Paper Title
AI-Based Timetabling Algorithms: A Comparative Analysis
Abstract
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