Evolutionary local search for solving the office space allocation problem
Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. Th...
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| Format: | Conference or Workshop Item |
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IEEE press
2012
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| Online Access: | https://eprints.nottingham.ac.uk/32128/ |
| _version_ | 1848794340033298432 |
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| author | Ulker, Ozgur Landa-Silva, Dario |
| author_facet | Ulker, Ozgur Landa-Silva, Dario |
| author_sort | Ulker, Ozgur |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include standard crossover and mutation operators and a relatively small population of individuals. The offspring produced by the evolutionary operators are subjected to a short but intense local search process. A very fast cost calculation method tailored for searching a large section of the search space is implemented. Extensive experimentation is carried out related to several parameters of the algorithm: the mutation rate, the population size, the length of the local search procedure after each mutation, hence the balance between the evolutionary and the local search stages, and the level of greediness of the local search process. The final results on 72 different data instances show that this hybrid evolutionary algorithm is very competitive with an integer programming model. |
| first_indexed | 2025-11-14T19:14:38Z |
| format | Conference or Workshop Item |
| id | nottingham-32128 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:14:38Z |
| publishDate | 2012 |
| publisher | IEEE press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-321282020-05-04T20:21:39Z https://eprints.nottingham.ac.uk/32128/ Evolutionary local search for solving the office space allocation problem Ulker, Ozgur Landa-Silva, Dario Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include standard crossover and mutation operators and a relatively small population of individuals. The offspring produced by the evolutionary operators are subjected to a short but intense local search process. A very fast cost calculation method tailored for searching a large section of the search space is implemented. Extensive experimentation is carried out related to several parameters of the algorithm: the mutation rate, the population size, the length of the local search procedure after each mutation, hence the balance between the evolutionary and the local search stages, and the level of greediness of the local search process. The final results on 72 different data instances show that this hybrid evolutionary algorithm is very competitive with an integer programming model. IEEE press 2012-06 Conference or Workshop Item PeerReviewed Ulker, Ozgur and Landa-Silva, Dario (2012) Evolutionary local search for solving the office space allocation problem. In: 2012 IEEE Congress on Evolutionary Computation (CEC 2012), 10-15 June 2012, Brisbane, Australia. Space allocation Hybrid evolutionary algorithms Hybrid metaheuristics Local search http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6253009 |
| spellingShingle | Space allocation Hybrid evolutionary algorithms Hybrid metaheuristics Local search Ulker, Ozgur Landa-Silva, Dario Evolutionary local search for solving the office space allocation problem |
| title | Evolutionary local search for solving the office space allocation problem |
| title_full | Evolutionary local search for solving the office space allocation problem |
| title_fullStr | Evolutionary local search for solving the office space allocation problem |
| title_full_unstemmed | Evolutionary local search for solving the office space allocation problem |
| title_short | Evolutionary local search for solving the office space allocation problem |
| title_sort | evolutionary local search for solving the office space allocation problem |
| topic | Space allocation Hybrid evolutionary algorithms Hybrid metaheuristics Local search |
| url | https://eprints.nottingham.ac.uk/32128/ https://eprints.nottingham.ac.uk/32128/ |