# Optimizing the Scheduling of Teaching Activities in a Faculty
## 9 Conclusions  
The present research delves into the subject of automated timetable creation in an academic institution and examines the background, rationale, aims, and anticipated ramifications of the investigation. The objective of this study is to enhance the scheduling of educational tasks through the creation of an automated framework that produces effective and conflict-free schedules while satisfying the preferences and limitations of both faculty and students. The present study concerns the optimization of didactic activities, which draws inspiration from the widely recognized timetable problem. The optimization of timetabling poses a complex challenge that pertains to the category of scheduling problems. They manifest in diverse fields, including education, healthcare, transportation, sports, and entertainment. The aforementioned issues are recognized as NP-complete, necessitating the utilization of heuristic methodologies to attain efficient resolutions. The implementation of an automated system for generating timetables offers a multitude of advantages to an academic institution. The implementation of automation technology leads to a reduction in manual labor, a decrease in the occurrence of errors, and an optimization of resource allocation, ultimately resulting in heightened levels of efficiency and productivity. The focus of this study on tackling the obstacles posed by a range of distinct requirements, limitations, and preferences makes a valuable contribution to the domains of computer science, operations research, and artificial intelligence. To accomplish the aims of this study, a proposed model has been put forth to generate an automatic timetable system. The implemented model integrates a web-based platform that has been constructed utilizing Angular to facilitate the frontend, Java in conjunction with Spring Boot to support the backend, and the Timefold Solver API for the timetable generation algorithm. This study has considered the architectural design, methodologies, algorithms, and appropriateness of a database. In addition, a research methodology has been delineated to provide direction for this study. This methodology encompasses problem analysis, literature review, system design, implementation, data collection and integration, performance evaluation, and iterative refinement. The utilization of a methodology guarantees a methodical and thorough strategy to effectively tackle the research objectives and challenges. Regarding performance analysis measures, the evaluation metrics have been deliberately upon to appraise the excellence of the produced timetables, computational efficiency, user contentment, comparison with manual timetabling, empirical experimentation, and data analysis methodologies. These metrics offer valuable perspectives on the efficacy, productivity, and user-friendliness of the system, facilitating enhancements and refinements. The results obtained from the experiments demonstrate the effectiveness and limitations of the algorithm on different hardware configurations. On the MacBook Pro M1 (2021), the algorithm achieved the best score of \(-284\) init/0 hard/\(-2102\) medium/7629 soft after eight hours and 0 hard/\(-2183\) medium/8121 soft after twelve hours, indicating improvements in soft constraint scores but increased medium constraint violations over extended runtime. On the MacBook Air M1 (2020), shorter runtimes (5, 8, or 11 h) resulted in higher unassigned counts and lower soft constraint scores, necessitating at least 16 h to achieve a feasible solution. An alternative configuration that first assigned timeslots led to significant hard and medium constraint violations, suggesting a less effective approach. In conclusion, this study on the implementation of automated approaches for creating timetables in a higher education institution demonstrates promise as an effective solution for the complex task of planning educational activities.