IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v52y2020i7p732-750.html
   My bibliography  Save this article

Joint optimization of maintenance planning and workforce routing for a geographically distributed networked infrastructure

Author

Listed:
  • Chuanzhou Jia
  • Chi Zhang

Abstract

It is paramount to perform timely and appropriate maintenance actions on networked infrastructures, such as power transmission, transportation, telecommunications, and so forth, in order to ensure their reliability in satisfying the prescribed demand required by the economic development and social well-being of a society. For this purpose, the time of travelling between the components to be maintained needs to be considered, as the components of a real-world infrastructure are usually geographically widely distributed. To address this problem, we propose a holistic bi-objective optimization approach for the joint optimization of maintenance planning and workforce routing for a networked infrastructure, in order to determine a practical maintenance plan that can simultaneously maximize its reliability and minimize the incurred cost. To deal with the complexity of the proposed problem, we develop a Two-level Pareto Simulated Annealing algorithm to approximate the Pareto-optimal solutions of the proposed problem. Finally, two numerical examples are employed to illustrate the ability of the proposed approach in dealing with the maintenance optimization problem of a geographically distributed networked infrastructure.

Suggested Citation

  • Chuanzhou Jia & Chi Zhang, 2020. "Joint optimization of maintenance planning and workforce routing for a geographically distributed networked infrastructure," IISE Transactions, Taylor & Francis Journals, vol. 52(7), pages 732-750, July.
  • Handle: RePEc:taf:uiiexx:v:52:y:2020:i:7:p:732-750
    DOI: 10.1080/24725854.2019.1647478
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24725854.2019.1647478
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/24725854.2019.1647478?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.

    Citations

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


    Cited by:

    1. Agnieszka Blokus & Przemysław Dziula, 2021. "Relations of Imperfect Repairs to Critical Infrastructure Maintenance Costs," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
    2. Si, Guojin & Xia, Tangbin & Li, Yaping & Wang, Dong & Chen, Zhen & Pan, Ershun & Xi, Lifeng, 2023. "Resource allocation and maintenance scheduling for distributed multi-center renewable energy systems considering dynamic scope division," Renewable Energy, Elsevier, vol. 217(C).
    3. Urbani, Michele & Brunelli, Matteo & Punkka, Antti, 2023. "An approach for bi-objective maintenance scheduling on a networked system with limited resources," European Journal of Operational Research, Elsevier, vol. 305(1), pages 101-113.
    4. Si, Guojin & Xia, Tangbin & Gebraeel, Nagi & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2022. "A reliability-and-cost-based framework to optimize maintenance planning and diverse-skilled technician routing for geographically distributed systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Jafar-Zanjani, Hamed & Zandieh, Mostafa & Sharifi, Mani, 2022. "Robust and resilient joint periodic maintenance planning and scheduling in a multi-factory network under uncertainty: A case study," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Akl, Amany M. & El Sawah, Sondoss & Chakrabortty, Ripon K. & Turan, Hasan Hüseyin, 2022. "A Joint Optimization of Strategic Workforce Planning and Preventive Maintenance Scheduling: A Simulation–Optimization Approach," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Manco, Pasquale & Rinaldi, Marta & Caterino, Mario & Fera, Marcello & Macchiaroli, Roberto, 2022. "Maintenance management for geographically distributed assets: a criticality-based approach," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. Jia, Chuanzhou & Zhang, Chi & Li, Yan-Fu & Li, Quan-Lin, 2023. "Joint pre- and post-disaster planning to enhance the resilience of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:uiiexx:v:52:y:2020:i:7:p:732-750. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.