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

Franco Peschiera (), Robert Dell, Johannes Royset, Alain Haït, Nicolas Dupin and Olga Battaïa
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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

OR Spectrum: Quantitative Approaches in Management, 2021, vol. 43, issue 3, No 3, 635-664

Abstract: 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.

Keywords: Maintenance; Flight; Aircraft; Military; Mixed integer programming; Supervised learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00291-020-00591-z

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