基于混合教学的大学课程排课问题建模与求解。

IF 1.4 4区 工程技术 Q4 ENGINEERING, MANUFACTURING
Journal of Scheduling Pub Date : 2025-01-01 Epub Date: 2024-10-20 DOI:10.1007/s10951-024-00817-w
Matthew Davison, Ahmed Kheiri, Konstantinos G Zografos
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引用次数: 0

摘要

大学课程排课问题是一个具有挑战性的问题。随着大学的发展,这个问题的特征也发生了变化。一个新兴的特点是混合教学,课程可以在线授课,也可以面对面授课,或者面对面授课和在线授课的结合。这项工作提出了一个多目标二进制规划模型,其中包括从文献中确定的常见大学时间表特征,以及混合教学特征。本文概述了一种词典编纂解决方案方法,并使用基准数据进行计算实验,以演示该模型的关键方面,并探索所考虑的目标之间的权衡。实验结果表明,该模型可用于寻找包含混合教学的大学的需求驱动时间表。他们还展示了如何使用该模型为参与大学战略决策的实践者提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling and solving the university course timetabling problem with hybrid teaching considerations.

The university course timetabling problem is a challenging problem to solve. As universities have evolved, the features of this problem have changed. One emerging feature is hybrid teaching where classes can be taught online, in-person or a combination of both in-person and online. This work presents a multi-objective binary programming model that includes common university timetabling features, identified from the literature, as well as hybrid teaching features. A lexicographic solution method is outlined and computational experiments using benchmark data are used to demonstrate the key aspects of the model and explore trade-offs among the objectives considered. The results of these experiments demonstrate that the model can be used to find demand-driven schedules for universities that include hybrid teaching. They also show how the model could be used to inform practitioners who are involved in strategic decision-making at universities.

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来源期刊
Journal of Scheduling
Journal of Scheduling 工程技术-工程:制造
CiteScore
3.80
自引率
10.00%
发文量
49
审稿时长
6-12 weeks
期刊介绍: The Journal of Scheduling provides a recognized global forum for the publication of all forms of scheduling research. First published in June 1998, Journal of Scheduling covers advances in scheduling research, such as the latest techniques, applications, theoretical issues and novel approaches to problems. The journal is of direct relevance to the areas of Computer Science, Discrete Mathematics, Operational Research, Engineering, Management, Artificial Intelligence, Construction, Distribution, Manufacturing, Transport, Aerospace and Retail and Service Industries. These disciplines face complex scheduling needs and all stand to gain from advances in scheduling technology and understanding.
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