Optimizing strategies for post-disaster reconstruction of school systems

Alisjahbana, Irene; Graur, Andrei; Lo, Irene; Kiremidjian, Anne S.

doi:10.1016/j.ress.2021.108253

Cited by 9 publications

(10 citation statements)

References 23 publications

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“…Our model can still incorporate this by assuming that a new hospital facility in the same location is set up during postearthquake reconstruction and subsequently shut down after it is no longer needed, thus replicating the additional capacity. Adding temporary facilities is similar to what was implemented in Alisjahbana et al (2022). However, this study does not focus on the addition of temporary facilities.…”

Section: Healthcare Facility As An M/m/s Queue With Prioritiesmentioning

confidence: 99%

“…While W defined in Equation (2) refers to the wait time at a specific facility, 𝜔 p represents p's wait time at the facility, and differs based on where the patients are allocated to. Alisjahbana et al (2022) presented a similar formulation for school reconstruction. However, unlike the reconstruction of school buildings, the objective function for the reconstruction of healthcare facilities (Equation ( 5)) includes minimizing the patient's total time to receive medical care, as defined in Equation (2).…”

Section: Minimizementioning

confidence: 99%

“…Similar to the MIP problem presented in Alisjahbana et al (2022), the objective function of Equation ( 5) is also subjected to constraints. This includes constraints to bound the number of healthcare facilities that can be reconstructed at any given time (depending on the number of reconstruction crews available), constraints to enforce that all damaged healthcare facilities will have to be reconstructed at the end of the recovery period, constraints to allocate patients to functional healthcare facilities, and constraints to limit the capacity of the healthcare facility.…”

Section: Minimizementioning

confidence: 99%

“…Few studies have focused on the long-term recovery and reconstruction of critical systems, and even fewer focused on the healthcare system. Alisjahbana et al (2022) proposed a greedy algorithm to determine the reconstruction ordering of school buildings after disasters. In the study, the authors compared the reconstruction strategy implemented by the government after the 2018 Lombok Earthquakes and demonstrated how the greedy algorithm could result in more equitable solutions.…”

Section: Introductionmentioning

confidence: 99%

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Prioritized reconstruction of healthcare facilities after earthquakes based on recovery of emergency services

2022

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Earthquakes can disrupt the healthcare system heavily, leading to long wait times and many untreated patients for years after the event. Emergency services, in particular, must return to preearthquake functionality as soon as possible such that patients, especially critically injured ones, can be treated promptly. However, reconstruction and restoration of emergency services can take years. Due to limited reconstruction resources, decision‐makers cannot reconstruct all hospitals simultaneously. They are typically forced to prioritize the reconstruction order, and this process is often poorly planned. This article models emergency services as an M/M/s queuing system that accounts for prioritized treatment of critical patients and formulates a greedy algorithm to plan for an effective healthcare system reconstruction. The algorithm finds the reconstruction ordering of hospital buildings such that emergency patients have the shortest time to receiving medical care possible. We show our greedy algorithm's good performance for small problem instances, with average deviations from the optimal solution below 16%. Further, we apply our methodology to a case study of Lima, Peru, under a hypothetical M8.0 earthquake. The application demonstrates that compared to typically implemented policies, a policy guided by our formulation results in shorter time to treatment and reduces the number of untreated patients over the course of the reconstruction period by more than a factor of 3 in a worst‐case scenario with 70% hospital capacity disruption. Finally, we demonstrate that our formulation can be integrated into risk analysis through Monte Carlo simulations to inform decision‐makers of reconstruction plans after future earthquakes.

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Section: Healthcare Facility As An M/m/s Queue With Prioritiesmentioning

confidence: 99%

Section: Minimizementioning

confidence: 99%

Section: Minimizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prioritized reconstruction of healthcare facilities after earthquakes based on recovery of emergency services

2022

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“…Examples of damage estimation includeIsoyama et al's assessment of pipelines (2000) andOgawa et al's (2021) study of building damage (and its impact on residents) subject to earthquakes and subsequent tsunami and fires. Models of restoration may consider the effects of multiple hazards or system modifications(Duffey 2019;Huang, Zhang, and Huang 2022;Porter 2016) and can include methods for improving recovery processes, such as greedy algorithm (2016;Alisjahbana et al 2022) and multi-objective optimization approaches (2017; Bozorgi-Amiri, Jabalameli, and Mirzapour Al-e-Hashem 2013). Others specifically highlight the uncertainties and difficulties of restoring complex networks and provide strategies for addressing these challenges(González et al 2016;Fang and Sansavini 2019;Z.…”

mentioning

confidence: 99%

Methods for representing regional disaster recovery estimates: modeling approaches and assessment tools for improving emergency planning and preparedness

Deelstra

Bristow

2023

Nat Hazards

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The complexity and interconnected nature of critical infrastructure systems across metropolitan regions presents a unique challenge for communities to understand how they may respond and recover in the face of a major disruption. Disaster recovery modeling facilitates coordination and planning among stakeholders, but detailed system models are often complex and require significant technical skill to construct and interpret. The first part of this work presents the development and assessment of a simplified seismic recovery model for water, wastewater, and power systems in the Metro Vancouver region of British Columbia, Canada. The model considers important geospatial and interdependent characteristics of multi-infrastructure systems without requiring access to complete operational models.The model is expanded in the second part of this work to consider the effectiveness of disaster risk reduction measures on infrastructure service recovery to the population after the earthquake. Finally, a detailed hydraulic water system analysis is compared to the simplified modeling approach for a seismic hazard scenario to consider how results from each compare given various restoration strategies. Results from the three sections of this work demonstrate the utility of a simplified multi-infrastructure modeling approach for assessing recovery at a regional scale, the potential benefits of investing in disaster risk reduction measures to improve recovery outcomes for residents, and aspects of modeling approaches that provide an understanding of their use and benefits for disaster management purposes.

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Developing an effective safety management system functional framework of an educational building environment with approach safety cube

Rezaie¹,

Shamskia²,

Shishegaran³

et al. 2023

Journal of Building Engineering

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Optimizing strategies for post-disaster reconstruction of school systems

Cited by 9 publications

References 23 publications

Prioritized reconstruction of healthcare facilities after earthquakes based on recovery of emergency services

Prioritized reconstruction of healthcare facilities after earthquakes based on recovery of emergency services

Methods for representing regional disaster recovery estimates: modeling approaches and assessment tools for improving emergency planning and preparedness

Developing an effective safety management system functional framework of an educational building environment with approach safety cube

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