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The heterogeneous multicrew scheduling and routing problem in road restoration

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  • Moreno, Alfredo
  • Alem, Douglas
  • Gendreau, Michel
  • Munari, Pedro

Abstract

This paper introduces the heterogeneous multicrew scheduling and routing problem (MCSRP) in road restoration. The MCSRP consists of identifying the schedule and route of heterogeneous crews that must perform the restoration of damaged nodes used in the paths to connect a source node to demand nodes in a network affected by extreme events. The objective is to minimize the accessibility time defined as the time that the demand nodes remain unconnected from the source node. The main contributions of the paper include three novel mathematical formulations that differ in the way of modeling the scheduling decisions and the synchronization of the crews, and the development of valid inequalities based on some particular properties of the problem. Additionally, we prove that the MCSRP is NP-hard. Extensive numerical experiments with randomly generated instances and a case study based on floods and landslides disasters in Rio de Janeiro, Brazil, are performed to assess the efficiency and applicability of our approach. In particular, we show that the valid inequalities significantly improve the solvability of the mathematical models. In terms of managerial implications, our results suggest that the incorporation of multiple crews helps to reduce the worst-case accessibility times across the demand nodes, thus providing more equitable solutions.

Suggested Citation

  • Moreno, Alfredo & Alem, Douglas & Gendreau, Michel & Munari, Pedro, 2020. "The heterogeneous multicrew scheduling and routing problem in road restoration," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 24-58.
    • Handle: RePEc:eee:transb:v:141:y:2020:i:c:p:24-58
    DOI: 10.1016/j.trb.2020.09.002
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    Cited by:

    1. Akbari, Vahid & Shiri, Davood & Sibel Salman, F., 2021. "An online optimization approach to post-disaster road restoration," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 1-25.
    2. Nabavi, S.M. & Vahdani, Behnam & Nadjafi, B. Afshar & Adibi, M.A., 2022. "Synchronizing victim evacuation and debris removal: A data-driven robust prediction approach," European Journal of Operational Research, Elsevier, vol. 300(2), pages 689-712.
    3. Ajam, Meraj & Akbari, Vahid & Salman, F. Sibel, 2022. "Routing multiple work teams to minimize latency in post-disaster road network restoration," European Journal of Operational Research, Elsevier, vol. 300(1), pages 237-254.
    4. Karen Castañeda & Omar Sánchez & Rodrigo F. Herrera & Guillermo Mejía, 2022. "Highway Planning Trends: A Bibliometric Analysis," Sustainability, MDPI, vol. 14(9), pages 1-33, May.
    5. Souza Almeida, Luana & Goerlandt, Floris & Pelot, Ronald, 2022. "Trends and gaps in the literature of road network repair and restoration in the context of disaster response operations," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    6. Hosseini, Yaser & Mohammadi, Reza Karami & Yang, Tony Y., 2024. "A comprehensive approach in post-earthquake blockage prediction of urban road network and emergency resilience optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Sina Nayeri & Zeinab Sazvar & Jafar Heydari, 2022. "A fuzzy robust planning model in the disaster management response phase under precedence constraints," Operational Research, Springer, vol. 22(4), pages 3571-3605, September.

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