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Course Outline
Introduction to Planner
- Understanding OptaPlanner
- Defining a planning problem
- Real-world use cases and illustrative examples
Bin Packing Problem Case Study
- Problem definition
- Complexity and scale of the problem
- Visual representation of the domain model
- Core execution entry point
- Configuring the Solver
- Implementing the domain model
- Defining score rules
Travelling Salesman Problem (TSP)
- Problem definition
- Complexity and scale of the problem
- Domain model structure
- Core execution entry point
- Implementing chains
- Configuring the Solver
- Implementing the domain model
- Defining score rules
Configuring the Planner
- Conceptual overview
- Solver setup
- Modelling the planning challenge
- Utilizing the Solver
Score Calculation
- Key terminology
- Selecting an appropriate score definition
- Computing the score
- Optimization techniques for score calculation performance
- Reusing score calculations independently of the Solver
Optimization Algorithms
- Real-world search space magnitude
- Does Planner guarantee optimal solutions?
- Architectural overview
- Survey of optimization algorithms
- Guidelines for selecting optimization algorithms
- SolverPhase
- Scope overview
- Termination conditions
- SolverEventListener
- Custom SolverPhase
Move and Neighborhood Selection
- Introduction to moves and neighborhoods
- Generic Move Selectors
- Combining multiple MoveSelectors
- EntitySelector
- ValueSelector
- Advanced Selector capabilities
- Custom move definitions
Construction Heuristics
- First Fit strategy
- Best Fit strategy
- Advanced Greedy Fit
- The Cheapest insertion method
- Regret insertion
Local Search
- Core concepts of Local Search
- Hill Climbing (Basic Local Search)
- Tabu Search
- Simulated Annealing
- Late Acceptance
- Hill Climbing with step counting
- Late Simulated Annealing (experimental feature)
- Implementing custom Termination, MoveSelector, EntitySelector, ValueSelector, or Acceptor
Evolutionary Algorithms
- Evolutionary Strategies
- Genetic Algorithms
Hyperheuristics
Exact Methods
- Brute Force approach
- Depth-first Search
Benchmarking and Tuning
- Identifying the optimal Solver configuration
- Conducting benchmarks
- Analyzing benchmark reports
- Reviewing summary statistics
- Detailed statistics per dataset (graphs and CSV)
- Advanced benchmarking techniques
Repeated Planning
- Introduction to repeated planning
- Backup planning strategies
- Continuous planning (windowed planning)
- Real-time planning (event-based planning)
Drools Integration
- Brief introduction to Drools
- Implementing Score Functions using Drools
System Integration
- Integration overview
- Managing persistent storage
- Integration with SOA and ESB
- Adapting to other environments
21 Hours
Testimonials (1)
The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.
