OptaPlanner Online Training

OptaPlanner Training is a specialized program that teaches participants how to solve real-world optimization and scheduling problems using the open-source OptaPlanner engine. It focuses on constraint satisfaction, AI-based planning, and resource optimization. Learners gain hands-on experience in developing efficient solutions for workforce scheduling, vehicle routing, and resource allocation. The training equips professionals with the skills to integrate OptaPlanner into enterprise systems for smarter, automated business decision-making.

Module 1: Planner introduction

• What is OptaPlanner?
• What is a planning problem?
• Use Cases and examples

Module 2: Bin Packaging Problem Example

• Problem statement
• Problem size
• Domain model diagram
• Main method
• Solver configuration
• Domain model implementation
• Score configuration

Module 3: Travelling Salesman Problem (TSP)

• Problem statement
• Problem size
• Domain model
• Main method
• Chaining
• Solver configuration
• Domain model implementation
• Score configuration

Module 4: Planner configuration

• Overview
• Solver configuration
• Model your planning problem
• Use the Solver

Module 5: Score calculation

• Score terminology
• Choose a Score definition
• Calculate the Score
• Score calculation performance tricks
• Reusing the Score calculation outside the Solver

Module 6: Optimization algorithms

• Search space size in the real world
• Does Planner find the optimal solution?
• Architecture overview
• Optimization algorithms overview
• Which optimization algorithms should I use?
• SolverPhase
• Scope overview
• Termination
• SolverEventListener
• Custom SolverPhase

Module 7: Move and neighborhood selection

• Move and neighborhood introduction
• Generic Move Selectors
• Combining multiple MoveSelectors
• EntitySelector
• ValueSelector
• General Selector features
• Custom moves

Module 8: Construction heuristics

• First Fit
• Best Fit
• Advanced Greedy Fit
• the Cheapest insertion
• Regret insertion

Module 9: Local search

• Local Search concepts
• Hill Climbing (Simple Local Search)
• Tabu Search
• Simulated Annealing
• Late Acceptance
• Step counting hill climbing
• Late Simulated Annealing (experimental)
• Using a custom Termination, MoveSelector, EntitySelector, ValueSelector or Acceptor

Module 10: Evolutionary algorithms

• Evolutionary Strategies
• Genetic Algorithms

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