IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i3d10.1007_s10845-024-02340-3.html
   My bibliography  Save this article

Intelligent factory layout design framework through collaboration between optimization, simulation, and digital twin

Author

Listed:
  • Seon Han Choi

    (Ewha Womans University)

  • Byeong Soo Kim

    (Seoul National University of Science and Technology)

Abstract

In the era of the fourth industrial revolution, various internet and communications technologies (ICTs) are being applied to manufacturing systems. Based on these technologies, many companies utilize smart manufacturing systems to optimize the design and operation of their lines and to diagnose failures. To build and/or improve production lines, various computer-aided engineering (CAE) tools such as optimization solvers and simulation tools for validation are required. In addition, experts depend on their experience or utilize numerous trial and error processes, implying that a large time investment is required obtain the best layout design, without any guarantee that the result is in fact the best. Therefore, the paper proposes an integrated intelligent layout design framework that automatically derives an optimal layout according the requirements of the layout. The proposed framework uses mixed integer linear programming, simulation-based optimization, and digital twin to perform processes such as assembly line balancing, cell/buffer optimization, and layout planning sequentially and repeatedly to derive an optimal layout. By applying this, it is possible to automatically derive the optimal layout design considering limited resources and physical constraints. In addition, it can contribute to improving productivity and work efficiency at manufacturing sites.

Suggested Citation

  • Seon Han Choi & Byeong Soo Kim, 2025. "Intelligent factory layout design framework through collaboration between optimization, simulation, and digital twin," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1547-1561, March.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02340-3
    DOI: 10.1007/s10845-024-02340-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02340-3
    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/s10845-024-02340-3?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. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    2. Miralles, Cristobal & Garcia-Sabater, Jose Pedro & Andres, Carlos & Cardos, Manuel, 2007. "Advantages of assembly lines in Sheltered Work Centres for Disabled. A case study," International Journal of Production Economics, Elsevier, vol. 110(1-2), pages 187-197, October.
    3. Araújo, Felipe F.B. & Costa, Alysson M. & Miralles, Cristóbal, 2012. "Two extensions for the ALWABP: Parallel stations and collaborative approach," International Journal of Production Economics, Elsevier, vol. 140(1), pages 483-495.
    4. Daria Leiber & David Eickholt & Anh-Tu Vuong & Gunther Reinhart, 2022. "Simulation-based layout optimization for multi-station assembly lines," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 537-554, February.
    5. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    6. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    7. Mariem Besbes & Marc Zolghadri & Roberta Costa Affonso & Faouzi Masmoudi & Mohamed Haddar, 2020. "A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A* search," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 615-640, March.
    8. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    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. Shimin Liu & Pai Zheng & Jinsong Bao, 2024. "Digital Twin-based manufacturing system: a survey based on a novel reference model," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2517-2546, August.
    2. Jielin Chen & Shuang Li & Hanwei Teng & Xiaolong Leng & Changping Li & Rendi Kurniawan & Tae Jo Ko, 2025. "Digital twin-driven real-time suppression of delamination damage in CFRP drilling," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1459-1476, February.
    3. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    4. Sebnem Demirkol Akyol & Adil Baykasoğlu, 2019. "A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 557-573, February.
    5. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    6. Mao, Zhaofang & Sun, Yiting & Fang, Kan & Huang, Dian & Zhang, Jiaxin, 2024. "Balancing and scheduling of assembly line with multi-type collaborative robots," International Journal of Production Economics, Elsevier, vol. 271(C).
    7. Weifei Hu & Jinyi Shao & Qing Jiao & Chuxuan Wang & Jin Cheng & Zhenyu Liu & Jianrong Tan, 2023. "A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2943-2961, October.
    8. Sana Bouajaja & Najoua Dridi, 2017. "A survey on human resource allocation problem and its applications," Operational Research, Springer, vol. 17(2), pages 339-369, July.
    9. Maksim Dli & Andrei Puchkov & Valery Meshalkin & Ildar Abdeev & Rail Saitov & Rinat Abdeev, 2020. "Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach," Energies, MDPI, vol. 13(21), pages 1-13, November.
    10. Tianjie Fu & Shimin Liu & Peiyu Li, 2025. "Digital twin-driven smelting process management method for converter steelmaking," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2749-2765, April.
    11. Battaïa, Olga & Delorme, Xavier & Dolgui, Alexandre & Hagemann, Johannes & Horlemann, Anika & Kovalev, Sergey & Malyutin, Sergey, 2015. "Workforce minimization for a mixed-model assembly line in the automotive industry," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 489-500.
    12. Benedek Kiss & Jose Dinis Silvestre & Rita Andrade Santos & Zsuzsa Szalay, 2021. "Environmental and Economic Optimisation of Buildings in Portugal and Hungary," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    13. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    14. Adriano Cordisco & Riccardo Melloni & Lucia Botti, 2022. "Sustainable Circular Economy for the Integration of Disadvantaged People: A Preliminary Study on the Reuse of Lithium-Ion Batteries," Sustainability, MDPI, vol. 14(13), pages 1-15, July.
    15. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    16. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    17. Kokaraki, Nikoleta & Hopfe, Christina J. & Robinson, Elaine & Nikolaidou, Elli, 2019. "Testing the reliability of deterministic multi-criteria decision-making methods using building performance simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 991-1007.
    18. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    19. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    20. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.

    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:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02340-3. 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.