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Title: Predictive Model for Partner Agencies Dependency on Food Banks
In the quest for equitable resource distribution within food banks and their partner agencies, understanding the dependencies of these agencies on food banks emerges as a critical factor. This study investigates the intricate dynamics influencing agency dependency ratios, exploring the complex factors that shape the demand for food resources. Leveraging historical self-reported dependency ratio data, this preliminary study employs predictive modeling using Multiple Linear Regression to forecast agency dependencies on food banks. The primary objective is to discern the underlying factors that significantly impact agency dependency ratios. Employing Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection technique, the study identifies the key variables that capture the essence of the dataset. Identifying the variables that contribute the most to the model paves the way for robust predictive modeling. This study offers a comprehensive approach to understanding and predicting agency dependencies on food banks. The findings hold significant implications for non-profit hunger relief organizations, aiding in strategic decision- making for equitable resource distribution. more »« less
Ivuawuogu, H.; Jiang, S.; Davis, L.
(, Proceedings of 2023 Industrial and Systems Engineering Annual Conference)
Babski-Reeves, K.; Eksioglu, B.; Hampton, D.
(Ed.)
Food insecurity is a serious problem in America and the pandemic makes the problem even worse. Feeding America has more than 200 food banks. that These food banks and their partner agencies are the key players in the battle against food insecurity. Partner agencies may vary in size and location depending on the service area and the variety of the partner agencies and the complexities of their operations make equitable food distribution very challenging. There is a need for a meaningful to group those partner agencies to assist food bank operations managers to make informed decisions. This study uses data from a local food bank and its partner agencies. Each agency is unique in terms of its behavior. Therefore, k-means clustering was used to categorize agencies into groups based on the number of persons served and the amount of food received. The results of the study will provide evidence-based information to assist the food bank in making informed decisions.
Food banks and food aid agencies help address food insecurity issues throughout the United States. This mission focused on understanding how critical infrastructure failures impact the function of food aid agencies and how the change in functioning changes food access. This research focused on five infrastructure systems -- transportation, electric power, communications, water, and the buildings or facilities utilized by food aid agencies to carry out their normal activities. The functioning of food aid agencies was broken down into three branches or domains that are critical for the operation of a food aid agencies. Specifically, food aid agencies 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 distribute. This mission includes five social science collections. The first two collections provide background on the planning and agenda for a focus group and the data collected from the focus group. The next three collections relate to an online survey of food aid agencies. These collections include the sample frame (a list of all active food aid agencies invited to participate in the survey), the primary (raw) data collected from the survey, and an example of a secondary (curated) dataset that focuses on critical infrastructure failures and changes in food aid agency 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.
The food aid agency survey design was informed by the focus group discussion and aimed to gain quantitative data from all food aid agencies affiliated or associated with the Southeast Texas Food Bank. A list of affiliated food aid agencies, their managers, and email addresses was obtained from the Southeast Texas Food Bank. The list included the 89 food aid agencies active in Southeast Texas prior to Hurricane Harvey. Each of these food aid agencies was emailed a personalized invitation to the online survey. The survey of food aid agencies was conducted online using the survey software program Qualtrics XM (Qualtrics, 2019). The online surveys integrated data collected from the Southeast Texas Food Bank Primarius system to tailor specific questions to specific food aid agencies. The survey was designed to collect information on (1) physical damage to the food aid agency, disruption or damage to infrastructure, and transportation and access problems; (2) impact on workers or volunteers; and (3) disruptions to operations and inventory, which included changes in the food distribution schedule, food categories distributed, and sources of food.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.
Food insecurity, defined as insufficient access to food for a healthy and active life, affected approximately 12.8% of U.S. households in 2022. A concerted effort from both the government and non-government organizations is underway to address this challenge in the United States. This study centers on the Foodbank of Central and Eastern North Carolina (FBCENC), a nonprofit hunger relief organization pivotal in collecting and distributing food donations to local agencies serving individuals in need. However, despite the critical role of food banks, nutritional considerations are often overlooked. To address this gap, the study employs the Healthy Eating Research (HER) Nutrition Guideline, categorizing nutrition types (Red, Yellow, and Green) to assess and enhance the nutritional equity of the current distribution system. A linear programming model is proposed, and equity is adopted as the performance measure. The study aims to develop a model that strategically reduces nutritional disparities across the network. By incorporating HER guidelines and emphasizing equity in distribution, this research contributes to the broader objective of creating a more nutritionally equitable response to food insecurity within the non-profit sector.
Islam, M.; Ivy, J.
(, Modeling for Efficient Assignment of Multiple Distribution Centers for the Equitable and Effective Distribution of Donated Food)
Food insecurity affects more than 41 million people annually in the United States. Within the Feeding America network, approximately 200 food banks are working throughout the US to serve people in need with donated food. Satisfying hunger need of food insecure people with limited supply is a challenge for these food banks. A numerical study is performed on data from Food Bank of Central and Eastern North Carolina (FBCENC) to capture the major attributes controlling its food distribution system. FBCENC seeks to distribute donated food equitably so that each service area (county) receives food proportional to its demand while minimizing the undistributed food donations. In addition to seeking equitable and effective food distribution policies, FBCENC wants to identify distribution branches to maximize the accessibility of the counties to donated food. An assignment and distribution model is developed to minimize the cost of maintaining a user-specified cap on the maximum inequity in food distribution. A sensitivity analysis between the user-specified maximum inequity cap and effectiveness shows the effectiveness of donated food distribution can be improved significantly by sacrificing equitable distribution slightly.
Ivuawuogu, Henry, Jiang, Steven, Davis, Lauren, and Hamilton, Mikaya. Predictive Model for Partner Agencies Dependency on Food Banks. Retrieved from https://par.nsf.gov/biblio/10616527. Web. doi:10.54941/ahfe1005573.
@article{osti_10616527,
place = {Country unknown/Code not available},
title = {Predictive Model for Partner Agencies Dependency on Food Banks},
url = {https://par.nsf.gov/biblio/10616527},
DOI = {10.54941/ahfe1005573},
abstractNote = {In the quest for equitable resource distribution within food banks and their partner agencies, understanding the dependencies of these agencies on food banks emerges as a critical factor. This study investigates the intricate dynamics influencing agency dependency ratios, exploring the complex factors that shape the demand for food resources. Leveraging historical self-reported dependency ratio data, this preliminary study employs predictive modeling using Multiple Linear Regression to forecast agency dependencies on food banks. The primary objective is to discern the underlying factors that significantly impact agency dependency ratios. Employing Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection technique, the study identifies the key variables that capture the essence of the dataset. Identifying the variables that contribute the most to the model paves the way for robust predictive modeling. This study offers a comprehensive approach to understanding and predicting agency dependencies on food banks. The findings hold significant implications for non-profit hunger relief organizations, aiding in strategic decision- making for equitable resource distribution.},
journal = {},
number = {159},
publisher = {AHFE International},
author = {Ivuawuogu, Henry and Jiang, Steven and Davis, Lauren and Hamilton, Mikaya},
editor = {Ahram, Tareq and Karwowski, Waldemar}
}
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