東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Optimizing Resource Allocation in No...

Ivuawuogu, Henry,

FindBook

Google Book

Amazon

博客來

Optimizing Resource Allocation in Non-Profit Organizations: A Multi-Source Machine Learning Approach for Efficient Food Procurement and Distribution /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimizing Resource Allocation in Non-Profit Organizations: A Multi-Source Machine Learning Approach for Efficient Food Procurement and Distribution // Henry Ivuawuogu.
作者:	Ivuawuogu, Henry,
面頁冊數:	1 electronic resource (172 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
標題:	Industrial engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31334004
ISBN:	9798383700143

Optimizing Resource Allocation in Non-Profit Organizations: A Multi-Source Machine Learning Approach for Efficient Food Procurement and Distribution /
Ivuawuogu, Henry,

Optimizing Resource Allocation in Non-Profit Organizations: A Multi-Source Machine Learning Approach for Efficient Food Procurement and Distribution /Henry Ivuawuogu. - 1 electronic resource (172 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Food insecurity is a significant issue in the United States, with food banks and their partner agencies playing crucial roles in addressing this challenge. This dissertation research focuses on resource allocation within non-profit organizations, particularly in the context of food procurement and distribution. It highlights the increasing emphasis placed on both ensuring the distribution of healthy food and fostering equity in distribution within the food bank community.The research has three objectives. Firstly, it acknowledges variations in how partner agencies rely on food banks. The study optimizes the distribution of special food items based on the shopping patterns of partner agencies. These shopping patterns are used as a proxy to measure each partner agency's dependency on the food bank, referred to as the dependency ratio. Clustering analysis is employed to balance resource allocation, considering different agency reliance levels and distinct resource sources and demand patterns. This approach aims to achieve equitable food distribution and address disparities in equity assessment at the agency level. The findings highlight the importance of data-driven approaches in addressing inequality within the food supply chain.Recognizing that each food bank is unique, some have started reporting the dependency ratio of their partner agencies. Secondly, the study introduces predictive analysis to forecast the dependency ratio, considering specific agency characteristics. By understanding socioeconomic factors influencing agency dependence, this study enhances prediction precision. This predictive capability aids decision-makers in food distribution, enabling more informed and targeted resource allocation.Additionally, non-profit hunger relief organizations receive monetary and non-monetary donations from government and private donors, but these donations may come with restrictions influencing procurement and distribution decisions. The dissertation research's third segment presents a heuristic approach for a multi-period, multi-product deterministic optimization model, addressing the challenge of allocating limited donor funds effectively for food purchases.Ultimately, the research aims to empower food banks and their partner agencies to better serve the people in need and address the pressing issue of hunger, ensuring that resources are allocated equitably and efficiently, based on the actual needs and dependencies of their partner agencies.

English

ISBN: 9798383700143Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Food insecurity

Optimizing Resource Allocation in Non-Profit Organizations: A Multi-Source Machine Learning Approach for Efficient Food Procurement and Distribution /
LDR:04036nmm a22004333i 4500 001 2429361
005 20250430100808.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798383700143
035 $a (MiAaPQD)AAI31334004
035 $a AAI31334004
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Ivuawuogu, Henry, $e author. $3 3817169
245 1 0 $a Optimizing Resource Allocation in Non-Profit Organizations: A Multi-Source Machine Learning Approach for Efficient Food Procurement and Distribution / $c Henry Ivuawuogu.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (172 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
500 $a Advisors: Jiang, Steven Committee members: Davis, Lauren; Qu, Xiuli; Stanfield, Paul; Thomas, Misty.
502 $b Ph.D. $c North Carolina Agricultural and Technical State University $d 2024.
520 $a Food insecurity is a significant issue in the United States, with food banks and their partner agencies playing crucial roles in addressing this challenge. This dissertation research focuses on resource allocation within non-profit organizations, particularly in the context of food procurement and distribution. It highlights the increasing emphasis placed on both ensuring the distribution of healthy food and fostering equity in distribution within the food bank community.The research has three objectives. Firstly, it acknowledges variations in how partner agencies rely on food banks. The study optimizes the distribution of special food items based on the shopping patterns of partner agencies. These shopping patterns are used as a proxy to measure each partner agency's dependency on the food bank, referred to as the dependency ratio. Clustering analysis is employed to balance resource allocation, considering different agency reliance levels and distinct resource sources and demand patterns. This approach aims to achieve equitable food distribution and address disparities in equity assessment at the agency level. The findings highlight the importance of data-driven approaches in addressing inequality within the food supply chain.Recognizing that each food bank is unique, some have started reporting the dependency ratio of their partner agencies. Secondly, the study introduces predictive analysis to forecast the dependency ratio, considering specific agency characteristics. By understanding socioeconomic factors influencing agency dependence, this study enhances prediction precision. This predictive capability aids decision-makers in food distribution, enabling more informed and targeted resource allocation.Additionally, non-profit hunger relief organizations receive monetary and non-monetary donations from government and private donors, but these donations may come with restrictions influencing procurement and distribution decisions. The dissertation research's third segment presents a heuristic approach for a multi-period, multi-product deterministic optimization model, addressing the challenge of allocating limited donor funds effectively for food purchases.Ultimately, the research aims to empower food banks and their partner agencies to better serve the people in need and address the pressing issue of hunger, ensuring that resources are allocated equitably and efficiently, based on the actual needs and dependencies of their partner agencies.
546 $a English
590 $a School code: 1544
650 4 $a Industrial engineering. $3 526216
650 4 $a Food science. $3 3173303
650 4 $a Engineering. $3 586835
653 $a Food insecurity
653 $a Resource allocation
653 $a Non-profit organizations
653 $a Food banks
690 $a 0546
690 $a 0796
690 $a 0537
690 $a 0359
710 2 $a North Carolina Agricultural and Technical State University. $b Industrial and Systems Engineering. $e degree granting institution. $3 3817092
720 1 $a Jiang, Steven $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-02B.
790 $a 1544
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31334004