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Scheduling fighter aircraft maintenance with reinforcement learning

Published: 11 December 2011 Publication History

Abstract

This paper presents two problem formulations for scheduling the maintenance of a fighter aircraft fleet under conflict operating conditions. In the first formulation, the average availability of aircraft is maximized by choosing when to start the maintenance of each aircraft. In the second formulation, the availability of aircraft is preserved above a specific target level by choosing to either perform or not perform each maintenance activity. Both formulations are cast as semi-Markov decision problems (SMDPs) that are solved using reinforcement learning (RL) techniques. As the solution, maintenance policies dependent on the states of the aircraft are obtained. Numerical experiments imply that RL is a viable approach for considering conflict time maintenance policies. The obtained solutions provide knowledge of efficient maintenance decisions and the level of readiness that can be maintained by the fleet.

References

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Bertsekas, D. P., and J. N. Tsitsiklis. 1996. Neuro-Dynamic Programming. Belmont, Massachusetts: Athena Scientific.
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Gosavi, A. 2003. Simulation-based Optimization - Parametric Optimization Techniques and Reinforcement Learning. Boston, Massachusetts: Kluwer Acadmic Publishers.
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Gosavi, A., N. Bandla, and T. K. Das. 2002. "A Reinforcement Learning Approach to a Single Leg Airline Revenue Management Problem with Multiple Fare Classes and Overbooking". IIE Transactions 34 (9): 729--742.
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Kaelbling, L. P., M. L. Littman, and A. W. Moore. 1996. "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research 4:237--285.
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Kozanidis, G., G. Liberopoulos, and C. Pitsilkas. 2010. "Flight and Maintenance Planning of Military Aircraft for Maximum Fleet Availability". Military Operations Research 15 (1): 53--73.
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Mattila, V. 2007. "Flight Time Allocation for a Fleet of Aircraft through Reinforcement Learning". Poster presented at the 2007 Winter Simulation Conference, Washington, DC. Available via http://www.sal.tkk.fi/publications/ppt-files/cmat07.ppt {accessed September 12, 2011}.
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Mattila, V., and K. Virtanen. 2006. "Scheduling Periodic Maintenence of Aircraft through Simulation-Based Optimization". In Proceedings of the 47th Conference on Simulation and Modelling, edited by E. Juuso, 38--43. Helsinki, Finland: Finnish Society of Automation.
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Mattila, V., K. Virtanen, and T. Raivio. 2008. "Improving Maintenance Decision-Making in the Finnish Air Force through Simulation". Interfaces 38 (3): 187--201.
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Simao, H., and W. B. Powell. 2009. "Approximate Dynamic Programming for Management of High-Value Spare Parts". Journal of Manufacturing Technology Management 20 (2): 147--160.
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Sutton, R. S., and A. G. Barto. 1998. Reinforcement Learning: An Introduction. Cambridge, Massachusetts: MIT Press.
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Tang, L., G. Kacprzynski, J. Bock, and M. Begin. 2006. "An Intelligent Agent-Based Self-Evolving Maintenance and Operations Reasoning System". Paper presented at the 2006 IEEE Aerospace Conference, Big Sky, MT.
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Wu, T. T., W. B. Powell, and A. Whisman. 2009. "The Optimizing-Simulator: An Illustration Using the Military Airlift Problem". ACM Transactions on Modeling and Computer Simulation 19 (3): 14:1--14:31.

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        WSC '11: Proceedings of the Winter Simulation Conference
        December 2011
        4336 pages

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        Winter Simulation Conference

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        Published: 11 December 2011

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        December 11 - 14, 2011
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        WSC '11 Paper Acceptance Rate 203 of 270 submissions, 75%;
        Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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