Constructing an optimized, conflict-free academic timetable ranks among the most computationally challenging problems in educational administration, formally classified as NP-hard within combinatorial optimization. The traditional approach of preparing schedules by hand is highly labor-intensive, prone to scheduling conflicts, and routinely consumes several working days. This paper introduces an Automated Timetable Generation System that overcomes these limitations through the integrated use of Constraint Satisfaction Problem (CSP) encoding and Genetic Algorithm (GA) optimization. Accepting structured inputs that include faculty availability, subject-faculty mappings, classroom capacities, and institutional time slot configurations, the system autonomously produces optimized, clash-free timetables spanning multiple departments and semesters. Hard scheduling constraints — faculty non-overlap, room non-overlap, and section non-overlap — are strictly enforced via CSP, whereas soft preferences such as faculty time slot choices and balanced workload allocation are progressively refined through the evolutionary search. Architecturally, the system rests on a three-tier web platform: Python with Flask powering the backend scheduling engine, React.js driving the frontend interface, and MySQL managing persistent data. Experiments conducted across institutions of varying scales confirm that schedule generation time drops from multiple days to under five minutes, with hard constraint fulfillment consistently exceeding 95%. Additional capabilities include automated conflict reporting, one-click PDF and Excel export, and institution-wide scalable deployment.
Ms.S.Sugarthi et al. (Thu,) studied this question.