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An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods

Author

Listed:
  • Nicholas C. Forrest

    (Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433)

  • Raymond R. Hill

    (Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433)

  • Phillip R. Jenkins

    (Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433)

Abstract

The U.S. Air Force has a severe shortage of pilots. The Air Force’s Pilot Training Next (PTN) program seeks a more efficient pilot-training environment emphasizing the use of virtual reality flight simulators alongside periodic real aircraft experience. The objective of the PTN program is to accelerate the training pace and progress in undergraduate pilot training. Currently, instructor pilots spend excessive time planning and scheduling flights. This research focuses on methods to autogenerate the planning of in-flight events using hybrid filtering and deep learning techniques. The resulting approach captures temporal trends of user-specific and program-wide student performance to recommend a feasible set of graded flight events for evaluation in students’ next training exercise to improve their progress toward fully qualified status.

Suggested Citation

  • Nicholas C. Forrest & Raymond R. Hill & Phillip R. Jenkins, 2022. "An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods," Interfaces, INFORMS, vol. 52(2), pages 198-209, March.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:2:p:198-209
    DOI: 10.1287/inte.2021.1099
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