Application of Artificial Intelligence and optimization algorithms in academic timetabling using a real university dataset: a simulated annealing approach - Miranda, Victor de Andrade; Garvão, Matheus Vinícius Pires da Silva; Silva, Ricardo Petri da; Tanaka, Sergio Akio (Revista Terra & Cultura: Cadernos de Ensino e Pesquisa; Vol 40 No especial (2024): Prêmio Reynaldo Camargo Neves 2024 e Bolsa Fundação Araucária, 2024)
This paper investigates the challenge of academic timetabling in higher education institutions, categorized as the University Course Timetabling Problem (UCTP), which is characterized by its high combinatorial complexity and classified as NP-hard. The study utilizes a real dataset from a university, containing data on rooms, professors, courses, and allocation preferences. The proposed solution involves the application of Artificial Intelligence (AI) techniques and optimization algorithms to automate and enhance the scheduling process. Using Java and the Timefold AI framework, the Simulated Annealing algorithm was employed to seek solutions that minimize hard and soft constraints, such as room conflicts, class overcrowding, and teacher preferences. Preliminary results indicate that the methodology, when applied to the real dataset , effectively reduces scheduling time and improves the quality of the timetables, offering a viable and scalable solution for university scheduling problems.