Naval Fleet Schedule Optimization Using an Integer Linear Program

Megan Widmer, Michèle Fee, François-Alex Bourque

2024

Abstract

To inform decisions about future fleet planning, a way to model asset availability over time is needed. To accomplish this, a tool was developed that generates optimized fleet schedules from simplified operations and maintenance cycles. By repeating these cycles and offsetting them from asset to asset, it is possible to generate schedules that meet a set of fleet availability requirements. Target schedule characteristics were encoded in an Integer Linear Program (ILP) and solved using the PuLP python package with the COIN-OR branch and cut solver. To evaluate the effectiveness of the approach, fleet schedules for notional asset fleets were generated and compared qualitatively to those made using a genetic algorithm (GA) based tool that is currently in use. The ILP tool was found to produce schedules that met the requirements more consistently than the GA.

Paper Citation

in Harvard Style

Widmer M., Fee M. and Bourque F. (2024). Naval Fleet Schedule Optimization Using an Integer Linear Program. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 47-57. DOI: 10.5220/0012272200003639

in Bibtex Style

@conference{icores24,
author={Megan Widmer and Michèle Fee and François-Alex Bourque},
title={Naval Fleet Schedule Optimization Using an Integer Linear Program},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={47-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012272200003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - Naval Fleet Schedule Optimization Using an Integer Linear Program
SN - 978-989-758-681-1
AU - Widmer M.
AU - Fee M.
AU - Bourque F.
PY - 2024
SP - 47
EP - 57
DO - 10.5220/0012272200003639
PB - SciTePress