{"title":"Exam scheduling under pandemic conditions: A mathematical model and decision support system","authors":"","doi":"10.1016/j.techfore.2024.123687","DOIUrl":null,"url":null,"abstract":"<div><p>The scheduling of university exams is a complex task that involves various constraints such as administrative limits, pedagogical needs, student volume, and different courses. The emergence of Covid-19 and future pandemics has added new constraints related to infection prevention and contact tracing. To address these challenges, this study proposes a multi-objective mathematical model that considers university resources, reduced classroom occupancy, and minimized student interaction. The model aims to minimize violations of pandemic-related constraints and categorize exams by difficulty. To facilitate scheduling for entire faculties or universities, a Genetic Algorithm based web-based decision support system is developed. With these tools, the study successfully created an optimal schedule for eight departments simultaneously.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technological Forecasting and Social Change","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0040162524004852","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
The scheduling of university exams is a complex task that involves various constraints such as administrative limits, pedagogical needs, student volume, and different courses. The emergence of Covid-19 and future pandemics has added new constraints related to infection prevention and contact tracing. To address these challenges, this study proposes a multi-objective mathematical model that considers university resources, reduced classroom occupancy, and minimized student interaction. The model aims to minimize violations of pandemic-related constraints and categorize exams by difficulty. To facilitate scheduling for entire faculties or universities, a Genetic Algorithm based web-based decision support system is developed. With these tools, the study successfully created an optimal schedule for eight departments simultaneously.
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