A memetic algorithm for the university course timetabling problem

This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods

Solving Examination Timetabling Problem using Partial Exam Assignment with Great Deluge Algorithm

Constructing a quality solution for the examination timetable problem is a difficult task. This paper presents a partial exam assignment approach with great deluge algorithm as the improvement mechanism in order to generate good quality timetable. In this approach, exams are ordered based on graph heuristics and only selected exams (partial exams) are scheduled first and then improved using great deluge algorithm. The entire process continues until all of the exams have been scheduled. We implement the proposed technique on the Toronto benchmark datasets. Experimental results indicate that in all problem instances, this proposed method outperforms traditional great deluge algorithm and when comparing with the state-of-the-art approaches, our approach produces competitive solution for all instances, with some cases outperform other reported result

Domain transformation approach to deterministic optimization of examination timetables

In this paper we introduce a new optimization method for the examinations scheduling problem. Rather than attempting direct optimization of assignments of exams to specific time-slots, we perform permutations of slots and reassignments of exams upon the feasible (but not optimal) schedules obtained by the standard graph colouring method with Largest Degree ordering. The proposed optimization methods have been evaluated on the University of Toronto, University of Nottingham and International Timetabling Competition (ITC2007) datasets. It is shown that the proposed method delivers competitive results compared to other constructive methods in the timetabling literature on both the Nottingham and Toronto datasets, and it maintains the same optimization pattern of the solution improvement on the ITC2007 dataset. A deterministic pattern obtained for all benchmark datasets, makes the proposed method more understandable to the users

Sequential constructive algorithm incorporate with fuzzy logic for solving real world course timetabling problem

Sequential constructive algorithm is one of the popular methods for solving timetabling problems. The concept of the algorithm is to assign event based on their difficulty value by using different sequential heuristic. The most common sequential heuristics are largest enrolment, largest degree and saturation degree. Each sequential heuristic has its own criteria to obtain events’ difficulty value. Instead of single sequential heuristic, this paper presents to use fuzzy logic to consider multiple sequential heuristics in order to obtain the difficulty value of the events. The proposed method will be used to generate feasible solution as well as improve the quality of the solution. Another objective of this paper is to tackle a real world course timetabling problem from Universiti Malaysia Sabah Labuan International Campus (UMSLIC). Currently, UMSLIC generates course timetable manually which is very time consuming and ineffective.The experimental results show that the proposed method is able to produce better quality of solution less than one minute. In terms of quality of timetable and efficiency, the proposed method is outperforming UMSLIC’s manual method