一个为定制的多级多标准大学时间表的数学.
1Institute for Operations Research, Discrete Optimization and Logistics, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany.
Annals of operations research
|June 26, 2023
概括
本研究介绍了大学时间表的多层次规划过程,优化讲座和教程时间表. 该方法使用数学和人工神经网络元模型生成高质量,定制的学生时间表.
科学领域:
- 运营研究 运营研究
- 教育技术的教育技术
- 计算机科学 计算机科学
背景情况:
- 大学课程时间表对于教育计划至关重要,平衡学生和讲师的偏好与工作量和置时间等规范标准.
- 现代的挑战包括为学生的个性需求定制时间表,整合在线课程,并适应灵活的学习环境,特别是流行后.
- 优化课程与讲座和辅导允许个性化学生的作业,以辅导槽.
研究的目的:
- 开发和评估大学时间表的多层次规划流程,以优化宏观层面 (讲座/教学计划) 和微观层面 (个人学生时间表) 的时间安排.
- 将学生的个人偏好和在线课程组件整合到时间表过程中.
- 通过先进的计算方法,通过平衡规范标准和个人偏好来提高时间表质量.
主要方法:
- 一个多层次的规划过程,涉及讲座和教学计划的战术层面和个人学生时间表的操作层面.
- 在数学框架内实施基于数学编程的规划过程,利用遗传算法进行优化.
- 开发一个人工神经网络元模型作为评估健身功能的代理,这涉及整个规划过程.
主要成果:
- 开发的多层次规划过程成功地产生了高质量的大学课程安排.
- 马修主义的方法,结合了遗传算法和ANN元模型,有效地优化了讲座计划,教程计划和个人学生的时间表.
- 计算结果表明该程序能够在整个大学课程中实现平衡的时间表绩效标准.
结论:
- 拟议的多层次规划流程为复杂的大学时间表挑战提供了强有力的解决方案,平衡了各种要求.
- 遗传算法和人工神经网络的整合为优化教育安排提供了一种有效的方法.
- 这种方法有助于创建定制和高质量的时间表,增强整体的大学教育经验.
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