A Bi-criteria Hybrid Genetic Algorithm with Robustness Objective for the Course Timetabling Problem

Abstract

Traditional methods of generating timetables may not yield robust solutions that may easily be adapted to changing inputs. Incorporating late changes by making minimum modifications is an important need in many practical applications of timetabling. Here, we first define a robustness measure for the International Timetabling Competition 2007 (ITC-2007) Curriculum-Based Course Timetabling Problem [5], and then try to find a set of good solutions in terms of both penalty and robustness values. We model the problem as a bi-criteria optimization problem and solve it by a hybrid Multi-objective Genetic Algorithm (MOGA), which makes use of hill-climbing and Simulated Annealing algorithms in addition to the standard Genetic Algorithm (GA) approach.