Designing difficult office space allocation problem instances with mathematical programming

Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and...

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Main Authors: Ulker, Ozgur, Landa-Silva, Dario
Format: Conference or Workshop Item
Published: Springer-Verlag 2011
Subjects:
Online Access:https://eprints.nottingham.ac.uk/32608/
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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) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and generate test instances for this difficult and important combinatorial optimisation problem. Systematic experimentation is then carried out to study the difficulty of the generated test instances when the parameters for adjusting space misuse (overuse and underuse) and constraint violations are subject to variation. The results show that the difficulty of solving OSA problem instances can be greatly affected by the value of these parameters.
first_indexed 2025-11-14T19:16:21Z
format Conference or Workshop Item
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institution University of Nottingham Malaysia Campus
institution_category Local University
last_indexed 2025-11-14T19:16:21Z
publishDate 2011
publisher Springer-Verlag
recordtype eprints
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spelling nottingham-326082020-05-04T20:23:20Z https://eprints.nottingham.ac.uk/32608/ Designing difficult office space allocation problem instances with mathematical programming Ulker, Ozgur Landa-Silva, Dario Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and generate test instances for this difficult and important combinatorial optimisation problem. Systematic experimentation is then carried out to study the difficulty of the generated test instances when the parameters for adjusting space misuse (overuse and underuse) and constraint violations are subject to variation. The results show that the difficulty of solving OSA problem instances can be greatly affected by the value of these parameters. Springer-Verlag 2011-05 Conference or Workshop Item PeerReviewed Ulker, Ozgur and Landa-Silva, Dario (2011) Designing difficult office space allocation problem instances with mathematical programming. In: Experimental Algorithms10th International Symposium, SEA 2011, May 5-7, 2011, Crete, Greece. Space planning problem formulation mathematical programming exact algorithms http://link.springer.com/chapter/10.1007%2F978-3-642-20662-7_24
spellingShingle Space planning
problem formulation
mathematical programming
exact algorithms
Ulker, Ozgur
Landa-Silva, Dario
Designing difficult office space allocation problem instances with mathematical programming
title Designing difficult office space allocation problem instances with mathematical programming
title_full Designing difficult office space allocation problem instances with mathematical programming
title_fullStr Designing difficult office space allocation problem instances with mathematical programming
title_full_unstemmed Designing difficult office space allocation problem instances with mathematical programming
title_short Designing difficult office space allocation problem instances with mathematical programming
title_sort designing difficult office space allocation problem instances with mathematical programming
topic Space planning
problem formulation
mathematical programming
exact algorithms
url https://eprints.nottingham.ac.uk/32608/
https://eprints.nottingham.ac.uk/32608/