Computer algorithms: introduction to design and analysis (2nd ed.)
Computer algorithms: introduction to design and analysis (2nd ed.)
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
Automating Space Allocation in Higher Education
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Scheduling, Timetabling and Rostering - A Special Relationship?
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Memetic Algorithm for University Exam Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Space Allocation: An Analysis of Higher Education Requirements
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Space-planning by ant colony optimisation
International Journal of Computer Applications in Technology
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
Designing difficult office space allocation problem instances with mathematical programming
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
The office-space-allocation problem in strongly hierarchized organizations
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
A multi-level genetic algorithm for a multi-stage space allocation problem
Mathematical and Computer Modelling: An International Journal
Office-space-allocation problem using harmony search algorithm
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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The space allocation problem within UK universities is highly constrained, has multiple objectives, varies greatly among different institutions, requires frequent modifications and has a direct impact on the functionality of the university. As in every optimisation problem, the application of different advanced search methodologies such as local search, metaheuristics and evolutionary algorithms provide a promising way forward. In this paper we discuss three well known methods applied to solve the space allocation problem: hill climbing, simulated annealing and a genetic algorithm. Results and a comprehensive comparison between all three techniques are presented using real test data. Although these algorithms have been extensively studied in different problems, our major objective is to investigate the application of these techniques to the variants of the space allocation problem, comparing advantages and disadvantages to achieve a better understanding of the problem and propose future hybridisation of these and additional methods.