We propose an algorithm selection approach and an instance space analysis for the well-known curriculum-based course timetabling problem (CB-CTT), which is an important problem for its application in higher education. Several state of the art algorithms exist, including both exact and metaheuristic methods. Results of these algorithms on existing instances in the literature show that there is no single algorithm outperforming the others. Therefore, a deep analysis of the strengths and weaknesses of these algorithms, depending on the instance, is an important research question. In this work, a detailed analysis of the instance space for CB-CTT is performed, charting the regions where these algorithms perform best. We further investigate the application of machine learning methods to automated algorithm selection for CB-CTT, strengthening the insights gained through the instance space analysis. For our research, we contribute new real-life instances and extend the generation of synthetic instances to better correspond to these new instances. Finally, this work shows how instance space analysis and the application of algorithm selection complement each other, underlining the value of both approaches in understanding algorithm performance.

我们针对著名的基于课程的课程时间表问题 (CB-CTT) 提出了一种算法选择方法和实例空间分析，这是其在高等教育中应用的一个重要问题。存在几种最先进的算法，包括精确和元启发式方法。这些算法对文献中现有实例的结果表明，没有一种算法能胜过其他算法。因此，根据实例深入分析这些算法的优缺点是一个重要的研究问题。在这项工作中，对 CB-CTT 的实例空间进行了详细分析，绘制了这些算法表现最佳的区域。我们进一步研究了机器学习方法在 CB-CTT 自动算法选择中的应用，加强通过实例空间分析获得的见解。对于我们的研究，我们贡献了新的现实生活实例并扩展合成实例的生成以更好地对应这些新实例。最后，这项工作展示了实例空间分析和算法选择的应用如何相互补充，强调了这两种方法在理解算法性能方面的价值。