This project provides a hybrid solution for the automated generation of timetables through the combination of Linear Programming (LP) and Constraint Satisfaction Problem (CSP) solutions. It's intended to address the NP-hard problem of university course timetabling, which involves the satisfaction of a considerable number of conflicting constraints and preferences. The method for solving this problem involves two distinct steps. In the first step, a linear programming method is employed to determine the optimal assignment of teachers to subjects. This is a global optimisation technique that aims to maximize the satisfaction of teacher preferences and ensure a well-balanced allocation of teaching loads for all faculty members. In the second phase, a CSP algorithm with backtracking is applied to these optimal assignments to further assign time slots and classrooms. This phase is handled by both hard and soft constraints. Hard constraints, such as the unavailability of a teacher or a classroom, are represented by hard constraints that should not be violated to avoid any scheduling conflicts. Soft constraints, such as teacher time slot preferences and constraints on the lecture interval, are employed to filter the most optimal assignments from the pool of feasible solutions to enhance the quality of the schedule.
Balakrishnan et al. (Thu,) studied this question.