314 research outputs found
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
Grammatical evolution hyper-heuristic for combinatorial optimization problems
Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains
Examination timetabling at the University of Cape Town: a tabu search approach to automation
With the rise of schedules and scheduling problems, solutions proposed in literature have expanded yet the disconnect between research and reality remains. The University of Cape Town's (UCT) Examinations Office currently produces their schedules manually with software relegated to error-checking status. While they have requested automation, this study is the first attempt to integrate optimisation techniques into the examination timetabling process. Tabu search and Nelder-Mead methodologies were tested on the UCT November 2014 examination timetabling data with tabu search proving to be more effective, capable of producing feasible solutions from randomised initial solutions. To make this research more accessible, a user-friendly app was developed which showcased the optimisation techniques in a more digestible format. The app includes data cleaning specific to UCT's data management system and was presented to the UCT Examinations Office where they expressed support for further development: in its current form, the app would be used as a secondary tool after an initial solution has been manually obtained
Structure based partial solution search for the examination timetabling problem.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The aim of this work is to present a new approach, namely, Structure Based Partial Solution
Search (SBPSS) to solve the Examination Timetabling Problem. The success of the
Developmental Approach in this problem domain suggested that the strategy of searching the
spaces of partial timetables whilst constructing them is promising and worth pursuing. This
work adopts a similar strategy. Multiple timetables are incrementally constructed at the same
time. The quality of the partial timetables is improved upon by searching their partial solution
spaces at every iteration during construction. Another key finding from the literature survey
revealed that although timetables may exhibit the same behaviour in terms of their objective
values, their structures or exam schedules may be different. The challenge with this finding is
to decide on which regions to pursue because some regions may not be worth investigating due
to the difficulty in searching them. These problematic areas may have solutions that are not
amenable to change which makes it difficult to improve them. Another reason is that the
neighbourhoods of solutions in these areas may be less connected than others which may restrict
the ability of the search to move to a better solution in that neighbourhood. By moving to these
problematic areas of the search space the search may stagnate and waste expensive
computational resources. One way to overcome this challenge is to use both structure and
behaviour in the search and not only behaviour alone to guide the search. A search that is guided
by structure is able to find new regions by considering the structural components of the
candidate solutions which indicate which part of the search space the same candidates occupy.
Another benefit to making use of a structure-based search is that it has no objective value bias
because it is not guided by only the objective value. This statement is consistent with the
literature survey where it is suggested that in order to achieve good performance the search
should not be guided by only the objective value. The proposed method has been tested on three popular benchmark sets for examination timetabling, namely, the Carter benchmark set; the
benchmark set from the International Timetabling competition in 2007 and the Yeditepe
benchmark set. The SBPSS found the best solutions for two of the Carter problem instances.
The SBPSS found the best solutions for four of the competition problem instances. Lastly, the
SBPSS improved on the best results for all the Yeditepe problem instances
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 nĂŁo Ă© uma tarefa fácil, mesmo para uma máquina. Por exemplo, horários escolares necessitam de seguir certas regras para que seja possĂvel a criação de um horário válido. Mas como o espaço de estados (soluções) válidas Ă© tĂŁo vasto, Ă© impraticável criar um algoritmo que faça a enumeração completa de soluções a fim de escolher a melhor solução possĂvel. Por outro lado, a utilização de algoritmos que realizam a enumeração implĂcita de soluções (por exemplo, branch and bound), nĂŁo Ă© viável para problemas de grande dimensĂŁo. A utilização de heurĂsticas que percorrem de uma forma guiada o espaço de estados, conseguindo assim uma solução razoável em tempo Ăştil, 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 heurĂsticas hĂbridas. Isto significa que utiliza mĂşltiplas heurĂsticas para obter a melhor solução possĂvel. Utiliza uma variação da heurĂstica de Graph Coloring para obter uma solução válida e as meta-heurĂsticas 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
A Classification of Hyper-heuristic Approaches
The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research
Iterated local search using an add and delete hyper- heuristic for university course timetabling
Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach
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