Metaheuristic and Multiobjective Approaches for Space Allocation
This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research stu...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2003
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| Online Access: | https://eprints.nottingham.ac.uk/10147/ |
| _version_ | 1848791039297454080 |
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| author | Landa Silva, Jesus Dario |
| author_facet | Landa Silva, Jesus Dario |
| author_sort | Landa Silva, Jesus Dario |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation
problem in academic institutions.
This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics. |
| first_indexed | 2025-11-14T18:22:10Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-10147 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:22:10Z |
| publishDate | 2003 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-101472025-02-28T11:07:17Z https://eprints.nottingham.ac.uk/10147/ Metaheuristic and Multiobjective Approaches for Space Allocation Landa Silva, Jesus Dario This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics. 2003 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/10147/1/JDLSPHDTHESIS.PDF Landa Silva, Jesus Dario (2003) Metaheuristic and Multiobjective Approaches for Space Allocation. PhD thesis, University of Nottingham. heuristics meta-heuristics space allocation combinatorial optimisation local search neighbourhood search multi-objective multi-criteria Pareto optimisation hybrid heuristics hybrid meta-heuristics |
| spellingShingle | heuristics meta-heuristics space allocation combinatorial optimisation local search neighbourhood search multi-objective multi-criteria Pareto optimisation hybrid heuristics hybrid meta-heuristics Landa Silva, Jesus Dario Metaheuristic and Multiobjective Approaches for Space Allocation |
| title | Metaheuristic and Multiobjective Approaches for Space Allocation |
| title_full | Metaheuristic and Multiobjective Approaches for Space Allocation |
| title_fullStr | Metaheuristic and Multiobjective Approaches for Space Allocation |
| title_full_unstemmed | Metaheuristic and Multiobjective Approaches for Space Allocation |
| title_short | Metaheuristic and Multiobjective Approaches for Space Allocation |
| title_sort | metaheuristic and multiobjective approaches for space allocation |
| topic | heuristics meta-heuristics space allocation combinatorial optimisation local search neighbourhood search multi-objective multi-criteria Pareto optimisation hybrid heuristics hybrid meta-heuristics |
| url | https://eprints.nottingham.ac.uk/10147/ |