This paper presents a novel decision support system (DSS) to address the University Course Timetabling Problem (UCTP). The solution decomposes the NP-complete UCTP into two sub-problems, allowing a structured approach to addressing the complexities inherent in the UCTP process. A mixed integer linear programming (MILP) model is proposed to integrate academic year course schedule planning and instructor assignment, accommodating various constraints to meet student demands. The model optimizes the number of course sections and strategically schedules instructors, aiming to reduce the number of new and distinct courses assigned to them. Historical data from an academic department encompassing multiple disciplines, including Computer Information Systems, Business Management, and Business Analytics, at a large public university in the U.S. is used to develop the model, and the results are compared with the actual course schedule and instructor assignment. The results demonstrate that the proposed DSS would result in a 14 % reduction in the number of course sections offered, translating to approximately $130,000 in annual savings. Additionally, it could significantly reduce the number of new courses assigned to instructors by up to 81 % and the number of distinct course sections assigned to them by 29 %.

中文翻译：

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通过改进规划应对人员配备挑战：高等教育中需求驱动的课程安排规划和教师分配


本文提出了一种新的决策支持系统 （DSS） 来解决大学课程时间表问题 （UCTP）。该解决方案将 NP 完全 UCTP 分解为两个子问题，允许采用结构化方法来解决 UCTP 过程中固有的复杂性。提出了一种混合整数线性规划 （MILP） 模型来整合学年课程计划规划和教师分配，适应各种约束条件以满足学生的需求。该模型优化了课程部分的数量，并战略性地安排了教师，旨在减少分配给他们的新课程和不同课程的数量。使用来自美国一所大型公立大学涵盖多个学科（包括计算机信息系统、商业管理和商业分析）的学术部门的历史数据来开发该模型，并将结果与实际课程安排和教师分配进行比较。结果表明，拟议的 DSS 将导致提供的课程部分数量减少 14%，相当于每年节省约 130,000 美元。此外，它可以显著减少分配给教师的新课程数量高达 81%，并将分配给他们的不同课程部分的数量减少 29%。