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dc.contributor.advisorPerez, Eduardo
dc.contributor.authorMarthak, Yash V. ( )
dc.date.accessioned2020-05-14T19:43:09Z
dc.date.available2020-05-14T19:43:09Z
dc.date.issued2020-05
dc.identifier.citationMarthak, Y. V. (2020). Characterizing and planning for key logistic obstacles in food banks operations after hurricane events (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/9916
dc.description.abstract

Food banks are non-profit, charitable organizations that distribute food and products to people in need. Food bank facilities receive donations from public and private agencies and distribute them with the help of city organizations, agencies, and volunteers. Natural disasters, such as hurricane Harvey, have exposed the complexities and challenges associated with those tasks. Food bank facilities become disaster relief centers for affected communities after natural disasters. These facilities typically experience an increase in product demand and an increase in the volume of donations after the impact of a natural disaster. Disaster response involves the planning, coordination, and distribution of supplies in an effective manner to the affected population. The goal of this research is to analyze and forecast the amount of donations received by food bank facilities impacted by natural disasters. A stochastic programming model is also presented which considers prepositioning strategies among food bank facilities located in high risks areas for hurricanes.

The first part of this thesis analyzes the donations received by two food bank facilities affected by hurricane Harvey in 2017. An extensive numerical study is performed that compares the donation behavior at each facility before and after the hurricane event. Multiple forecasting models are evaluated to determine their accuracy in predicting the observed behavior. The results deduced from this part can be used to develop policies that can help in planning for future events. Predictions for total food donations provided least mean absolute percentage error for the analysis. Predictions using econometric model too provided least error for Houston Food Bank for disaster relief period.

The second part of this thesis proposes a stochastic model that considers the uncertainty associated with the impact of the hurricane at each facility in terms of the number of available supplies, donations received at the facility, and the expected demand for their service region. The first-stage decisions attempt to minimize the number of people not receiving the needed supplies by prepositioning the existing supplies at each facility. Second-stage decisions maximize the system responsiveness by trying to satisfy the observed demand for the scenarios under consideration such that unmet demand is minimized. The experiments consider scenarios in which one or two food bank facilities are shut down after the disaster and study the impact of prepositioning supplies. Analysis revealed unmet demand observed for the experiments conducted. The implementation of this model can have a global outreach by minimizing the damage due to any natural disaster by making key food allocation decisions and having an ideal response strategy.

dc.formatText
dc.format.extent137 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectNatural disasters
dc.subjectFood banks
dc.subjectDonations
dc.subjectForecasting
dc.subjectTime series models
dc.subjectStochastic Programming
dc.subjectPre-positioning,
dc.subject.lcshEmergency management
dc.subject.lcshDisaster relief
dc.subject.lcshStochastic programming
dc.titleCharacterizing And Planning For Key Logistic Obstacles In Food Banks Operations After Hurricane Events
txstate.documenttypeThesis
dc.contributor.committeeMemberNovoa, Clara
dc.contributor.committeeMemberMéndez Mediavilla, Francis A.
thesis.degree.departmentEngineering
thesis.degree.disciplineEngineering
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
dc.description.departmentIngram School of Engineering


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