Project Almanac is the result of the dedication of two SLTC Research University undergraduates in completing their final year project in Electronics and Telecommunication Engineering. Fueled by the desire to innovate, they harnessed technologies such as High-Performance Computing (HPC), Machine Learning (ML), Artificial Intelligence (AI) and Cluster Computing to transform timetable scheduling into new heights. This venture stands as a testament to their commitment to efficiency and optimized problem-solving, offering a solution that optimizes timetable scheduling while impacting the industries to evolve with the new technology.
Project Almanac starts with the question 'Can we use the power of machine learning to create an optimized timetable scheduling algorithm and optimize it using high-performance computing?' This led the two students on a journey to create a hardware, and software implementation that does exactly that. The journey took place on the premises of the SLTC Research University, Meepe, Sri Lanka. By first starting the research through tireless visits to the library and research online through a lot of research articles, the initial conceptual understanding was met. Then it was a matter of development of the architecture and the system itself. This journey has been a learning experience like no other, creating a result that is new and innovative.
The highest recognition for Project Almanac so far came when it was featured in the 8th International Conference on Information Technology Research held on the 7th and 8th of December, 2023 at the University of Moratuwa, Sri Lanka. The conference was aimed at promoting research in ICT and scientific exchange among researchers and scholars. The research paper titled 'Enhanced Timetable Scheduling: A High- Performance Computational Approach' was published in the IEEE Xplore digital library on the 10th of January, 2024. This recognition will be a strong driving factor to propel the project even further into newer heights.
Since the early '90s, researchers have tried to use technologies such as Operational Methods, Human-Machine Interaction and Artificial Intelligence for optimizing timetable scheduling. Most of this research revolves around Particle Swarm Optimization, Genetic Algorithm, Tabu Search, etc. However, it was evident that most of this research didn't take a modular approach to timetable scheduling while focusing towards an optimum solution rather than a good one. This the project felt, was a research gap that must be addressed. Along these lines, research was carried on further which led to countless research that dealt with the same subject of timetable optimization.

Former Senior Lecturer, SLTC Research University

Head of IT, SLTC Research University

Former Head of IT, SLTC Research University
