1. Asynchronous Termination from Another Thread 6.10. UnimprovedTimeMillisSpentTermination 6.9.3. Power tweaking or default parameter values 6.7. Which Optimization Algorithms Should I Use? 6.6. Does Planner Find the Optimal Solution? 6.3. Explaining the Score: Using Score Calculation Outside the Solver 6. Other Score Calculation Performance Tricks 5.4.8. Avoid Calling Remote Services During Score Calculation 5.4.5. Incremental Score Calculation (with Deltas) 5.4.4. Average Calculation Count Per Second 5.4.3. Score Calculation Performance Tricks 5.4.1. Incremental Java Score Calculation 5.3.4. Avoid Floating Point Numbers in Score Calculation 5.2. Pareto Scoring (AKA Multi-objective Optimization Scoring) 5.1.6. Score Constraint Signum (Positive or Negative) 5.1.3. Logging Level: What is the Solver Doing? 4.4.5. Environment Mode: Are There Bugs in my Code? 4.4.4. Planning Problem and Planning Solution 4.4. Planning Value and Planning Value Range 4.3.6. Is This Class a Problem Fact or Planning Entity? 4.3.2. Investment asset class allocation (portfolio optimization) 4. Traveling Tournament Problem (TTP) 3.4.4. Employee Rostering (INRC 2010 - Nurse Rostering) 3.4.3. Exam Timetabling (ITC 2007 track 1 - Examination) 3.4.2. Hospital Bed Planning (PAS - Patient Admission Scheduling) 3.4. Machine Reassignment (Google ROADEF 2012) 3.3.3. Course Timetabling (ITC 2007 Track 3 - Curriculum Course Scheduling) 3.3.2. Traveling Salesman (TSP - Traveling Salesman Problem) 3.2.4. Use OptaPlanner with Maven, Gradle, Ivy, Buildr or ANT 1.4.4. Run the Examples in an IDE (IntelliJ, Eclipse, NetBeans) 1.4.3. A Planning Problem Has a Huge Search Space 1.4. A Planning Problem Has (Hard and Soft) Constraints 1.3.3. A Planning Problem is NP-complete or NP-hard 1.3.2.

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