O. Gervasi and M. Gavrilova (Eds.): ICCSA 2007, LNCS 4707, Part III, pp. 611–624, 2007. © Springer-Verlag Berlin Heidelberg 2007 Solving a Practical Examination Timetabling Problem: A Case Study Masri Ayob 1 , Ariff Md Ab Malik 1 , Salwani Abdullah 1 , Abdul Razak Hamdan 1 , Graham Kendall 2 , and Rong Qu 2 1 Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi,Selangor, Malaysia {masri,salwani,arh}@ftsm.ukm.my, ariff215@salam.uitm.edu.my 2 ASAP Research Group, School of Computer Science and Information Technology, The University of Nottingham,Nottingham NG8 1BB, UK {gxk,rxq}@cs.nott.ac.uk Abstract. This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of the least saturation degree heuristic) to solve a real-world examination timetabling problem at the University Kebangsaan, Malaysia. We utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. This heuristic has the potential to be used for the benchmark examination datasets (e.g. the Carter datasets) as well as other real world problems. Computational results are presented. Keywords: Timetabling, Heuristic, Graph colouring. 1 Introduction The examination timetabling problem is characterised by assigning a set of exams into a limited number of timeslots subject to a set of constraints (see [1], [2], [3], [12], [16], and [20]). These constraints are usually classified as hard and soft constraints. The hard constraints must be completely satisfied where solutions that satisfy all the hard constraints are called feasible. On the other hand, there might be some requirements that are not essential but should be satisfied as far as possible, which are referred to as soft constraints. Common hard constraints for examination timetabling problem are: (i) no student should be required to sit two exams at the same time and (ii) the scheduled exams must not exceed the room capacity. However, in practical examination timetabling problem, there are many other constraints, which are vary among institutions. Similarly in our dataset, we have some unique hard constraints, which we describe in section 2. A particularly common soft constraint aims to spread exams as evenly as possible throughout the schedule. Due to the complexity of the problem, it is not usually possible to have solutions that do not violate some of the soft constraints. Indeed, the