Constraint Match Total Best Score Over Time Statistic (Graph And CSV) 14.6.3. Statistic Per Single Benchmark (Graph And CSV) 14.6.1. Memory Use Statistic (Graph And CSV) 14.6. Move Count Per Step Statistic (Graph And CSV) 14.5.7. Best Solution Mutation Over Time Statistic (Graph And CSV) 14.5.6. Calculate Count Per Second Statistic (Graph And CSV) 14.5.5. Step Score Over Time Statistic (Graph And CSV) 14.5.4. Best Score Over Time Statistic (Graph And CSV) 14.5.3. Statistic Per Dataset (Graph And CSV) 14.5.1. Best Score Per Time Spent Summary (Graph) 14.5. Time Spent Scalability Summary (Graph) 14.4.9. Time Spent Summary (Graph And Table) 14.4.8. Average Calculation Count Summary (Graph and Table) 14.4.7. Worst Score Difference Percentage (ROI) Summary (Graph and Table) 14.4.6. Winning Score Difference Summary (Graph And Table) 14.4.5. Best Score Distribution Summary (Graph) 14.4.4. Best Score Scalability Summary (Graph) 14.4.3. Best Score Summary (Graph And Table) 14.4.2. Write The Output Solution Of Benchmark Runs 14.2.7. Benchmark Blueprint: A Predefined Configuration 14.2.6. SolutionFileIO: Input And Output Of Solution Files 14.2.4. Add Dependency On optaplanner-benchmark 14.2.2. 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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