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Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.
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A modified approximate dynamic programming algorithm for community-level food security following disasters
In the aftermath of an extreme natural hazard, community residents must have access to functioning food retailers to maintain food security. Food security is dependent on supporting critical infrastructure systems, including electricity, potable water, and transportation. An understanding of the response of such interdependent networks and the process of post-disaster recovery is the cornerstone of an efficient emergency management plan. In this study, the interconnectedness among different critical facilities, such as electrical power networks, water networks, highway bridges, and food retailers, is modeled. The study considers various sources of uncertainty and complexity in the recovery process of a community to capture the stochastic behavior of the spatially distributed infrastructure systems. The study utilizes an approximate dynamic programming (ADP) framework to allocate resources to restore infrastructure components efficiently. The proposed ADP scheme enables us to identify near-optimal restoration decisions at the community level. Furthermore, we employ a simulated annealing (SA) algorithm to complement the proposed ADP framework and to identify near-optimal actions accurately. In the sequel, we use the City of Gilroy, California, USA to illustrate the applicability of the proposed methodology following a severe earthquake. The approach can be implemented efficiently to identify practical policy interventions to hasten recovery of food systems and to reduce adverse food-insecurity impacts for other hazards and communities.
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- Award ID(s):
- 1638284
- PAR ID:
- 10097439
- Date Published:
- Journal Name:
- 2018 9th International Congress on Environmental Modelling and Software (iEMSs)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.more » « less
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The functioning of interdependent civil infrastructure systems in the aftermath of a disruptive event is critical to the performance and vitality of any modern urban community. Post-event stressors and chaotic circumstances, time limitations, and complexities in the community recovery process highlight the necessity for a comprehensive decision-making framework at the community-level for post-event recovery management. Such a framework must be able to handle large-scale scheduling and decision processes, which involve difficult control problems with large combinatorial decision spaces. This study utilizes approximate dynamic programming algorithms along with heuristics for the identification of optimal community recovery actions following the occurrence of an extreme earthquake event. The proposed approach addresses the curse of dimensionality in its analysis and management of multi-state, large-scale infrastructure systems. Furthermore, the proposed approach can consider the cur-rent recovery policies of responsible public and private entities within the community and shows how their performance might be improved. A testbed community coarsely modeled after Gilroy, California, is utilized as an illustrative example. While the illustration provides optimal policies for the Electrical Power Network serving Gilroy following a severe earthquake, preliminary work shows that the methodology is computationally well suited to other infrastructure systems and hazards.more » « less
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Food retailers are stores that stock staple perishable foods such as vegetables, fruits, dairy, bread, cereal, meat, poultry, or fish on a continuous basis and sell these items to the public. Store types include supercenters, grocery stores, convenience stores, combination stores, dollar stores, butcher shops, bakeries, and other specialty food stores. This mission focused on understanding how critical infrastructure failures impact the function of food retailers and how the change in functioning changes food access. This research focused on five infrastructure systems -- transportation, electric power, communications, water, and the buildings utilized by food retailers to carry out their normal activities. The functioning of food retailers was broken down into three branches or domains that are critical for the operation of a food retailer. Specifically, food retailers need 1) people to help run the operation, 2) property or, more generally, a physical structure or structures, to house and conduct operations; 3) products or food stuffs to sell. This mission includes four social science collections related to the in-person survey of food retailers. These collections include the sample frame (a list of all food retailers within the study area with a chance of being randomly selected for the survey), the primary (raw) data collected from the Harris County and Southeast Texas surveys, and an example of a secondary (curated) dataset that focuses on critical infrastructure failures and changes in food retailer functioning.Food insecurity is a chronic problem in the United States that annually affects over 40 million people under normal conditions. This difficult reality can dramatically worsen after disasters. Such events can disrupt both the supply and demand sides of food systems, restricting food distribution and access precisely when households are in a heightened need for food assistance. Often, retailers and food banks must react quickly to meet local needs under difficult post-disaster circumstances. Residents of Harris County and Southeast Texas experienced this problem after Hurricane Harvey made landfall on the Texas Gulf Coast in August 2017. The primary data collected by this project relate specifically to the supply side. The data attempt to identify factors that impacted the ability of suppliers to help ensure access to food, with a focus on fresh food access. Factors included impacts to people, property and products due to hurricane-related damage to infrastructure. Two types of food suppliers were the foci of this research: food aid agencies and food retailers. The research team examined food aid agencies in Southeast Texas with data collection methods that included secondary data analysis, a focus group and an online survey. The second population studied was food retailers with in-person surveys with store managers. Food retailers were randomly sampled in three Texas counties: Jefferson, Orange, and Harris. The data collection methods resulted in 32 food aid agency online survey responses and 210 completed food retail in-person surveys. Data were collected five to eight months after the event, which helped to increase the reliability and validity of the data. The time-sensitive nature of post-disaster data requires research teams to quickly organize their efforts before entering the field. The purpose of this project archive is to share the primary data collected, document methods, and to help future research teams reduce the amount of time needed for project development and reporting. This archive does not contain Personally or Business Identifiable Information.more » « less
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