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Optimizing strategies for post-disaster reconstruction of school systems

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  • Alisjahbana, Irene
  • Graur, Andrei
  • Lo, Irene
  • Kiremidjian, Anne

Abstract

School systems are often significantly impacted after disasters, forcing students to transfer to neighboring schools and resulting in worse learning conditions. In this paper, we take an optimization approach to reconstructing schools as soon as possible to improve student well-being. We focus on the scenario in which a central decision-making authority is responsible for the reconstruction of the school system. Limited construction crew availability means that schools must be reconstructed in sequence, and we study the order in which schools should be reconstructed to minimize the time and distance students travel to neighboring schools. We formulate a greedy algorithm through intuitive examples and discuss advantageous properties of this algorithm. We prove that finding the optimal reconstruction order with multiple construction crews is NP-hard, but our greedy algorithm finds a reasonable solution in polynomial time. We then extend the greedy algorithm to a typical post-disaster scenario incorporating school capacities, multiple construction crews, and temporary schools. Finally, we empirically evaluate our greedy policy in a case study using data after the 2018 Lombok Earthquakes and demonstrate that our greedy algorithm performs significantly better than policies typically considered by decision-makers. Our results demonstrate how theoretically motivated policies can be useful for post-disaster decision-making.

Suggested Citation

  • Alisjahbana, Irene & Graur, Andrei & Lo, Irene & Kiremidjian, Anne, 2022. "Optimizing strategies for post-disaster reconstruction of school systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
  • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021007298
    DOI: 10.1016/j.ress.2021.108253
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    References listed on IDEAS

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    1. Stefanie S. Schulze & Erica C. Fischer & Sara Hamideh & Hussam Mahmoud, 2020. "Wildfire impacts on schools and hospitals following the 2018 California Camp Fire," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 901-925, October.
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    7. Victoria Johnson & Kevin Ronan, 2014. "Classroom responses of New Zealand school teachers following the 2011 Christchurch earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 1075-1092, June.
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    1. Anwar, Ghazanfar Ali & Zhang, Xiaoge, 2024. "Deep reinforcement learning for intelligent risk optimization of buildings under hazard," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Yang, Sen & Zhang, Yi & Lu, Xinzheng & Guo, Wei & Miao, Huiquan, 2024. "Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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