Office Space Allocation (OSA) is the task of efficient usage of spatial resources of an organisation. A common goal in a typical OSA problem is to minimise the wastage of space either by limiting the overuse or underuse of the facilities. The problem also contains a myriad of hard and soft constraints based on the preferences of respective organisations. In this thesis, the OSA variant usually encountered in academic institutions is investigated. Previous research in this area is rather sparse. This thesis provides a definition, extension, and literature review for the problem as well as a new parametrised data instance generator. In this thesis, two main algorithmic approaches for tackling the OSA are proposed: The first one is integer linear programming. Based on the definition of several constraints and some additional variables, two different mathematical models are proposed. These two models are not strictly alternatives to each other. While one of them provides more performance for the types of instances it is applicable, it lacks generality. The other approach provides less performance; however, it is easier to apply this model to different OSA problems. The second algorithmic approach is based on metaheuristics. A three step process in heuristic development is followed. In the first step, general local search techniques (descent methods, threshold acceptance, simulated annealing, great deluge) traverse within the neighbourhood via random relocation and swap moves. The second step of heuristic development aims to investigate large sections of the whole neighbourhood greedily via very fast cost calculation, cost update, and search for best move procedures within an evolutionary local search framework. The final step involves refinements and hybridisation of best performing (in terms of solution quality) mathematical programming and meta-heuristic techniques developed in prior steps. This thesis aims to be one of the pioneering works in the research area of OSA. The major contributions are: the analysis of the problem, a new parametrised data instance generator, mathematical programming models, and meta-heuristic approaches in order to extend the state-of-the art in this area.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:594670 |
| Date | January 2013 |
| Creators | Ulker, Ozgur |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/13604/ |
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