IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v271y2024ics0925527324000331.html
   My bibliography  Save this article

Multi-level digital twin-driven kitting-synchronized optimization for production logistics system

Author

Listed:
  • Pan, Yanghua
  • Zhong, Ray Y.
  • Qu, Ting
  • Ding, Liqiang
  • Zhang, Jun

Abstract

The kit production mode is favored by many manufacturing companies due to its ability to meet customized and personalized customer demands through versatile combinations. However, it also poses significant challenges to production management. In the production process, companies need to consider not only horizontal coordination between upstream and downstream stages of the production logistics system (production, transportation, warehousing) but also vertical coordination of production progress for different products within the kit. Moreover, they must address various dynamic disturbances during production operations. This paper proposes an intelligent kit production management platform to tackle these challenges. It adopts a multi-level digital twin architecture as the platform control structure, a "two-stage three-level" synchronization mechanism as the qualitative control mechanism, and the target cascade method based on genetic algorithms as the quantitative solution foundation. This platform enables synchronized control of kit production in dynamic environments. Finally, through a case study simulation, the feasibility and effectiveness of the proposed approach are verified. The study also explores the impact of varying magnitudes of dynamics at different times of occurrence on the production logistics system, providing valuable insights for management practices.

Suggested Citation

  • Pan, Yanghua & Zhong, Ray Y. & Qu, Ting & Ding, Liqiang & Zhang, Jun, 2024. "Multi-level digital twin-driven kitting-synchronized optimization for production logistics system," International Journal of Production Economics, Elsevier, vol. 271(C).
  • Handle: RePEc:eee:proeco:v:271:y:2024:i:c:s0925527324000331
    DOI: 10.1016/j.ijpe.2024.109176
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527324000331
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2024.109176?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. Nozick, Linda K. & Turnquist, Mark A., 2001. "A two-echelon inventory allocation and distribution center location analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(6), pages 425-441, December.
    2. Zhang, Guoqing & Nishi, Tatsushi & Turner, Sarina D.O. & Oga, Keisuke & Li, Xindan, 2017. "An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches," Omega, Elsevier, vol. 68(C), pages 85-94.
    3. Bozer, Yavuz A. & McGinnis, Leon F., 1992. "Kitting versus line stocking: A conceptual framework and a descriptive model," International Journal of Production Economics, Elsevier, vol. 28(1), pages 1-19, November.
    4. Liu, Songsong & Papageorgiou, Lazaros G., 2013. "Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry," Omega, Elsevier, vol. 41(2), pages 369-382.
    5. Ting Qu & Matthias Thürer & Junhao Wang & Zongzhong Wang & Huan Fu & Congdong Li & George Q. Huang, 2017. "System dynamics analysis for an Internet-of-Things-enabled production logistics system," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2622-2649, May.
    6. 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.
    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. Zhang, Linda L. & Jiao, Roger J. & Huang, George & MacCarthy, Bart L., 2025. "Extended guest editorial: Smart product platforming in the industry 4.0 era and beyond," International Journal of Production Economics, Elsevier, vol. 280(C).
    2. Sarkar, Biswajit & Sao, Sreymouy & Ghosh, Santanu Kumar, 2025. "Smart production and photocatalytic ultraviolet (PUV) wastewater treatment effect on a textile supply chain management," International Journal of Production Economics, Elsevier, vol. 283(C).

    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. El Mehdi, Er Raqabi & Ilyas, Himmich & Nizar, El Hachemi & Issmaïl, El Hallaoui & François, Soumis, 2023. "Incremental LNS framework for integrated production, inventory, and vessel scheduling: Application to a global supply chain," Omega, Elsevier, vol. 116(C).
    2. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    3. Yang, Yuxiang & Goodarzi, Shadi & Jabbarzadeh, Armin & Fahimnia, Behnam, 2022. "In-house production and outsourcing under different emissions reduction regulations: An equilibrium decision model for global supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    4. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.
    5. Al-Husain, Raed & Khorramshahgol, Reza, 2020. "Incorporating analytical hierarchy process and goal programming to design responsive and efficient supply chains," Operations Research Perspectives, Elsevier, vol. 7(C).
    6. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    7. Daria Battini & Nils Boysen & Simon Emde, 2013. "Just-in-Time supermarkets for part supply in the automobile industry," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 24(2), pages 209-217, July.
    8. Cosmin Aron & Fabio Sgarbossa & Eric Ballot & Dmitry Ivanov, 2024. "Cloud material handling systems: a cyber-physical system to enable dynamic resource allocation and digital interoperability," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3815-3836, December.
    9. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    10. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2021. "Designing the data supply chain of a smart construction factory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 41-62, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    11. Xiaoyang Zhou & Yan Tu & Jing Han & Jiuping Xu & Xionghui Ye, 2017. "A Class of Level-2 Fuzzy Decision-Making Model with Expected Objectives and Chance Constraints: Application to Supply Chain Network Design," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 907-938, July.
    12. 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.
    13. Mustapha Sali & Evren Sahin, 2016. "Line feeding optimization for Just in Time assembly lines: an application to the automotive industry," Post-Print hal-01265041, HAL.
    14. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    15. Mohammed, Ahmed & Wang, Qian, 2017. "The fuzzy multi-objective distribution planner for a green meat supply chain," International Journal of Production Economics, Elsevier, vol. 184(C), pages 47-58.
    16. Roberto León & Pablo A. Miranda-Gonzalez & Francisco J. Tapia-Ubeda & Elias Olivares-Benitez, 2024. "An Inventory Service-Level Optimization Problem for a Multi-Warehouse Supply Chain Network with Stochastic Demands," Mathematics, MDPI, vol. 12(16), pages 1-20, August.
    17. Hepu Deng & Feng Luo & Santoso Wibowo, 2018. "Multi-Criteria Group Decision Making for Green Supply Chain Management under Uncertainty," Sustainability, MDPI, vol. 10(9), pages 1-13, September.
    18. Emenike, Scholastica N. & Falcone, Gioia, 2020. "A review on energy supply chain resilience through optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    19. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    20. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.

    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:eee:proeco:v:271:y:2024:i:c:s0925527324000331. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.