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Title: Investigations of constructive approaches for examination timetable and 3d-strip packing
Author: Pham, Nam
ISNI:       0000 0004 2747 1136
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2011
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This thesis aims at designing search methods that can produce competitive solutions and to some extent, are of higher generality than the state of the art search/optimisation systems. Attaining this aim would underpin the next generation of automated systems with the goal being to require less specialist knowledge in solving complex optimisation problems. The main challenge in this project is to develop systems of higher generality which can intelligently select, evolve or combine search methods (heuristics) to operate upon a wider range of problems and problem instances. This research follows that direction and contributes to the goal of exploring the generality boundary of this new trend of automating the design of search systems. The mam contributions in this thesis are divided into two parts. The first part investigates different approaches to combine constructive heuristics which are capable of producing good solutions for timetabling problems. Chapter 3 presents a weighted graph model for the exam timetabling problem where vertices and edges store several extra-attributes to improve the process of finding difficult exams and selecting timeslots for them. Chapter 4 investigates sequential and linear combinations of vertex-selection heuristics that have emerged from the weighted graph model. The results on the Toronto exam timetabling benchmark are compared with those obtained from other approaches in the literature. The second part of the research focuses on raising the level of generality for search methodologies by investigating the use of estimation of distribution algorithms into a proposed hyper-heuristic for several optimisation problems. Chapter 5 presents an extended framework for the best-fit strategy for the three-dimensional strip packing 2 problem. Chapter 6 proposes a hyper-heuristic based on estimation of distribution algorithms. Then we investigate the level of generality of the hyper-heuristic by applying it to different problem domains (graph colouring, exam timetabling, and 3D strip packing). Experimental evidence indicates that the hyper-heuristic can operate on a wide range of problems to produce some competitive results. We also demonstrate the capability of identifying the effectiveness of the low-level heuristics. This may facilitate the development of efficient automated search systems in future research. Finally, Chapter 7 evaluates all the results obtained and summarises promising future research directions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available