{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/70362"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/70362","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"Large-scale optimization for green logistics and stochastic resource allocation for food security","abstract":"We studied several important management and policy analysis problems in food supply chain systems utilizing large-scale optimization, stochastic resource allocation, and data-analytics methodologies. We focused on three main research questions: 1) How can retailers build green, efficient last-mile logistics system when the objective is to maximize their profit and minimize the costs due to fuel consumption, inventory holding, and greenhouse gas emissions (Chapter 2); 2) what is the best environmental intervention policy to reduce the environmental externalities associated with the production of fruits and vegetables considering environmental and economic dimensions simultaneously (Chapter 3); and (3) How can food banks better manage food supplies distribution to combat food insecurity of underserved population (Chapters 4 & 5). Specifically, we have explored the following four dimensions in food supply chains 1) Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. We consider the problem of inventory routing in the context of perishable products and find near-optimal replenishment scheduling and vehicle routes. To solve the problem efficiently, we develop an exact method based on Benders decomposition to find high-quality solutions in reasonable time and a two-stage meta-heuristic. 2) A systems approach to carbon policy for fruit supply chains: carbon tax, technology innovation, or land sparing? Reducing carbon emissions of food supply chains has increasingly received attention from businesses and policymakers. In order to propose sound policies aimed at lowering such emissions, policy makers favor tools that are informative in the economic and environmental dimensions simultaneously. In this study we offer a systems-based approach which is intended to do just that by developing a spatially and temporally disaggregated price equilibrium mathematical model for a food production and distribution system and applying it to the U.S. apple supply chain. We find that R&D which leads to storage technologies with lower carbon emission rates has the greatest potential for emission reduction. 3) Unified framework for efficient, effective, and fair resource allocation by food banks based on Approximate Dynamic Programming. The evidence linking food insecurity, poor nutrition, and increased risk of chronic health problems, combined with the high cost of health-care systems to treat food insecurity, poses significant health threats and presents challenges to the food bank system. We develop a framework for optimizing resource allocation by food banks using a dynamic programming model. To deal with the high-dimensional state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. Computational experiments using real-world data obtained from one of the food banks in New York State demonstrate the performance of the approach. Specifically, when compared against the policy currently implemented in practice, our algorithm demonstrates a 7.73% improvement in total utility. 4) Predicting demand patterns at mobile food pantries using interpretable analytics. In a food bank environment under limited budget and supplies, predicting demand patterns can help food bank better serve needy people and improve the efficiency of its operations. We use data from 80 mobile food pantry programs served by one of the food banks in New York state to build guidelines for food bank personnel to better forecast the demand at these programs. We construct a data-driven representation of programs and apply a broad class of analytics methods to predict several aspects of demand. Our study demonstrates that powerful data-analytics techniques combined with data-visualization models can be used to understand and interpret the variability in demand at mobile food pantry programs.","abstract_html":"We studied several important management and policy analysis problems in food supply chain systems utilizing large-scale optimization, stochastic resource allocation, and data-analytics methodologies. We focused on three main research questions: 1) How can retailers build green, efficient last-mile logistics system when the objective is to maximize their profit and minimize the costs due to fuel consumption, inventory holding, and greenhouse gas emissions (Chapter 2); 2) what is the best environmental intervention policy to reduce the environmental externalities associated with the production of fruits and vegetables considering environmental and economic dimensions simultaneously (Chapter 3); and (3) How can food banks better manage food supplies distribution to combat food insecurity of underserved population (Chapters 4 & 5). Specifically, we have explored the following four dimensions in food supply chains 1) Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. We consider the problem of inventory routing in the context of perishable products and find near-optimal replenishment scheduling and vehicle routes. To solve the problem efficiently, we develop an exact method based on Benders decomposition to find high-quality solutions in reasonable time and a two-stage meta-heuristic. 2) A systems approach to carbon policy for fruit supply chains: carbon tax, technology innovation, or land sparing? Reducing carbon emissions of food supply chains has increasingly received attention from businesses and policymakers. In order to propose sound policies aimed at lowering such emissions, policy makers favor tools that are informative in the economic and environmental dimensions simultaneously. In this study we offer a systems-based approach which is intended to do just that by developing a spatially and temporally disaggregated price equilibrium mathematical model for a food production and distribution system and applying it to the U.S. apple supply chain. We find that R&D which leads to storage technologies with lower carbon emission rates has the greatest potential for emission reduction. 