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Data-Driven Planning for Food and Fo...

Elimbi Moudio, Marie Pelagie.

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Data-Driven Planning for Food and Forest Supply Chains Under Disruptions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Planning for Food and Forest Supply Chains Under Disruptions./
Author:	Elimbi Moudio, Marie Pelagie.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	122 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30493752
ISBN:	9798380877855

Data-Driven Planning for Food and Forest Supply Chains Under Disruptions.
Elimbi Moudio, Marie Pelagie.

Data-Driven Planning for Food and Forest Supply Chains Under Disruptions. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 122 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2023.

Global disruptions such as the Covid-19 pandemic emphasize the fragile nature of connected supply chains and the impact of these interruptions on our daily lives. The alarming growth in the type and number of disruptions complicates planning efforts and will lead to increased losses in the absence of efficient systems that reduce complexity for policy makers. We investigate food and forestry supply chains, key systems for human survival. In the context of food and agricultural supply chains, disruptions have not only caused bottlenecks in different stages of food supply chains but also exposed the need for holistic solutions to food supply and allocation. To tackle this challenge, we model a two-stage stochastic resource allocation problem with non-linear connectivity costs to capture trade dynamics between countries. We compare model recommendations to historical trade flow data including coffee trade between countries, unveiling the value of centralized planning under potential disruption scenarios against the current practices. In another chapter, we design and incrementally update a data-driven network risk measure that focuses on including the downstream impact of nodes. starting with a hand crafted intuitive risk measure then the Downstream Protection Value (DPV) and finally proposing the Downstream Supply Risk Measure (DSRM). The consistency in results between DPV and DSRM measures is investigated and their limitations are discussed. Additionally, the existence of a large variety of food products that are exchanged within and across countries renders the creation of separate policies for each product impractical. To aid this problem, we develop a framework to systematically group different food product supply chains by identifying their structure using world bank trade data between countries for $53$ different products over a period of $25$ years. Decision makers can thus create policies for food networks at the group level instead of individual policies for the large variety of food and agricultural products. In the context of Forestry systems, we extend an existing fuel treatment allocation model to include socio-environmental costs to the objective function. We investigate how to develop efficient action to mitigate future fires (fuel treatment plans) so as to mitigate risk in Forestry supply chains. In the long run, developing an understanding of the impacts of risk and how they can be mitigated will be essential in resolving their lasting and usually devastating impacts to the world ecosystem.

ISBN: 9798380877855Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Decision-making

Data-Driven Planning for Food and Forest Supply Chains Under Disruptions.
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245 1 0 $a Data-Driven Planning for Food and Forest Supply Chains Under Disruptions.
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300 $a 122 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Shen, Zuo-Jun Max.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2023.
520 $a Global disruptions such as the Covid-19 pandemic emphasize the fragile nature of connected supply chains and the impact of these interruptions on our daily lives. The alarming growth in the type and number of disruptions complicates planning efforts and will lead to increased losses in the absence of efficient systems that reduce complexity for policy makers. We investigate food and forestry supply chains, key systems for human survival. In the context of food and agricultural supply chains, disruptions have not only caused bottlenecks in different stages of food supply chains but also exposed the need for holistic solutions to food supply and allocation. To tackle this challenge, we model a two-stage stochastic resource allocation problem with non-linear connectivity costs to capture trade dynamics between countries. We compare model recommendations to historical trade flow data including coffee trade between countries, unveiling the value of centralized planning under potential disruption scenarios against the current practices. In another chapter, we design and incrementally update a data-driven network risk measure that focuses on including the downstream impact of nodes. starting with a hand crafted intuitive risk measure then the Downstream Protection Value (DPV) and finally proposing the Downstream Supply Risk Measure (DSRM). The consistency in results between DPV and DSRM measures is investigated and their limitations are discussed. Additionally, the existence of a large variety of food products that are exchanged within and across countries renders the creation of separate policies for each product impractical. To aid this problem, we develop a framework to systematically group different food product supply chains by identifying their structure using world bank trade data between countries for $53$ different products over a period of $25$ years. Decision makers can thus create policies for food networks at the group level instead of individual policies for the large variety of food and agricultural products. In the context of Forestry systems, we extend an existing fuel treatment allocation model to include socio-environmental costs to the objective function. We investigate how to develop efficient action to mitigate future fires (fuel treatment plans) so as to mitigate risk in Forestry supply chains. In the long run, developing an understanding of the impacts of risk and how they can be mitigated will be essential in resolving their lasting and usually devastating impacts to the world ecosystem.
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650 4 $a Statistics. $3 517247
650 4 $a Systems science. $3 3168411
653 $a Decision-making
653 $a Global Food Networks
653 $a Stochastic optimization
653 $a Supply chain and risk
653 $a Uncertainty
690 $a 0796
690 $a 0546
690 $a 0463
690 $a 0790
710 2 $a University of California, Berkeley. $b Industrial Engineering & Operations Research. $3 1672475
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0028
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30493752