IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v310y2022i1d10.1007_s10479-021-04184-z.html
   My bibliography  Save this article

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

Author

Listed:
  • Silvestro Vespoli

    (Università degli Studi di Napoli Federico II)

  • Guido Guizzi

    (Università degli Studi di Napoli Federico II)

  • Elisa Gebennini

    (Universitas Mercatorum)

  • Andrea Grassi

    (Università degli Studi di Napoli Federico II)

Abstract

Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors.

Suggested Citation

  • Silvestro Vespoli & Guido Guizzi & Elisa Gebennini & Andrea Grassi, 2022. "A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture," Annals of Operations Research, Springer, vol. 310(1), pages 201-221, March.
  • Handle: RePEc:spr:annopr:v:310:y:2022:i:1:d:10.1007_s10479-021-04184-z
    DOI: 10.1007/s10479-021-04184-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04184-z
    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/s10479-021-04184-z?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. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    2. Fogliatto, Flavio S. & da Silveira, Giovani J.C. & Borenstein, Denis, 2012. "The mass customization decade: An updated review of the literature," International Journal of Production Economics, Elsevier, vol. 138(1), pages 14-25.
    3. Byeongwoo Jeon & Joo-Sung Yoon & Jumyung Um & Suk-Hwan Suh, 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1837-1859, December.
    4. Dmitry Ivanov & Boris Sokolov & Weiwei Chen & Alexandre Dolgui & Frank Werner & Semyon Potryasaev, 2021. "A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints," IISE Transactions, Taylor & Francis Journals, vol. 53(1), pages 21-38, January.
    5. Stelian Brad & Mircea Murar & Emilia Brad, 2018. "Design of smart connected manufacturing resources to enable changeability, reconfigurability and total-cost-of-ownership models in the factory-of-the-future," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2269-2291, March.
    6. Carlos Paternina-Arboleda & Jairo Montoya-Torres & Milton Acero-Dominguez & Maria Herrera-Hernandez, 2008. "Scheduling jobs on a k-stage flexible flow-shop," Annals of Operations Research, Springer, vol. 164(1), pages 29-40, November.
    7. Dolgui, Alexandre & Kovalev, Sergey & Kovalyov, Mikhail Y. & Nossack, Jenny & Pesch, Erwin, 2014. "Minimizing setup costs in a transfer line design problem with sequential operation processing," International Journal of Production Economics, Elsevier, vol. 151(C), pages 186-194.
    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. Muhammad Rahies Khan & Amir Manzoor, 2021. "Application and Impact of New Technologies in the Supply Chain Management During COVID-19 Pandemic: A Systematic Literature Review," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 277-292.
    2. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    3. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Bindu K. Nambiar & Kartikeya Bolar, 2023. "Factors influencing customer preference of cardless technology over the card for cash withdrawals: an extended technology acceptance model," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 58-73, March.
    5. Na Liu & Pui-Sze Chow & Hongshan Zhao, 2020. "Challenges and critical successful factors for apparel mass customization operations: recent development and case study," Annals of Operations Research, Springer, vol. 291(1), pages 531-563, August.
    6. Huimin Liu & Yupeng Shi & Xuze Yang & Wentao Zhang, 2023. "The Role of Business Environment and Digital Government in Mitigating Supply Chain Vulnerability—Evidence from the COVID-19 Shock," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    7. Murphree, Michael & Anderson, John (Andy), 2018. "Countering Overseas Power in Global Value Chains: Information Asymmetries and Subcontracting in the Plastics Industry," Journal of International Management, Elsevier, vol. 24(2), pages 123-136.
    8. Sandrin, Enrico & Trentin, Alessio & Forza, Cipriano, 2018. "Leveraging high-involvement practices to develop mass customization capability: A contingent configurational perspective," International Journal of Production Economics, Elsevier, vol. 196(C), pages 335-345.
    9. Wen, Xin & Choi, Tsan-Ming & Chung, Sai-Ho, 2019. "Fashion retail supply chain management: A review of operational models," International Journal of Production Economics, Elsevier, vol. 207(C), pages 34-55.
    10. Gedas Baranauskas & Agota Giedrė Raišienė & Renata Korsakienė, 2020. "Mapping the Scientific Research on Mass Customization Domain: A Critical Review and Bibliometric Analysis," JRFM, MDPI, vol. 13(9), pages 1-20, September.
    11. Natália Barbosa, 2024. "Artificial Intelligence and exporting performance:Firm-level evidence from Portugal," GEE Papers 183, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Sep 2024.
    12. Young Won Park & Junjiro Shintaku, 2022. "Sustainable Human–Machine Collaborations in Digital Transformation Technologies Adoption: A Comparative Case Study of Japan and Germany," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    13. Liu, Weihua & Wang, Qian & Mao, Qiaomei & Wang, Shuqing & Zhu, Donglei, 2015. "A scheduling model of logistics service supply chain based on the mass customization service and uncertainty of FLSP’s operation time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 189-215.
    14. Syed Abdul Rehman Khan & Muhammad Waqas & Xue Honggang & Naveed Ahmad & Zhang Yu, 2022. "Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic," Operations Management Research, Springer, vol. 15(3), pages 1115-1133, December.
    15. Alikhani, Reza & Ranjbar, Amirhossein & Jamali, Amir & Torabi, S. Ali & Zobel, Christopher W., 2023. "Towards increasing synergistic effects of resilience strategies in supply chain network design," Omega, Elsevier, vol. 116(C).
    16. Frank Wiengarten & Prakash J. Singh & Brian Fynes & Ali Nazarpour, 2017. "Impact of mass customization on cost and flexiblity performances: the role of social capital," Operations Management Research, Springer, vol. 10(3), pages 137-147, December.
    17. A. Arrighetti & F. Landini, 2018. "Eterogeneità delle imprese e stagnazione del capitalismo italiano," Economics Department Working Papers 2018-EP01, Department of Economics, Parma University (Italy).
    18. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    19. 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.
    20. Anna Adamik & Michał Nowicki & Andrius Puksas, 2022. "Energy Oriented Concepts and Other SMART WORLD Trends as Game Changers of Co-Production—Reality or Future?," Energies, MDPI, vol. 15(11), pages 1-38, June.

    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:annopr:v:310:y:2022:i:1:d:10.1007_s10479-021-04184-z. 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.