3) Unified framework for efficient, effective, and fair resource allocation by food banks based on Approximate Dynamic Programming. The evidence linking food insecurity, poor nutrition, and increased risk of chronic health problems, combined with the high cost of health-care systems to treat food insecurity, poses significant health threats and presents challenges to the food bank system. We develop a framework for optimizing resource allocation by food banks using a dynamic programming model. To deal with the high-dimensional state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. Computational experiments using real-world data obtained from one of the food banks in New York State demonstrate the performance of the approach. Specifically, when compared against the policy currently implemented in practice, our algorithm demonstrates a 7.73% improvement in total utility. 4) Predicting demand patterns at mobile food pantries using interpretable analytics. In a food bank environment under limited budget and supplies, predicting demand patterns can help food bank better serve needy people and improve the efficiency of its operations. We use data from 80 mobile food pantry programs served by one of the food banks in New York state to build guidelines for food bank personnel to better forecast the demand at these programs. We construct a data-driven representation of programs and apply a broad class of analytics methods to predict several aspects of demand. Our study demonstrates that powerful data-analytics techniques combined with data-visualization models can be used to understand and interpret the variability in demand at mobile food pantry programs.","abstract_has_math":false,"creators":["Alkhannan Alkaabneh, Faisal M"],"institution":"Cornell University","degree_name":"Ph. D., Systems Engineering","degree_level":"Doctor of Philosophy","degree_discipline":"Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Gomez, Miguel","Banerjee, Siddhartha"],"year":2020,"date_issued":"2020-05","date_published":"2020-05","updated_at":"2026-07-24T01:48:58Z","subjects":["Approximate dynamic programming","Data analytics","Food banks","Large-scale optimization","Supply chain"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/d4b9-g420"],"render_values":[{"text":"https://doi.org/10.7298/d4b9-g420","href":"https://doi.org/10.7298/d4b9-g420","code":true}]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 12006","ProQuest Publication ID: 27993131"],"render_values":[{"text":"ProQuest Submission ID: 12006","href":null,"code":true},{"text":"ProQuest Publication ID: 27993131","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1813/70362","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Gomez, Miguel","Banerjee, Siddhartha"]},{"key":"dc:creator","label":"Author","values":["Alkhannan Alkaabneh, Faisal M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-08-10T20:23:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-08T06:00:12Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-05"]},{"key":"dc:type","label":"Dc Type","values":["dissertation or thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctor of Philosophy"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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We focused on three main research questions: 1) How can retailers build green, efficient last-mile logistics system when the objective is to maximize their profit and minimize the costs due to fuel consumption, inventory holding, and greenhouse gas emissions (Chapter 2); 2) what is the best environmental intervention policy to reduce the environmental externalities associated with the production of fruits and vegetables considering environmental and economic dimensions simultaneously (Chapter 3); and (3) How can food banks better manage food supplies distribution to combat food insecurity of underserved population (Chapters 4 & 5). Specifically, we have explored the following four dimensions in food supply chains 1) Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. We consider the problem of inventory routing in the context of perishable products and find near-optimal replenishment scheduling and vehicle routes. To solve the problem efficiently, we develop an exact method based on Benders decomposition to find high-quality solutions in reasonable time and a two-stage meta-heuristic. 2) A systems approach to carbon policy for fruit supply chains: carbon tax, technology innovation, or land sparing? Reducing carbon emissions of food supply chains has increasingly received attention from businesses and policymakers. In order to propose sound policies aimed at lowering such emissions, policy makers favor tools that are informative in the economic and environmental dimensions simultaneously. In this study we offer a systems-based approach which is intended to do just that by developing a spatially and temporally disaggregated price equilibrium mathematical model for a food production and distribution system and applying it to the U.S. apple supply chain. We find that R&D which leads to storage technologies with lower carbon emission rates has the greatest potential for emission reduction. 3) Unified framework for efficient, effective, and fair resource allocation by food banks based on Approximate Dynamic Programming. The evidence linking food insecurity, poor nutrition, and increased risk of chronic health problems, combined with the high cost of health-care systems to treat food insecurity, poses significant health threats and presents challenges to the food bank system. We develop a framework for optimizing resource allocation by food banks using a dynamic programming model. To deal with the high-dimensional state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. Computational experiments using real-world data obtained from one of the food banks in New York State demonstrate the performance of the approach. Specifically, when compared against the policy currently implemented in practice, our algorithm demonstrates a 7.73% improvement in total utility. 