Guiding Metaheuristics Through Machine Learning Predictions for Dynamic Autonomous Ridesharing

Guiding Metaheuristics Through Machine Learning Predictions for Dynamic Autonomous Ridesharing

GERAD Research Center via YouTube Direct link

Optimization: Validation Phase

19 of 21

19 of 21

Optimization: Validation Phase

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Guiding Metaheuristics Through Machine Learning Predictions for Dynamic Autonomous Ridesharing

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  1. 1 Guiding metaheuristics trough machine learning predictions
  2. 2 Urban Mobility and Logistics
  3. 3 The Dial-a-Ride Problem (DARP)¹
  4. 4 The Electric Autonomous Dial-a-Ride Problem (e-ADARP)2
  5. 5 The (Dynamic) e-ADARP
  6. 6 Two-Phase Metaheuristic
  7. 7 Popular Operators and Metaheuristics
  8. 8 Machine Learning-Based Large Neighborhood Search
  9. 9 The Uber Dataset?
  10. 10 Event-Based Simulation Framework
  11. 11 Creating Examples (Labeled Dataset)
  12. 12 Statistics Example
  13. 13 Extracted Features
  14. 14 The Prediction Problem
  15. 15 The MLNS Algorithm
  16. 16 ML: Training Phase
  17. 17 ML: Performance Measures
  18. 18 ML: Features Importance
  19. 19 Optimization: Validation Phase
  20. 20 Expected difference in the objective function improvemen
  21. 21 Summary of Contributions

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