1. Proceedings
  2. I3M
  3. 2022
  4. EMSS
  5. 10.46354/i3m.2022.emss.009

Enhancing a Simulation-Based Allocator Software with Machine Learning and Genetic Algorithms for Improving the Gate Assignment Problem

  • a Alejandro Murrieta-Mendoza ,
  • b Roberto Salvador Félix Patrón ,
  • c Miguel Mújica Mota
  • a,b,c  Aviation Academy, Amsterdam University of Applied Sciences, Rhijnspoorplein 2, Amsterdam, 1091GC, The
    Netherlands
Cite as
Murrieta-Mendoza A., Felix Patron R.S., and Mujica Mota M. (2022).,Gate Assignment Problem Optimization Using a Simulator as a Model and Flight Delay Prediction. Proceedings of the 34th European Modeling & Simulation Symposium (EMSS 2022). , 009 . DOI: https://doi.org/10.46354/i3m.2022.emss.009

Abstract

Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently.

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