4) Predicting demand patterns at mobile food pantries using interpretable analytics. In a food bank environment under limited budget and supplies, predicting demand patterns can help food bank better serve needy people and improve the efficiency of its operations. We use data from 80 mobile food pantry programs served by one of the food banks in New York state to build guidelines for food bank personnel to better forecast the demand at these programs. We construct a data-driven representation of programs and apply a broad class of analytics methods to predict several aspects of demand. Our study demonstrates that powerful data-analytics techniques combined with data-visualization models can be used to understand and interpret the variability in demand at mobile food pantry programs."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Large-scale optimization for green logistics and stochastic resource allocation for food security"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Gomez, Miguel","Banerjee, Siddhartha"],"dc:creator":["Alkhannan Alkaabneh, Faisal M"],"dc:date.accessioned":["2020-08-10T20:23:40Z"],"dc:date.available":["2022-06-08T06:00:12Z"],"dc:date.issued":["2020-05"],"dc:description":["224 pages"],"dc:description.abstract":["We studied several important management and policy analysis problems in food supply chain systems utilizing large-scale optimization, stochastic resource allocation, and data-analytics methodologies. We focused on three main research questions: 1) How can retailers build green, efficient last-mile logistics system when the objective is to maximize their profit and minimize the costs due to fuel consumption, inventory holding, and greenhouse gas emissions (Chapter 2); 2) what is the best environmental intervention policy to reduce the environmental externalities associated with the production of fruits and vegetables considering environmental and economic dimensions simultaneously (Chapter 3); and (3) How can food banks better manage food supplies distribution to combat food insecurity of underserved population (Chapters 4 & 5). Specifically, we have explored the following four dimensions in food supply chains 1) Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. We consider the problem of inventory routing in the context of perishable products and find near-optimal replenishment scheduling and vehicle routes. To solve the problem efficiently, we develop an exact method based on Benders decomposition to find high-quality solutions in reasonable time and a two-stage meta-heuristic. 2) A systems approach to carbon policy for fruit supply chains: carbon tax, technology innovation, or land sparing? Reducing carbon emissions of food supply chains has increasingly received attention from businesses and policymakers. In order to propose sound policies aimed at lowering such emissions, policy makers favor tools that are informative in the economic and environmental dimensions simultaneously. In this study we offer a systems-based approach which is intended to do just that by developing a spatially and temporally disaggregated price equilibrium mathematical model for a food production and distribution system and applying it to the U.S. apple supply chain. We find that R&D which leads to storage technologies with lower carbon emission rates has the greatest potential for emission reduction. 3) Unified framework for efficient, effective, and fair resource allocation by food banks based on Approximate Dynamic Programming. The evidence linking food insecurity, poor nutrition, and increased risk of chronic health problems, combined with the high cost of health-care systems to treat food insecurity, poses significant health threats and presents challenges to the food bank system. We develop a framework for optimizing resource allocation by food banks using a dynamic programming model. To deal with the high-dimensional state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. Computational experiments using real-world data obtained from one of the food banks in New York State demonstrate the performance of the approach. Specifically, when compared against the policy currently implemented in practice, our algorithm demonstrates a 7.73% improvement in total utility. 4) Predicting demand patterns at mobile food pantries using interpretable analytics. In a food bank environment under limited budget and supplies, predicting demand patterns can help food bank better serve needy people and improve the efficiency of its operations. We use data from 80 mobile food pantry programs served by one of the food banks in New York state to build guidelines for food bank personnel to better forecast the demand at these programs. We construct a data-driven representation of programs and apply a broad class of analytics methods to predict several aspects of demand. Our study demonstrates that powerful data-analytics techniques combined with data-visualization models can be used to understand and interpret the variability in demand at mobile food pantry programs."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["https://doi.org/10.7298/d4b9-g420"],"dc:identifier.other":["ProQuest Submission ID: 12006","ProQuest Publication ID: 27993131"],"dc:identifier.uri":["https://hdl.handle.net/1813/70362"],"dc:language.iso":["en"],"dc:subject":["Approximate dynamic programming","Data analytics","Food banks","Large-scale optimization","Supply chain"],"dc:title":["Large-scale optimization for green logistics and stochastic resource allocation for food security"],"dc:type":["dissertation or thesis"],"thesis:degree_discipline":["Systems Engineering"],"thesis:degree_level":["Doctor of Philosophy"],"thesis:degree_name":["Ph. D., Systems Engineering"],"thesis:institution_name":["Cornell University"]},"updated_at":"2026-07-24T01:48:58Z"}