IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v18y2017i1d10.1007_s10799-015-0223-7.html
   My bibliography  Save this article

An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly

Author

Listed:
  • Siqing Shan

    (Beihang University)

  • Zhongjun Hu

    (Beihang University)

  • Zhilian Liu

    (Beihang University)

  • Jihong Shi

    (Beihang University)

  • Li Wang

    (Beihang University)

  • Zhuming Bi

    (Indiana University Purdue University Fort Wayne)

Abstract

Scheduling of aircraft assembling activities is proven as a non-deterministic polynomial-time hard problem; which is also known as a typical resource-constrained project scheduling problem (RCPSP). Not saying the scheduling of the complex assemblies of an aircraft, even for a simple product requiring a limited number of assembling operations, it is difficult or even infeasible to obtain the best solution for its RCPSP. To obtain a high quality solution in a short time frame, resource constraints are treated as the objective function of an RCPSP, and an adaptive genetic algorithm (GA) is proposed to solve demand-driven scheduling problems of aircraft assembly. In contrast to other GA-based heuristic algorithms, the proposed algorithm is innovative in sense that: (1) it executes a procedure with two crossovers and three mutations; (2) its fitness function is demand-driven. In the formulation of RCPSP for aircraft assembly, the optimizing criteria are the utilizations of working time, space, and operators. To validate the effectiveness of the proposed algorithm, two encoding approaches have been tested with the real data of demand.

Suggested Citation

  • Siqing Shan & Zhongjun Hu & Zhilian Liu & Jihong Shi & Li Wang & Zhuming Bi, 2017. "An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly," Information Technology and Management, Springer, vol. 18(1), pages 41-53, March.
    • Handle: RePEc:spr:infotm:v:18:y:2017:i:1:d:10.1007_s10799-015-0223-7
    DOI: 10.1007/s10799-015-0223-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-015-0223-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10799-015-0223-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Neumann, K. & Zimmermann, J., 2000. "Procedures for resource leveling and net present value problems in project scheduling with general temporal and resource constraints," European Journal of Operational Research, Elsevier, vol. 127(2), pages 425-443, December.
    2. Lova, Antonio & Tormos, Pilar & Cervantes, Mariamar & Barber, Federico, 2009. "An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes," International Journal of Production Economics, Elsevier, vol. 117(2), pages 302-316, February.
    3. 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.
    4. Valls, Vicente & Ballestin, Francisco & Quintanilla, Sacramento, 2008. "A hybrid genetic algorithm for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 495-508, March.
    5. Bautista, Joaquin & Pereira, Jordi, 2007. "Ant algorithms for a time and space constrained assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2016-2032, March.
    6. 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.
    7. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
    8. Elloumi, Sonda & Fortemps, Philippe, 2010. "A hybrid rank-based evolutionary algorithm applied to multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 31-41, August.
    9. Jinfeng Li & Kanliang Wang & Lida Xu, 2009. "Chameleon based on clustering feature tree and its application in customer segmentation," Annals of Operations Research, Springer, vol. 168(1), pages 225-245, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boda Liu & Bin Yang & Jianzhuang Xiao & Dayu Zhu & Binghan Zhang & Zhichen Wang & Miaosi Dong, 2021. "Review of Optimization Dynamically Applied in the Construction and the Application Potential of ICT," Sustainability, MDPI, vol. 13(10), pages 1-18, May.
    2. Xumai Qi & Dongdong Zhang & Hu Lu & Rupeng Li, 2023. "A GA-Based Scheduling Method for Civil Aircraft Distributed Production with Material Inventory Replenishment Consideration," Mathematics, MDPI, vol. 11(14), pages 1-25, July.

    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. Luis F. Machado-Domínguez & Carlos D. Paternina-Arboleda & Jorge I. Vélez & Agustin Barrios-Sarmiento, 2021. "A memetic algorithm to address the multi-node resource-constrained project scheduling problem," Journal of Scheduling, Springer, vol. 24(4), pages 413-429, August.
    2. Van Peteghem, Vincent & Vanhoucke, Mario, 2014. "An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances," European Journal of Operational Research, Elsevier, vol. 235(1), pages 62-72.
    3. 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.
    4. Geiger, Martin Josef, 2017. "A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 256(3), pages 729-741.
    5. Fündeling, C.-U. & Trautmann, N., 2010. "A priority-rule method for project scheduling with work-content constraints," European Journal of Operational Research, Elsevier, vol. 203(3), pages 568-574, June.
    6. Kellenbrink, Carolin & Helber, Stefan, 2015. "Scheduling resource-constrained projects with a flexible project structure," European Journal of Operational Research, Elsevier, vol. 246(2), pages 379-391.
    7. Coelho, José & Vanhoucke, Mario, 2011. "Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers," European Journal of Operational Research, Elsevier, vol. 213(1), pages 73-82, August.
    8. Abdollah Arasteh, 2020. "Considering Project Management Activities for Engineering Design Groups," SN Operations Research Forum, Springer, vol. 1(4), pages 1-29, December.
    9. Nima Zoraghi & Aria Shahsavar & Babak Abbasi & Vincent Peteghem, 2017. "Multi-mode resource-constrained project scheduling problem with material ordering under bonus–penalty policies," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 49-79, April.
    10. Cédric Verbeeck & Vincent Peteghem & Mario Vanhoucke & Pieter Vansteenwegen & El-Houssaine Aghezzaf, 2017. "A metaheuristic solution approach for the time-constrained project scheduling problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 353-371, March.
    11. T Wauters & K Verbeeck & G Vanden Berghe & P De Causmaecker, 2011. "Learning agents for the multi-mode project scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 281-290, February.
    12. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
    13. 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.
    14. 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.
    15. Tseng, Lin-Yu & Lin, Ya-Tai, 2010. "A hybrid genetic algorithm for no-wait flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 128(1), pages 144-152, November.
    16. Hongbo Li & Erik Demeulemeester, 2016. "A genetic algorithm for the robust resource leveling problem," Journal of Scheduling, Springer, vol. 19(1), pages 43-60, February.
    17. 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.
    18. Ramírez Palencia, Alberto E. & Mejía Delgadillo, Gonzalo E., 2012. "A computer application for a bus body assembly line using Genetic Algorithms," International Journal of Production Economics, Elsevier, vol. 140(1), pages 431-438.
    19. Luis F. Machado-Domínguez & Carlos D. Paternina-Arboleda & Jorge I. Vélez & Agustín Barrios-Sarmiento, 2022. "An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 221-248, July.
    20. He, Naihui & Zhang, David Z. & Yuce, Baris, 2022. "Integrated multi-project planning and scheduling - a multiagent approach," European Journal of Operational Research, Elsevier, vol. 302(2), pages 688-699.

    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:spr:infotm:v:18:y:2017:i:1:d:10.1007_s10799-015-0223-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.