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An heuristic genetic algorithm for strategic university tuition planning and workload balancing

机译：用于战略性大学学费计划和工作量平衡的启发式遗传算法

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摘要

The typical tuition planning challenge that universities face is to optimize students' and teachers' timetables on a weekly basis. While crucial for operational arrangement of tuition, such short-term planning is often done in relative isolation from longer-term strategic considerations, such as organizational competence development. This article presents an heuristic method, based on genetic algorithms, for long-term strategic planning of tuition for multi-year university degree programmes, at the semester level. The proposed method attempts to produce such a tuition schedule that satisfies given pre-requisite knowledge constraints, while honouring teacher-specific preferences, such that the workload across the teachers is as uniform as possible. The proposed method accommodates many real-world constraints, and can be applied to several distinct student groups advancing in parallel. As an application, the proposed method is used for optimizing the tuition schedule of a mechanical engineering degree programme at a Finnish university.
机译：大学面临的典型的学费计划挑战是每周优化学生和教师的时间表。虽然这对于学费的业务安排至关重要，但这样的短期计划通常是与长期战略考虑（例如组织能力发展）相对隔离地进行的。本文提出了一种基于遗传算法的启发式方法，用于在学期一级针对多年大学学位课程的学费进行长期战略规划。所提出的方法试图产生满足给定的先决条件知识约束的学费时间表，同时尊重教师特定的偏爱，从而使整个教师的工作量尽可能地统一。所提出的方法适应了许多现实世界的限制，并且可以应用于并行前进的几个不同的学生群体。作为一种应用，提出的方法用于优化芬兰大学机械工程学位课程的学费时间表。

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