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PFI:BIC - Flexible,equitable, efficient, and effective distribution (FEEED)

PFI:BIC - Flexible,equitable, efficient, and effective distribution (FEEED)
PFI:BIC - 灵活、公平、高效、有效的分配(FEEED)
批准号:
1718672
负责人:
Lauren Davis
金额:
$100.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-08-01 至 2021-07-31

项目摘要

项目成果

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中文摘要
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英文摘要
This project develops a smart service system to assist hunger relief organizations, like food banks, in the Flexible, Equitable, Efficient, and Effective Distribution (FEEED) of food to those in need. One in eight people in America struggle with hunger. Food banks try to connect excess food to these families in need but face many challenges. The true need is uncertain; donated food has a limited shelf life; warehouse networks and receiving agencies have capacity constraints; and the donated food supply is unpredictable in timing, quantity, and quality. These challenges are becoming increasingly complex as food banks must distribute food equitably to those in need, efficiently maximize donated supply, minimize waste and distribute food in a cost effective manner. The existing tools used by food banks provide information to facilitate all levels of decision making but are limited in their ability to (i) provide a comprehensive picture; (ii) recommend improvements; and (iii) learn from and respond to the highly dynamic and uncertain environment. FEEED will synthesize data from various sources to automatically predict, visualize, learn from decision maker?s actions, and identify strategies to advance operational effectiveness of food collection, distribution, and resource management and fundamentally transform the way food banks operate.FEEED advances the management of complex dynamic distribution systems that require intelligence to effectively utilize data to improve decision making. This research project will involve the exploration and development of: (1) a novel, stochastic modeling framework to adapt to the dynamically changing objectives of the human decision maker, thus enabling the system to learn over time and more effectively recommend solutions tailored to the decision maker's preferences; (2) an intelligent operational guidance engine and knowledge base that help food bank staff operationalize complex modeling into their daily processes; (3) a framework for synthesizing and managing big data in the humanitarian relief sector; and (4) human/machine communication modalities that facilitate user understanding and visualization of smart system analysis, predictions and recommendations. This research addresses the critical intersection of humanitarian relief, engineering, computer science and industry by linking donations management and food distribution to supply chain management and forges a relationship necessary to address this significant global challenge. The proposed research integrates discovery in industrial engineering and computer science with industry best practice knowledge for the development of technology that can be applied for flexible, equitable, efficient, and effective humanitarian relief operations worldwide.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1111/poms.13551
发表时间: 2021-10-22
期刊: PRODUCTION AND OPERATIONS MANAGEMENT
影响因子: 5
作者: [Hasnain, Tanzid, Sengul Orgut, Irem, Ivy, Julie Simmons]
通讯作者: Ivy, Julie Simmons
Estimating True Demand at a Local Hunger Relief Organization
估计当地饥饿救济组织的真实需求
DOI: --
发表时间: 2020
期刊: Proceedings of the 2020 IISE Annual Conference
影响因子: --
作者: [Odubela, Kehinde, Jiang, Steven, Davis, Lauren]
通讯作者: Davis, Lauren
DOI: --
发表时间: 2019
期刊: Predicting Food Donor Contribution Behavior Using Support Vector Regression
影响因子: --
作者: [Paul, Shubra, Davis, Lauren]
通讯作者: Davis, Lauren
DOI: --
发表时间: 2018
期刊: Modeling for Efficient Assignment of Multiple Distribution Centers for the Equitable and Effective Distribution of Donated Food
影响因子: --
作者: [Islam, M., Ivy, J.]
通讯作者: Ivy, J.
9
    PFI-RP: A Smart Food Distribution System for Allocating Scarce Resources Under Extreme Events
    I-Corps: Development of a smart food distribution software system
    Collaborative Research RAPID: Matriculation and Well-Being Under Emergent Events (MWEE): Using Data to Empower Campus Communities in Times of Crisis
    RAPID/Collaborative Research: Capacity Adjustment, Resilience and Information Sharing in a Network for Good (CARING)
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