Abstract
This work presents the Late Acceptance Randomized Descent Algorithm (LARD) and the Late Acceptance Strategy in Hill Climbing (LAHC) to solve university course timetabling problems. The aim of this work is to produce an effective algorithm for assigning a set of courses (events) and students to a specific number of rooms and timeslots, subject to a set of constraints. LAHC approach was originally introduced by Burke and Bykove for exam timetabling problem. LAHC can be embedded into any search method. LARD differs from the basic LAHC, as it use randomized decent method instead of using simple hill climbing. We evaluate the effectiveness of LARD algorithm by testing it on standard benchmark course timetabling datasets, which were introduced by Socha; and compared to the basic late acceptance strategy in hill climbing with fair comparison. Results show that LARD significantly outperformed LAHC in some instances. Results also show that LARD is able to produce good quality solutions, which is comparable to other work in the literature.