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Joint optimization of workforce scheduling and routing for restoring a disrupted critical infrastructure

Author

Listed:
  • Li, Yulong
  • Zhang, Chi
  • Jia, Chuanzhou
  • Li, Xiaodong
  • Zhu, Yimin

Abstract

Once a critical infrastructure is disrupted, it needs to be restored as soon as possible in order to minimize the influence of the disruptions on the economic development and social well-being of a society. Components of critical infrastructures are usually geographically distributed and the time required to travel between disrupted components cannot be ignored when making restoration plans. As a result, the problem of optimally routing restoration workforce needs to be taken into account when scheduling the tasks of restoring disrupted components. To deal with this problem, this research proposes a joint optimization approach to simultaneously determine the time of restoring each disrupted component, the assignment of restoration tasks to the available teams of restoration technicians, and the routes of each team. To deal with the complexity of the proposed problem, ant colony optimization algorithm is employed with necessary adaptations to solve it.

Suggested Citation

  • Li, Yulong & Zhang, Chi & Jia, Chuanzhou & Li, Xiaodong & Zhu, Yimin, 2019. "Joint optimization of workforce scheduling and routing for restoring a disrupted critical infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832017314801
    DOI: 10.1016/j.ress.2019.106551
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    References listed on IDEAS

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    Cited by:

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    3. Bešinović, Nikola & Ferrari Nassar, Raphael & Szymula, Christopher, 2022. "Resilience assessment of railway networks: Combining infrastructure restoration and transport management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Xu, Min & Ouyang, Min & Hong, Liu & Mao, Zijun & Xu, Xiaolin, 2022. "Resilience-driven repair sequencing decision under uncertainty for critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. 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).
    6. Bellè, Andrea & Abdin, Adam F. & Fang, Yi-Ping & Zeng, Zhiguo & Barros, Anne, 2023. "A resilience-based framework for the optimal coupling of interdependent critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. 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).
    8. Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. 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).
    10. Poulin, Craig & Kane, Michael B., 2021. "Infrastructure resilience curves: Performance measures and summary metrics," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. 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).

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