OptaPlanner in Practice Schulung

Kurs Code

optaprac

Dauer

21 Stunden (üblicherweise 3 Tage inklusive Pausen)

Übersicht

This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.

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

★★★★★
★★★★★

Buchungen, Preise und Anfragen

Privater Klassenraum

Ab 6050EUR

Öffentlicher Fernkurs

Ab 5250EUR (49)

Öffentlicher Klassenraum

Standort Datum Kurspreis (Fernkurs/Schulungsraum)
Stuttgart2018-10-31 09:30 5250 EUR / 6050 EUR
Düsseldorf2018-10-31 09:30 5250 EUR / 6050 EUR
Bremen2018-10-31 09:30 5250 EUR / 6050 EUR
Dresden2018-11-06 09:30 5250 EUR / 6050 EUR
Hannover2018-11-07 09:30 5250 EUR / 6050 EUR
Berlin 2018-11-14 09:30 5250 EUR / 6050 EUR
Köln2018-11-19 09:30 5250 EUR / 6050 EUR
Frankfurt am Main2018-12-05 09:30 5250 EUR / 6050 EUR
München2018-12-05 09:30 5250 EUR / 6050 EUR
Nürnberg2018-12-10 09:30 5250 EUR / 6050 EUR
Leipzig2018-12-12 09:30 5250 EUR / 6050 EUR
Potsdam2018-12-17 09:30 5250 EUR / 6050 EUR
Erfurt2018-12-20 09:30 5250 EUR / 6050 EUR
Hamburg2018-12-31 09:30 5250 EUR / 6050 EUR
Stuttgart2018-12-31 09:30 5250 EUR / 6050 EUR
Hannover2019-01-01 09:30 5250 EUR / 6050 EUR
Düsseldorf2019-01-02 09:30 5250 EUR / 6050 EUR
Bremen2019-01-02 09:30 5250 EUR / 6050 EUR
Dresden2019-01-02 09:30 5250 EUR / 6050 EUR
Berlin 2019-01-09 09:30 5250 EUR / 6050 EUR
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Sonderangebote

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Managing Configuration with Ansible Frankfurt am Main Mi, 2018-11-21 09:30 4455EUR / 5255EUR
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