Smart Classroom and Timetable Scheduler: A Review of Constraint-Based, Optimization, and Hybrid Scheduling Approaches

Building a college timetable takes far more than filling empty slots. Classes, rooms, teachers and students all have to fit together under firm rules and local preferences, while shared space keeps shrinking and teaching moves between in-person and online modes. This paper looks at five research papers on timetabling: a systematic review of practice in higher education, a mixed-integer programming (MIP) method, a hybrid-teaching model, a comparison of solvers from the International Timetabling Competition 2019 (ITC 2019), and a web-based Smart Classroom and Timetable Scheduler. Between them they draw on constraint validation, round-robin allocation, integer programming, matheuristics, metaheuristics and explicit modelling of online and in-person delivery. One study describes a two-stage MIP matheuristic that finished second at ITC 2019 and solved 29 of the 30 instances; it did well on small and medium cases but scaled poorly to very large ones. The ITC 2019 overview reports gaps of up to 94.46% against the available lower bounds, with five instances now proven optimal. The practical scheduler reports conflict-free timetables but gives no numerical benchmarks. Overall, the papers suggest that no single method suits every institution. A practical smart scheduler should pair checked input data and strict hard-constraint enforcement with a feasibility-first, improvement-second solving strategy, optional hybrid-teaching support, open evaluation and human review. Key open problems are scalability, benchmark coverage, fair evaluation, real-time rescheduling, and the risk of accepting a generated timetable without checking it.