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A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem

Author

Listed:
  • Franco Peschiera

    (Université de Toulouse)

  • Robert Dell

    (Naval Postgraduate School)

  • Johannes Royset

    (Naval Postgraduate School)

  • Alain Haït

    (Université de Toulouse)

  • Nicolas Dupin

    (Université Paris-Saclay)

  • Olga Battaïa

    (KEDGE Business School)

Abstract

This paper deals with the long-term Military Flight and Maintenance Planning problem. In order to solve this problem efficiently, we propose a new solution approach based on a new Mixed Integer Program and the use of both valid cuts generated on the basis of initial conditions and learned cuts based on the prediction of certain characteristics of optimal or near-optimal solutions. These learned cuts are generated by training a Machine Learning model on the input data and results of 5000 instances. This approach helps to reduce the solution time with little losses in optimality and feasibility in comparison with alternative matheuristic methods. The obtained experimental results show the benefit of a new way of adding learned cuts to problems based on predicting specific characteristics of solutions.

Suggested Citation

  • Franco Peschiera & Robert Dell & Johannes Royset & Alain Haït & Nicolas Dupin & Olga Battaïa, 2021. "A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 635-664, September.
    • Handle: RePEc:spr:orspec:v:43:y:2021:i:3:d:10.1007_s00291-020-00591-z
    DOI: 10.1007/s00291-020-00591-z
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    References listed on IDEAS

    as
    1. Gavranis, Andreas & Kozanidis, George, 2015. "An exact solution algorithm for maximizing the fleet availability of a unit of aircraft subject to flight and maintenance requirements," European Journal of Operational Research, Elsevier, vol. 242(2), pages 631-643.
    2. Adamo, Tommaso & Ghiani, Gianpaolo & Guerriero, Emanuela & Manni, Emanuele, 2017. "Automatic instantiation of a Variable Neighborhood Descent from a Mixed Integer Programming model," Operations Research Perspectives, Elsevier, vol. 4(C), pages 123-135.
    3. Rockafellar, R.T. & Royset, J.O., 2010. "On buffered failure probability in design and optimization of structures," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 499-510.
    4. M. W. P. Savelsbergh, 1994. "Preprocessing and Probing Techniques for Mixed Integer Programming Problems," INFORMS Journal on Computing, INFORMS, vol. 6(4), pages 445-454, November.
    5. El-Ghazali Talbi, 2016. "Combining metaheuristics with mathematical programming, constraint programming and machine learning," Annals of Operations Research, Springer, vol. 240(1), pages 171-215, May.
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    Cited by:

    1. Robin Dee & Armin Fügenschuh & George Kaimakamis, 2021. "The Unit Re-Balancing Problem," Mathematics, MDPI, vol. 9(24), pages 1-19, December.
    2. Cha, Guesik & Park, Junseok & Moon, Ilkyeong, 2023. "Military aircraft flight and maintenance planning model considering heterogeneous maintenance tasks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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