IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3777-d941356.html
   My bibliography  Save this article

An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem

Author

Listed:
  • Changjiu Li

    (School of Basic Science for Aviation, Naval Aviation University, Yantai 264001, China)

  • Yong Zhang

    (School of Basic Science for Aviation, Naval Aviation University, Yantai 264001, China
    Department of Precision Instrument, Tsinghua University, Beijing 100084, China)

  • Xichao Su

    (School of Basic Science for Aviation, Naval Aviation University, Yantai 264001, China)

  • Xinwei Wang

    (State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China)

Abstract

The maintenance of carrier-based aircraft is a critical factor restricting the availability of aircraft fleets and their capacity to sortie and operate. In this study, an aeronautical maintenance and repair task scheduling problem for carrier-based aircraft fleets in hangar bays is investigated to improve the maintenance efficiency of aircraft carrier hangar bays. First, the operational process of scheduling aeronautical maintenance tasks is systematically analyzed. Based on maintenance resource constraints and actual maintenance task requirements, a wave availability index and load balance index for the maintenance personnel are proposed for optimization. An aeronautical maintenance task scheduling model is formulated for carrier-based aircraft fleets. Second, model abstraction is performed to simulate a multi-skill resource-constrained project scheduling problem, and an improved teaching-learning-based optimization algorithm is proposed. The algorithm utilizes a serial scheduling generation scheme based on resource constraint advancement. Finally, the feasibility and effectiveness of the modeling and algorithm are verified by using simulation cases and algorithm comparisons. The improved teaching-learning-based optimization algorithm exhibits improved solution stability and optimization performance. This method provides theoretical support for deterministic aeronautical maintenance scheduling planning and reduces the burden associated with manual scheduling and planning.

