OptaPlanner in Practice Schulung

Kurs Code

optaprac

Duration

21 hours (üblicherweise 3 Tage inklusive Pausen)

Overview

Dieser Kurs verwendet einen praktischen Ansatz, um OptaPlanner zu unterrichten. Es bietet den Teilnehmern die Werkzeuge, die benötigt werden, um die Grundfunktionen dieses Werkzeugs auszuführen.

Schulungsübersicht

Planner introduction

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

Bin Packaging Problem Example

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

Travelling Salesman Problem (TSP)

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

Planner configuration

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

Score calculation

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

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

Move and neighborhood selection

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

Construction heuristics

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

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

Evolutionary algorithms

  • Evolutionary Strategies
  • Genetic Algorithms

Hyperheuristics

Exact methods

  • Brute Force
  • Depth-first Search

Benchmarking and tweaking

  • Finding the best Solver configuration
  • Doing a benchmark
  • Benchmark report
  • Summary statistics
  • Statistics per dataset (graph and CSV)
  • Advanced benchmarking

Repeated planning

  • Introduction to repeated planning
  • Backup planning
  • Continuous planning (windowed planning)
  • Real-time planning (event based planning)

Drools

  • Short introduction to Drools
  • Writing Score Function in Drools

Integration

  • Overview
  • Persistent storage
  • SOA and ESB
  • Other environment

Erfahrungsberichte

★★★★★
★★★★★

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