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dc.contributor.authorBarnhart, Cynthia
dc.contributor.authorBertsimas, Dimitris
dc.contributor.authorDelarue, Arthur
dc.contributor.authorYan, Julia
dc.date.accessioned2022-07-27T15:34:43Z
dc.date.available2022-07-27T15:34:43Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/144070
dc.description.abstract<jats:p> Problem definition: Physical distancing requirements during the COVID-19 pandemic have dramatically reduced the effective capacity of university campuses. Under these conditions, we examine how to make the most of newly scarce resources in the related problems of curriculum planning and course timetabling. Academic/practical relevance: We propose a unified model for university course scheduling problems under a two-stage framework and draw parallels between component problems while showing how to accommodate individual specifics. During the pandemic, our models were critical to measuring the impact of several innovative proposals, including expanding the academic calendar, teaching across multiple rooms, and rotating student attendance through the week and school year. Methodology: We use integer optimization combined with enrollment data from thousands of past students. Our models scale to thousands of individual students enrolled in hundreds of courses. Results: We projected that if Massachusetts Institute of Technology moved from its usual two-semester calendar to a three-semester calendar, with each student attending two semesters in person, more than 90% of student course demand could be satisfied on campus without increasing faculty workloads. For the Sloan School of Management, we produced a new schedule that was implemented in fall 2020. The schedule allowed half of Sloan courses to include an in-person component while adhering to safety guidelines. Despite a fourfold reduction in classroom capacity, it afforded two thirds of Sloan students the opportunity for in-person learning in at least half their courses. Managerial implications: Integer optimization can enable decision making at a large scale in a domain that is usually managed manually by university administrators. Our models, although inspired by the pandemic, are generic and could apply to any scheduling problem under severe capacity constraints. </jats:p>en_US
dc.language.isoen
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.relation.isversionof10.1287/MSOM.2021.0996en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleCourse Scheduling Under Sudden Scarcity: Applications to Pandemic Planningen_US
dc.typeArticleen_US
dc.identifier.citationBarnhart, Cynthia, Bertsimas, Dimitris, Delarue, Arthur and Yan, Julia. 2022. "Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning." Manufacturing and Service Operations Management, 24 (2).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.contributor.departmentSloan School of Management
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center
dc.relation.journalManufacturing and Service Operations Managementen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-27T15:31:02Z
dspace.orderedauthorsBarnhart, C; Bertsimas, D; Delarue, A; Yan, Jen_US
dspace.date.submission2022-07-27T15:31:03Z
mit.journal.volume24en_US
mit.journal.issue2en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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