Hamza Turabieh

Generating University Course Timetable Using Genetic Algorithms and Local Search

In this paper we establish a new algorithm based on genetic algorithms (GA) and sequential local search to solve course timetabling problem. Universities are challenged to arise in number of complexity, their resources and events are becoming harder to schedule. Timetabling is a kind of problem in which events (classes, exams, courses, etc) have to be arranged into a number of timeslots such that conflicts in using a given set of resources are avoided. We perform preliminary experiments on standard benchmark course timetable problems and able to produce promising results.

On the use of multi neighbourhood structures within a Tabu-based memetic approach to university timetabling problems

Finding a good university timetabling system is not a simple task for a higher educational organisation. As a result, many approaches to generating sufficiently good solutions have been introduced. This is mainly due to the high complexity within the search landscape; moreover, each educational organisation has its own rules and specifications. In this paper, a Tabu-based memetic algorithm that hybridises a genetic algorithm with a Tabu Search algorithm is proposed as an improved algorithm for university timetabling problems. This algorithm is employed on a set of neighbourhood structures during the search process with the aim of gaining significant improvements in solution quality. The sequence of neighbourhood structures has been considered to understand its effect on the search space. Random, best and general sequences of neighbourhood structures have been evaluated in this work. The concept of a Tabu list is embedded to control the selection of neighbourhood structures that are not dependent on the problem domains during the optimisation process after the crossover and mutation operators are applied to the selected solutions from the population pool. The algorithm will penalise neighbourhood structures that are unable to generate better solutions. The proposed algorithm has been applied and evaluated against the latest methodologies in the literature with respect to standard benchmark problems. We demonstrate that the proposed algorithm produces some of the best known results when tested on ITC2007 competition datasets.

Electromagnetism-like Mechanism with Force Decay Rate Great Deluge for the Course Timetabling Problem

Combinations of population-based approaches with local search have provided very good results for a variety of scheduling problems. This paper describes the development of a population-based algorithm called Electromagnetism-like mechanism with force decay rate great deluge algorithm for university course timetabling. This problem is concerned with the assignment of lectures to a specific numbers of timeslots and rooms. For a solution to be feasible, a number of hard constraints must be satisfied. A penalty value which represents the degree to which various soft constraints are satisfied is measured which reflects the quality of the solution. This approach is tested over established datasets and compared against state-of-the-art techniques from the literature. The results obtained confirm that the approach is able to produce solutions to the course timetabling problem which demonstrate some of the lowest penalty values in the literature on these benchmark problems.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

A Hybridization of Electromagnetic-Like Mechanism and Great Deluge for Examination Timetabling Problems

In this paper, we present a hybridization of an electromagnetic-like mechanism (EM) and the great deluge (GD) algorithm. This technique can be seen as a dynamic approach as an estimated quality of a new solution and a decay rate are calculated each iteration during the search process. These values are depending on a force value calculated using the EM approach. It is observed that applying these dynamic values help generate high quality solutions. Experimental results on benchmark examination timetabling problems demonstrate the effectiveness of this hybrid EM-GD approach compared with previous available methods. Possible extensions upon this simple approach are also discussed.

Incorporating tabu search into memetic approach for enrolment-based course timetabling problems

This work presents a tabu search and a memetic approach to an enrolment based course timetabling problem called Tabu-based memetic algorithm, the proposed approach employed crossover and mutation operators to a selected solution from the population. Then applying neighborhood structure randomly which is not in tabu-list to enhance the quality of the solution. The tabu list is used to penalize neighborhood structures that are unable to generate better solutions. We demonstrate that our approach is able to produce good quality solutions due to the ability to select more promising neighborhood structures.

A hybrid fish swarm optimisation algorithm for solving examination timetabling problems

A hybrid fish swarm algorithm has been proposed to solve exam timetabling problems where the movement of the fish is simulated when searching for food inside water (refer as a search space). The search space is categorised into three categories which are crowded, not crowded and empty areas. The movement of fish (where the fish represents the solution) is determined based on a Nelder-Mead simplex search algorithm. The quality of the solution is enhanced using a great deluge algorithm or a steepest descent algorithm. The proposed hybrid approach is tested on a set of benchmark examination timetabling problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test problem.

A constructive hyper-heuristics for rough set attribute reduction

Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving attribute reduction problems. At the high level, the hyper-heuristics (at each iteration) adaptively select the most suitable low level heuristics using roulette wheel selection mechanism. Whilst, at the underlying low level, four low level heuristics are used to gradually, and indirectly construct the solution. The proposed hyper-heuristics has been evaluated on a widely used UCI datasets. Results show that our hyper-heuristic produces good quality solutions when compared against other metaheuristic and outperforms other approaches on some benchmark instances.

A tabu-based memetic approach for examination timetabling problems

Constructing examination timetable for higher educational institutions is a very complex task due to the complexity of the issues involved. The objective of examination timetabling problem is to satisfy the hard constraints and minimize the violations of soft constraints. In this work, a tabu-based memetic approach has been applied and evaluated against the latest methodologies in the literature on standard benchmark problems. The approach hybridizes the concepts of tabu search and memetic algorithms. A tabu list is used to penalise neighbourhood structures that are unable to generate better solutions after the crossover and mutation operators have been applied to the selected solutions from the population pool. We demonstrate that our approach is able to enhance the quality of the solutions by carefully selecting the effective neighbourhood structures. Hence, some best known results have been obtained.

Fish swarm intelligent algorithm for the course timetabling problem

In this work, a simulation of fish swarm intelligence has been applied on the course timetabling problem. The proposed algorithm simulates the movements of the fish when searching for food inside a body of water (refer as a search space). The search space is classified based on the visual scope of fishes into three categories which are crowded, not crowded and empty areas. Each fish represents a solution in the solution population. The movement direction of solutions is determined based on a Nelder-Mead simplex algorithm. Two types of local search i.e. a multi decay rate great deluge (where the decay rate is intelligently controlled by the movement direction) and a steepest descent algorithm have been applied to enhance the quality of the solution. The performance of the proposed approach has been tested on a standard course timetabling problem. Computational experiments indicate that our approach produces best known results on a number of these benchmark problems.