Design of a Stochastic Cold-Stored Strawberry Supply Chain Model

dc.contributor.advisorCastillo-Villar, Krystel
dc.contributor.advisorAlaeddini, Adel
dc.contributor.authorHernandez Cuellar, David
dc.contributor.committeeMemberAlaeddini, Adel
dc.contributor.committeeMemberBhuiyan, Tanveer
dc.creator.orcidhttps://orcid.org/0009-0006-3195-9208
dc.date.accessioned2024-03-26T22:49:47Z
dc.date.available2024-03-26T22:49:47Z
dc.date.issued2023
dc.description.abstractSupply chain systems for fresh produce are essential parts of the food industry, ensuring timely delivery of fruits and vegetables to customers at the right cost and desired quality. In recent decades, researchers have been advocating for hub-and-spoke network models as a method to optimize large-scale food Supply Chains (SC). While the traditional optimization of SC addresses inefficiencies, most of the conventional models are deterministic and fail to consider the implicit variability of crop yields and the consequence of climate change on the agricultural supply. The increasing concentration of CO2, along with the shifts in weather patterns, pose various threats, including fluctuations, to the supply of produce. These fluctuations may prompt distributors to seek alternative suppliers in distant areas, introducing additional costs as the SC needs to be reconfigured. This thesis introduces a stochastic hub-and-spoke network model along with an optimization algorithm, aiming to optimize the distribution and transportation network while considering climate variability and its impact on crop yield. The practical application is demonstrated through a stochastic Cold Food Supply Chain (CFSC) case study for strawberries in California, incorporating multiple weather scenarios and real soil data. The analysis reveals a significant increase in the average supply per farm between the best- (75th percentile) and worst-case (25th percentile) scenarios within each RCP projection. These findings highlight a correlation between higher precipitation scenarios and increased crop yield, while lower precipitation levels lead to reduced fresh fruit production. The heightened crop yield offers distributors an opportunity to lower transportation costs by sourcing more produce from nearby farms. Notably, a detailed analysis of transportation costs reveals that about 80% of the total cost difference between these scenarios can be attributed to the transportation of produce from farms to other nodes in the network.
dc.description.departmentMechanical Engineering
dc.format.extent1 electronic resource (54 pages)
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798381179958
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6284
dc.languageeng
dc.subjectSupply chain systems
dc.subjectCold Food Supply Chain
dc.subjectStrawberry
dc.subjectFood industry
dc.subject.classificationMechanical engineering
dc.subject.classificationFood science
dc.subject.classificationEngineering
dc.titleDesign of a Stochastic Cold-Stored Strawberry Supply Chain Model
dc.typeThesis
dc.type.dcmiText
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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