Citation
Optimisation of a Stagger Chart for Aviation Fleet Planning. In proceedings of the 7th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2015), 25 - 28 Aug 2015, Prague, Czech Republic, pages 579-589, 2015.
Paper
Abstract
Within the Commercial Aviation Industry, the maintenance planning process takes into account the number of spare engines available to meet a minimum spares level (usually contractual). This is to minimize the risk of disruption to aircraft, if engines are required to be replaced due to unplanned events. To ensure the efficient use of the spare engines pool while maximizing the engine time on wing between planned removals requires a forecast that is set for the predicted life of an operator’s specific aircraft type fleet. The engines are required to be refurbished at certain intervals which can be projected on to a forecast plan. The process of producing this plan by implementing the engine removals is known as Stagger. The aim of this research is to produce good quality Stagger Plans using evolutionary algorithms based upon data from an actual forecast. This paper presents our early attempts on modelling this problem and then solving it with Genetic Algorithms. Results show that the Stagger Plan produced by the GA reduced the number of weeks that the spare engines level had fallen below the minimum spare engine value when this was compared to the original forecast.
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Bibtex
@INPROCEEDINGS{2015-579-589-P, author = {R. Weedon and S. Ahmadi and M. Critchley },
title = {Optimisation of a Stagger Chart for Aviation Fleet Planning },
booktitle = {In proceedings of the 7th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2015), 25 - 28 Aug 2015, Prague, Czech Republic},
year = {2015},
editor = {Z. Hanzalek and G. Kendall and B. McCollum and P. Sucha},
pages = {579--589},
note = {Paper},
abstract = {Within the Commercial Aviation Industry, the maintenance planning process takes into account the number of spare engines available to meet a minimum spares level (usually contractual). This is to minimize the risk of disruption to aircraft, if engines are required to be replaced due to unplanned events. To ensure the efficient use of the spare engines pool while maximizing the engine time on wing between planned removals requires a forecast that is set for the predicted life of an operator’s specific aircraft type fleet. The engines are required to be refurbished at certain intervals which can be projected on to a forecast plan. The process of producing this plan by implementing the engine removals is known as Stagger. The aim of this research is to produce good quality Stagger Plans using evolutionary algorithms based upon data from an actual forecast. This paper presents our early attempts on modelling this problem and then solving it with Genetic Algorithms. Results show that the Stagger Plan produced by the GA reduced the number of weeks that the spare engines level had fallen below the minimum spare engine value when this was compared to the original forecast. },
owner = {Graham},
timestamp = {2017.01.16},
webpdf = {2015-579-589-P.pdf} }