{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/140371"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/140371","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Healthy Food Access and Consumption: Informing Interventions Through Analytics","abstract":"In the U.S., about 19 million people reside in low income food deserts--neighborhoods where the majority of the population does not have access to large grocery stores. These areas are associated with less healthy diets and higher rates of poor health outcomes such as obesity and heart disease. Roughly 25% of cardiovascular-related deaths (140,000 deaths per year) in the U.S. can be attributed to low fruit and vegetable intake. Thus, increasing consumption of fruits and vegetables among underserved communities is a key priority of policymakers. At the same time, over half of the produce that is grown in the U.S. each year is wasted. Unfortunately, connecting surplus produce to households in need is a difficult task. This thesis uses advanced analytics to inform public interventions and supply chain design with the goal of increasing access to, and consumption of, fresh produce, particularly among underserved communities. Chapters 2, 3, and 4 focus on understanding the impact of, and optimizing, consumer-level interventions for increasing fruit and vegetable consumption among low income households. First, we perform an empirical analysis in order to understand which interventions are most effective as a function of the household attributes (Chapter 2). Based on these empirical findings, we develop a novel consumer behavioral model of grocery shopping dynamics, which is nested into a bi-level optimization model for determining the government’s optimal investments across three different types of food policy interventions: access, education, and price-related interventions (Chapter 3). Although this model is developed at an individual household level, we also discuss designing interventions for groups of individuals. Chapter 4 generalizes the idea of group-level interventions by defining a new problem in which a service provider must determine the optimal bundles of products or services to offer its users while meeting an individual-level fairness constraint. This problem arises in settings such as healthcare and public policy (where services can be thought of as interventions or treatments), as well as retail settings in which fair out- come guarantees are desirable. We present two approximation algorithms for solving this problem. Lastly, Chapter 5 proposes and analyzes a new supply chain management intervention that increases efficiency in perishable food supply chains. This chapter studies a supply chain with multiple retailers who practice dual sourcing and compete with each other for supply, but do not have transparency to the inventory distributions of their suppliers a priori. A new downstream information sharing scheme is proposed that results in better ordering decisions that benefit the entire supply chain while decreasing food waste.","abstract_html":"In the U.S., about 19 million people reside in low income food deserts--neighborhoods where the majority of the population does not have access to large grocery stores. These areas are associated with less healthy diets and higher rates of poor health outcomes such as obesity and heart disease. Roughly 25% of cardiovascular-related deaths (140,000 deaths per year) in the U.S. can be attributed to low fruit and vegetable intake. Thus, increasing consumption of fruits and vegetables among underserved communities is a key priority of policymakers. At the same time, over half of the produce that is grown in the U.S. each year is wasted. Unfortunately, connecting surplus produce to households in need is a difficult task. This thesis uses advanced analytics to inform public interventions and supply chain design with the goal of increasing access to, and consumption of, fresh produce, particularly among underserved communities. Chapters 2, 3, and 4 focus on understanding the impact of, and optimizing, consumer-level interventions for increasing fruit and vegetable consumption among low income households. First, we perform an empirical analysis in order to understand which interventions are most effective as a function of the household attributes (Chapter 2). Based on these empirical findings, we develop a novel consumer behavioral model of grocery shopping dynamics, which is nested into a bi-level optimization model for determining the government’s optimal investments across three different types of food policy interventions: access, education, and price-related interventions (Chapter 3). Although this model is developed at an individual household level, we also discuss designing interventions for groups of individuals. Chapter 4 generalizes the idea of group-level interventions by defining a new problem in which a service provider must determine the optimal bundles of products or services to offer its users while meeting an individual-level fairness constraint. This problem arises in settings such as healthcare and public policy (where services can be thought of as interventions or treatments), as well as retail settings in which fair out- come guarantees are desirable. We present two approximation algorithms for solving this problem. Lastly, Chapter 5 proposes and analyzes a new supply chain management intervention that increases efficiency in perishable food supply chains. This chapter studies a supply chain with multiple retailers who practice dual sourcing and compete with each other for supply, but do not have transparency to the inventory distributions of their suppliers a priori. A new downstream information sharing scheme is proposed that results in better ordering decisions that benefit the entire supply chain while decreasing food waste.","abstract_has_math":false,"creators":["Paulson, Elisabeth"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Operations Research Center","school":null,"contributors":[],"advisors":["Levi, Retsef","Perakis, Georgia"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09","date_published":"2021-09","updated_at":"2026-07-22T22:21:05Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/140371","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Levi, Retsef","Perakis, Georgia"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Operations Research