We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.

利用数学规划与机器学习技术优化美国希腊学院学生的课程安排，包括最快完成时间、课程难度平衡等指标，最为重要的目标是根据学生往年表现最大化预期学生成绩，结果表明，该优化方案可以保证所有安排的合理性，运用最大化学生成绩的策略较人工安排方案更加优化，且总规划时间可以在10秒内解决。

规划希腊美国学院学生成功的课程