AI-Based Timetabling Algorithms: A Comparative Analysis
AI-based timetabling algorithms are keys in programmatically providing an ideal and conflict-free university class schedules among academic institutions worldwide periodically regardless of institutional structure or complexity of offered programs. Considered as an NP-hard problem, timetabling algorithms are founded on local search and optimization techniques which are the foundations of artificial intelligence’s (AI’s) base algorithms. Theoretically, timetabling algorithms look for optimum solutions rather than feasible ones, thus, incorporating a significant computational power and time in relation to schedule constraints. In this paper, the researchers evaluated four of the most commonly usedAI-based timetabling algorithms, namely, Tabu Search, Greedy Algorithm, Integer Linear Programming, and Bi-Partite Graph Approach, to determine which algorithms works best in terms of number of constraints, computation time, and computation resources.
Keywords - Timetabling Problem (TP), University Class Scheduling Problem (UCSP), local search and optimization techniques