Optimizing Strategies for Post-Disaster Reconstruction of School Systems

In the aftermath of natural disasters, the school system is one of the many sectors that can be severely impacted. As a result of damaged schools, students are usually forced to transfer to neighboring schools, resulting in a decline in learning conditions and affecting the psychological well-being of the students. In this paper, we take an optimization approach to determining how to reconstruct schools as soon as possible to improve student well-being. In particular, limited reconstruction crews mean 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 first show that it is computationally difficult (NP-hard) to find the optimal order. We then propose a simple greedy policy for determining the order of reconstruction and prove that this policy achieves close to the optimal objective in a simple setting with one construction crew. Finally, we empirically evaluate our greedy policy in a case study using data after the 2018 Lombok earthquakes and an expanded model with multiple construction crews, school capacity constraints, and temporary schools. In the case study, our algorithm performs significantly better than policies typically considered by policymakers. Our results demonstrate how theoretically motivated policies can be useful for post-disaster decision making.

The journal paper for this project is published in the journal Reliability Engineering & System Safety, and can be found here.

The code for this project can be found here.

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About Me

Stanford PhD graduate. Data Scientist @ Lacuna Technologies. Lecturer @ Stanford