Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning
Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning
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DOI：
10.1287/msom.2021.0996
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发表时间：
2021-11-05
影响因子：
6.3
通讯作者：
Yan, Julia
中科院分区：
文献类型：
--
作者：
Barnhart, Cynthia;Bertsimas, Dimitris;Yan, Julia
问题定义：新冠肺炎疫情期间的物理距离要求极大地降低了大学校园的有效容量。在这种情况下，我们研究了如何最大限度地利用新的稀缺资源来解决与课程规划和课程安排相关的问题。学术/实践相关性：我们提出了一个两阶段框架下大学课程安排问题的统一模型，并在展示如何适应个别细节的同时，在组件问题之间进行了类比。在大流行期间，我们的模型对于衡量几项创新建议的影响至关重要，包括扩大教学日历，跨多个房间教学，以及在一周和一学年轮换学生出勤率。方法：我们使用整数优化结合数千名过去学生的招生数据。我们的模型可以扩展到数千名注册数百门课程的单个学生。结果：我们预测，如果麻省理工学院从通常的两学期日历转变为三学期日历，每个学生亲自参加两个学期，90%以上的学生课程需求可以在不增加教师工作量的情况下在校园内得到满足。对于斯隆管理学院，我们制定了一个新的时间表，并于2020年秋季实施。该时间表允许一半的斯隆课程包括面对面的内容，同时遵守安全指南。尽管课堂容量减少了四倍，但它为斯隆管理学院三分之二的学生提供了至少一半课程的面对面学习机会。管理含义：整数优化可以在通常由大学管理人员手动管理的领域中进行大规模决策。我们的模型虽然受到了大流行的启发，但都是通用的，可以适用于严重能力限制下的任何调度问题。
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.