Suggested Citation

  • Changjiu Li & Yong Zhang & Xichao Su & Xinwei Wang, 2022. "An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3777-:d:941356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3777/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/20/3777/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moukrim, Aziz & Quilliot, Alain & Toussaint, Hélène, 2015. "An effective branch-and-price algorithm for the Preemptive Resource Constrained Project Scheduling Problem based on minimal Interval Order Enumeration," European Journal of Operational Research, Elsevier, vol. 244(2), pages 360-368.
    2. Witteman, Max & Deng, Qichen & Santos, Bruno F., 2021. "A bin packing approach to solve the aircraft maintenance task allocation problem," European Journal of Operational Research, Elsevier, vol. 294(1), pages 365-376.
    3. Hartmann, Sonke & Kolisch, Rainer, 2000. "Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 127(2), pages 394-407, December.
    4. Shafiee, Mahmood & Chukova, Stefanka, 2013. "Maintenance models in warranty: A literature review," European Journal of Operational Research, Elsevier, vol. 229(3), pages 561-572.
    5. Kolisch, Rainer, 1996. "Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation," European Journal of Operational Research, Elsevier, vol. 90(2), pages 320-333, April.
    6. Panagiotis Tsarouhas & Maria Makrygianni, 2017. "A framework for maintenance and combat readiness management of a jet fighter aircraft," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1895-1909, November.
    7. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "A model enhancement heuristic for building robust aircraft maintenance personnel rosters with stochastic constraints," European Journal of Operational Research, Elsevier, vol. 246(2), pages 661-673.
    8. Deng, Qichen & Santos, Bruno F. & Curran, Richard, 2020. "A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 281(2), pages 256-273.
    9. Kiefer, Alexander & Schilde, Michael & Doerner, Karl F., 2018. "Scheduling of maintenance work of a large-scale tramway network," European Journal of Operational Research, Elsevier, vol. 270(3), pages 1158-1170.
    10. Deng, Qichen & Santos, Bruno F., 2022. "Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 299(3), pages 814-833.
    11. Liang, Zhe & Feng, Yuan & Zhang, Xiaoning & Wu, Tao & Chaovalitwongse, Wanpracha Art, 2015. "Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 238-259.
    12. Hartmann, Sönke & Kolisch, R., 2000. "Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 11180, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Nima Safaei & Dragan Banjevic & Andrew Jardine, 2011. "Workforce-constrained maintenance scheduling for military aircraft fleet: a case study," Annals of Operations Research, Springer, vol. 186(1), pages 295-316, June.
    14. Pellerin, Robert & Perrier, Nathalie & Berthaut, François, 2020. "A survey of hybrid metaheuristics for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 280(2), pages 395-416.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hartmann, Sönke & Briskorn, Dirk, 2022. "An updated survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 1-14.
    2. Deng, Qichen & Santos, Bruno F., 2022. "Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 299(3), pages 814-833.
    3. Dieter Debels & Mario Vanhoucke, 2007. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem," Operations Research, INFORMS, vol. 55(3), pages 457-469, June.
    4. Debels, Dieter & De Reyck, Bert & Leus, Roel & Vanhoucke, Mario, 2006. "A hybrid scatter search/electromagnetism meta-heuristic for project scheduling," European Journal of Operational Research, Elsevier, vol. 169(2), pages 638-653, March.
    5. Bouleimen, K. & Lecocq, H., 2003. "A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version," European Journal of Operational Research, Elsevier, vol. 149(2), pages 268-281, September.
    6. Anıl Can & Gündüz Ulusoy, 2014. "Multi-project scheduling with two-stage decomposition," Annals of Operations Research, Springer, vol. 217(1), pages 95-116, June.
    7. Qin, Yichen & Ng, Kam K.H., 2023. "Analysing the impact of collaborations between airlines and maintenance service company under MRO outsourcing mode: Perspective from airline's operations," Journal of Air Transport Management, Elsevier, vol. 109(C).
    8. Alfredo S. Ramos & Pablo A. Miranda-Gonzalez & Samuel Nucamendi-Guillén & Elias Olivares-Benitez, 2023. "A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic," Mathematics, MDPI, vol. 11(2), pages 1-25, January.
    9. Debels, Dieter & Vanhoucke, Mario, 2005. "A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem," Vlerick Leuven Gent Management School Working Paper Series 2005-8, Vlerick Leuven Gent Management School.
    10. Buddhakulsomsiri, Jirachai & Kim, David S., 2006. "Properties of multi-mode resource-constrained project scheduling problems with resource vacations and activity splitting," European Journal of Operational Research, Elsevier, vol. 175(1), pages 279-295, November.
    11. Zhenyuan Liu & Lei Xiao & Jing Tian, 2016. "An activity-list-based nested partitions algorithm for resource-constrained project scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4744-4758, August.
    12. V. Van Peteghem & M. Vanhoucke, 2008. "A Genetic Algorithm for the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/494, Ghent University, Faculty of Economics and Business Administration.
    13. Bernardo F. Almeida & Isabel Correia & Francisco Saldanha-da-Gama, 2018. "A biased random-key genetic algorithm for the project scheduling problem with flexible resources," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 283-308, July.
    14. Zheng, Huan-yu & Wang, Ling, 2015. "Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm," International Journal of Production Economics, Elsevier, vol. 164(C), pages 421-432.
    15. Luise-Sophie Hoffmann & Carolin Kellenbrink & Stefan Helber, 2020. "Simultaneous structuring and scheduling of multiple projects with flexible project structures," Journal of Business Economics, Springer, vol. 90(5), pages 679-711, June.
    16. Peteghem, Vincent Van & Vanhoucke, Mario, 2010. "A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 201(2), pages 409-418, March.
    17. Jens Poppenborg & Sigrid Knust, 2016. "A flow-based tabu search algorithm for the RCPSP with transfer times," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(2), pages 305-334, March.
    18. Kolisch, R. & Padman, R., 2001. "An integrated survey of deterministic project scheduling," Omega, Elsevier, vol. 29(3), pages 249-272, June.
    19. Moumene, Khaled & Ferland, Jacques A., 2009. "Activity list representation for a generalization of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 199(1), pages 46-54, November.
    20. Weglarz, Jan & Józefowska, Joanna & Mika, Marek & Waligóra, Grzegorz, 2011. "Project scheduling with finite or infinite number of activity processing modes - A survey," European Journal of Operational Research, Elsevier, vol. 208(3), pages 177-205, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3777-:d:941356. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.