CampusSmart Scheduler: AI-Based Automated Timetable Management System

Designing an academic timetable is a complex and time-consuming task that requires balancing multiple constraints related to classrooms, faculty availability, student batches, and institutional policies. This challenge, formally known as the University Course Timetabling Problem (UCTP), belongs to the class of NPhard combinatorial optimization problems due to its vast search space and tightly coupled constraints. In most universities, timetable preparation is still carried out manually, often taking 12–15 days and resulting in poor utilization of physical resources, typically below 40%. This paper presents Campus Smart Scheduler, an intelligent and automated timetable management system developed using the OptaPlannerconstraint-solving framework. The proposed solution models the UCTP as a Weighted Constraint Satisfaction Problem (WCSP) and employs a hybrid approach that combines Constraint Satisfaction Programming for feasibility with Genetic Algorithms for optimization. A key contribution of this work is a mathematically defined softconstraint model that explicitly promotes smart space utilization by penalizing room underutilization and fragmented scheduling. Experimental results demonstrate significant improvements in scheduling speed, feasibility, and room utilization efficiency, making the system a practical and scalable solution for modern academic institutions.