{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/127294"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/127294","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"School choice : a discrete optimization approach","abstract":"An equitable and flexible mechanism for assigning students to schools is a major concern for many school districts. The school a student attends dramatically impacts the quality of education, access to resources, family and neighborhood cohesion, and transportation costs. Facing this intricate optimization problem, school districts often utilize to stable-matching techniques which only produce stable matchings that do not incorporate these different objectives; this can be expensive and inequitable. We present a new optimization model for the Stable Matching (SM) school choice problem which relies on an algorithm we call Price-Costs-Flexibility-and- Fairness (PCF2). Our model leverages techniques to balance competing objectives using mixed-integer optimization methods. We explore the trade-offs between stability, costs, and preferences and show that, surprisingly, there are stable solutions that decrease transportation costs by 8-17% over the Gale-Shapley solution.","abstract_html":"An equitable and flexible mechanism for assigning students to schools is a major concern for many school districts. The school a student attends dramatically impacts the quality of education, access to resources, family and neighborhood cohesion, and transportation costs. Facing this intricate optimization problem, school districts often utilize to stable-matching techniques which only produce stable matchings that do not incorporate these different objectives; this can be expensive and inequitable. We present a new optimization model for the Stable Matching (SM) school choice problem which relies on an algorithm we call Price-Costs-Flexibility-and- Fairness (PCF2). Our model leverages techniques to balance competing objectives using mixed-integer optimization methods. We explore the trade-offs between stability, costs, and preferences and show that, surprisingly, there are stable solutions that decrease transportation costs by 8-17% over the Gale-Shapley solution.","abstract_has_math":false,"creators":["Graham, Justin W."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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The school a student attends dramatically impacts the quality of education, access to resources, family and neighborhood cohesion, and transportation costs. Facing this intricate optimization problem, school districts often utilize to stable-matching techniques which only produce stable matchings that do not incorporate these different objectives; this can be expensive and inequitable. We present a new optimization model for the Stable Matching (SM) school choice problem which relies on an algorithm we call Price-Costs-Flexibility-and- Fairness (PCF2). Our model leverages techniques to balance competing objectives using mixed-integer optimization methods. We explore the trade-offs between stability, costs, and preferences and show that, surprisingly, there are stable solutions that decrease transportation costs by 8-17% over the Gale-Shapley solution."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["School choice : a discrete optimization approach"]}]}],"canonical_facts":{"dc:contributor.advisor":["Dimitris Bertsimas."],"dc:contributor.department":["Massachusetts Institute of Technology. Operations Research Center","Sloan","OperRes"],"dc:contributor.other":["Massachusetts Institute of Technology. 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We present a new optimization model for the Stable Matching (SM) school choice problem which relies on an algorithm we call Price-Costs-Flexibility-and- Fairness (PCF2). Our model leverages techniques to balance competing objectives using mixed-integer optimization methods. We explore the trade-offs between stability, costs, and preferences and show that, surprisingly, there are stable solutions that decrease transportation costs by 8-17% over the Gale-Shapley solution."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/127294"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. 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