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

Examination timetabling automation using hybrid meta-heuristics

Trabalho de projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de ComputadoresNos últimos anos, o tema da geração automática de horários tem sido alvo de muito estudo. Em muitas instituições, a elaboração de horários ainda é feita manualmente, constituindo-se uma tarefa demorada e penosa para instâncias de grande dimensão. Outro problema recorrente na abordagem manual é a existência de falhas dada a dificuldade do processo de verificação, e também a qualidade final do horário produzido. Se este fosse criado por computador, o horário seria válido e seriam de esperar horários com qualidade superior dada a capacidade do computador para pesquisar o espaço de soluções. A elaboração de horários não é uma tarefa fácil, mesmo para uma máquina. Por exemplo, horários escolares necessitam de seguir certas regras para que seja possível a criação de um horário válido. Mas como o espaço de estados (soluções) válidas é tão vasto, é impraticável criar um algoritmo que faça a enumeração completa de soluções a fim de escolher a melhor solução possível. Por outro lado, a utilização de algoritmos que realizam a enumeração implícita de soluções (por exemplo, branch and bound), não é viável para problemas de grande dimensão. A utilização de heurísticas que percorrem de uma forma guiada o espaço de estados, conseguindo assim uma solução razoável em tempo útil, constituem uma abordagem adequada para este tipo de problemas. Um dos objetivos do projeto consiste na criação duma abordagem que siga as regras do International Timetabling Competition (ITC) 2007 incidindo na criação de horários de exames em universidades (Examination timetabling track). Este projeto utiliza uma abordagem de heurísticas híbridas. Isto significa que utiliza múltiplas heurísticas para obter a melhor solução possível. Utiliza uma variação da heurística de Graph Coloring para obter uma solução válida e as meta-heurísticas Simulated Annealing e Hill Climbing para melhorar a solução obtida. Os resultados finais são satisfatórios, pois em algumas instâncias os resultados são melhores do que alguns dos cinco finalistas do concurso ITC 2007.Abstract: In the last few years the automatic creation of timetables is being a well-studied subject. In many institutions, the elaboration of timetables is still manual, thus being a time-consuming and difficulty task for large instances. Another current problem in the manual approach is the existence of failures given the difficulty in the process verification, and so the quality of the produced timetable. If this timetable had been created by a computer, the timetable would be valid and timetables with better quality should be obtained, given the computer’s capacity to search the solution space. It is not easy to elaborate timetables, even for a machine. For example, scholar/university timetables need to follow certain type of constraints or rules for them to be considered valid. But since the solution space is so vast, it is highly unlikely to create an algorithm that completely enumerates the solutions in order to choose the best solution possible, considering the problem structure. The use of algorithms that perform implicit enumeration solutions (for example, an branch bound), is not feasible for large problems. Hence the use of heuristics which navigate through the solution space in a guided way, obtaining then a reasonable solution in acceptable time. One main objective of this project consists in creating an approach that follows the International Timetabling Competition (ITC) 2007 rules, focusing on creating examination timetables. This project will use a hybrid approach. This means it will use an approach that includes multiple heuristics in order to find the best possible solution. This approach uses a variant of the Graph Coloring heuristic to find an initial valid solution, and the metaheuristics Simulated Annealing and Hill Climbing to improve that solution. The final results are satisfactory, as in some instances the obtained results beat the results of some of the five finalists from ITC 2007

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

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing feasible examination timetable while meta-heuristics are applied for improving the solution quality. This paper presents the performances of graph heuristics and major trajectory metaheuristics or S-metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristic with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 dataset respectively. Experimental results indicate that, in terms of construction of solution of datasets, hybridizing of SD produces the best initial solutions. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark examination timetabling datasets

Linear Combinations of Heuristics for Examination Timetabling

Although they are simple techniques from the early days of timetabling research, graph colouring heuristics are still attracting significant research interest in the timetabling research community. These heuristics involve simple ordering strategies to first select and colour those vertices that are most likely to cause trouble if deferred until later. Most of this work used a single heuristic to measure the difficulty of a vertex. Relatively less attention has been paid to select an appropriate colour for the selected vertex. Some recent work has demonstrated the superiority of combining a number of different heuristics for vertex and colour selection. In this paper, we explore this direction and introduce a new strategy of using linear combinations of heuristics for weighted graphs which model the timetabling problems under consideration. The weights of the heuristic combinations define specific roles that each simple heuristic contributes to the process of ordering vertices. We include specific explanations for the design of our strategy and present the experimental results on a set of benchmark real world examination timetabling problem instances. New best results for several instances have been obtained using this method when compared with other constructive methods applied to this benchmark dataset

Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Many decision and optimization problems arising in bioinformatics field are time demanding, and several algorithms are designed to solve these problems or to improve their current best solution approach. Modeling and implementing a new heuristic algorithm may be time‐consuming but has strong motivations: on the one hand, even a small improvement of the new solution may be worth the long time spent on the construction of a new method; on the other hand, there are problems for which good‐enough solutions are acceptable which could be achieved at a much lower computational cost. In the first case, specially designed heuristics or metaheuristics are needed, while the latter hyper‐heuristics can be proposed. The paper will describe both approaches in different domain problems