Center"]},{"key":"dc:creator","label":"Author","values":["Paulson, Elisabeth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-02-15T17:02:46Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-02-15T17:02:46Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctoral","Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/140371"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In the U.S., about 19 million people reside in low income food deserts--neighborhoods where the majority of the population does not have access to large grocery stores. These areas are associated with less healthy diets and higher rates of poor health outcomes such as obesity and heart disease. Roughly 25% of cardiovascular-related deaths (140,000 deaths per year) in the U.S. can be attributed to low fruit and vegetable intake. Thus, increasing consumption of fruits and vegetables among underserved communities is a key priority of policymakers. At the same time, over half of the produce that is grown in the U.S. each year is wasted. Unfortunately, connecting surplus produce to households in need is a difficult task. This thesis uses advanced analytics to inform public interventions and supply chain design with the goal of increasing access to, and consumption of, fresh produce, particularly among underserved communities. Chapters 2, 3, and 4 focus on understanding the impact of, and optimizing, consumer-level interventions for increasing fruit and vegetable consumption among low income households. First, we perform an empirical analysis in order to understand which interventions are most effective as a function of the household attributes (Chapter 2). Based on these empirical findings, we develop a novel consumer behavioral model of grocery shopping dynamics, which is nested into a bi-level optimization model for determining the government’s optimal investments across three different types of food policy interventions: access, education, and price-related interventions (Chapter 3). Although this model is developed at an individual household level, we also discuss designing interventions for groups of individuals. Chapter 4 generalizes the idea of group-level interventions by defining a new problem in which a service provider must determine the optimal bundles of products or services to offer its users while meeting an individual-level fairness constraint. This problem arises in settings such as healthcare and public policy (where services can be thought of as interventions or treatments), as well as retail settings in which fair out- come guarantees are desirable. We present two approximation algorithms for solving this problem. Lastly, Chapter 5 proposes and analyzes a new supply chain management intervention that increases efficiency in perishable food supply chains. This chapter studies a supply chain with multiple retailers who practice dual sourcing and compete with each other for supply, but do not have transparency to the inventory distributions of their suppliers a priori. A new downstream information sharing scheme is proposed that results in better ordering decisions that benefit the entire supply chain while decreasing food waste."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Healthy Food Access and Consumption: Informing Interventions Through Analytics"]}]}],"canonical_facts":{"dc:contributor.advisor":["Levi, Retsef","Perakis, Georgia"],"dc:contributor.department":["Massachusetts Institute of Technology. Operations Research Center"],"dc:creator":["Paulson, Elisabeth"],"dc:date.accessioned":["2022-02-15T17:02:46Z"],"dc:date.available":["2022-02-15T17:02:46Z"],"dc:date.issued":["2021-09"],"dc:description.abstract":["In the U.S., about 19 million people reside in low income food deserts--neighborhoods where the majority of the population does not have access to large grocery stores. These areas are associated with less healthy diets and higher rates of poor health outcomes such as obesity and heart disease. Roughly 25% of cardiovascular-related deaths (140,000 deaths per year) in the U.S. can be attributed to low fruit and vegetable intake. Thus, increasing consumption of fruits and vegetables among underserved communities is a key priority of policymakers. At the same time, over half of the produce that is grown in the U.S. each year is wasted. Unfortunately, connecting surplus produce to households in need is a difficult task. This thesis uses advanced analytics to inform public interventions and supply chain design with the goal of increasing access to, and consumption of, fresh produce, particularly among underserved communities. Chapters 2, 3, and 4 focus on understanding the impact of, and optimizing, consumer-level interventions for increasing fruit and vegetable consumption among low income households. First, we perform an empirical analysis in order to understand which interventions are most effective as a function of the household attributes (Chapter 2). Based on these empirical findings, we develop a novel consumer behavioral model of grocery shopping dynamics, which is nested into a bi-level optimization model for determining the government’s optimal investments across three different types of food policy interventions: access, education, and price-related interventions (Chapter 3). Although this model is developed at an individual household level, we also discuss designing interventions for groups of individuals. Chapter 4 generalizes the idea of group-level interventions by defining a new problem in which a service provider must determine the optimal bundles of products or services to offer its users while meeting an individual-level fairness constraint. This problem arises in settings such as healthcare and public policy (where services can be thought of as interventions or treatments), as well as retail settings in which fair out- come guarantees are desirable. We present two approximation algorithms for solving this problem. Lastly, Chapter 5 proposes and analyzes a new supply chain management intervention that increases efficiency in perishable food supply chains. This chapter studies a supply chain with multiple retailers who practice dual sourcing and compete with each other for supply, but do not have transparency to the inventory distributions of their suppliers a priori. A new downstream information sharing scheme is proposed that results in better ordering decisions that benefit the entire supply chain while decreasing food waste."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/140371"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Healthy Food Access and Consumption: Informing Interventions Through Analytics"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:05Z"}