A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem
Authors
Peschiera, Franco
Dell, Robert
Royset, Johannes
Haït, Alain
Dupin, Nicolas
Battaïa, Olga
Subjects
maintenance
flight
aircraft
military
mixed integer programming
supervised learning
flight
aircraft
military
mixed integer programming
supervised learning
Advisors
Date of Issue
2020
Date
Publisher
Language
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.
Type
Preprint
Description
Series/Report No
Department
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
French Defense Procurement Agency of the French Ministry of Defense (DGA).
Format
30 p.
Citation
Peschiera, Franco, et al. "A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem." (2020)
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.