A comparative study for layout planning of temporary construction facilities: Optimization by using ant colony algorithms

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dc.contributor.authorCalis G.
dc.contributor.authorYuksel O.
dc.date.accessioned2019-10-26T21:12:32Z
dc.date.available2019-10-26T21:12:32Z
dc.date.issued2019
dc.departmentEge Üniversitesien_US
dc.description.abstractConstruction site layout is among the most challenging tasks of the construction planning process that consists of identifying temporary facilities to support construction activities, defining their shapes, sizes and allocating them into available spaces within the site boundaries. A good site layout can minimize the travel time between facilities, improve site safety, increase productivity, and, thus, decrease construction cost and time. Although site layout has such a major role in planning, it has received relatively little attention due to the complex nature of the problem, which is formulated as a combinatorial optimization problem. In this study, the Ant Colony Optimization (ACO) algorithm is proposed for site layout problems. ACO mimics the behavior of real ants for finding solutions and is proved to introduce feasible solutions for combinatorial optimization problems. The effectiveness of proposed algorithm is illustrated by using a literature problem. It was observed that the developed ACO model performed a better layout alternative. © 2018 Esprit. All rights reserved.en_US
dc.identifier.isbn9781907284601
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11454/15717
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherNottinghamen_US
dc.relation.ispartofEG-ICE 2010 - 17th International Workshop on Intelligent Computing in Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnt colony optimizationen_US
dc.subjectSite layout planningen_US
dc.titleA comparative study for layout planning of temporary construction facilities: Optimization by using ant colony algorithmsen_US
dc.typeConference Objecten_US

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