Collaborative optimisation in building design with a Pareto-based genetic algorithm

Large-scale building design is a constantly evolving discipline. Design managers are consistently trying to identify means for producing a better product in a shorter period of time. Hence there is a need for design assistant tools that can help designers understand the big picture. It is becoming h...

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Bibliographic Details
Main Author:	Fan Yang
Format:	Default Thesis
Published:	2008
Subjects:	Other built environment and design not elsewhere classified untagged Built Environment and Design not elsewhere classified
Online Access:	https://hdl.handle.net/2134/35017
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id	rr-article-9456584
record_format	Figshare
spelling	rr-article-94565842008-01-01T00:00:00Z Collaborative optimisation in building design with a Pareto-based genetic algorithm Fan Yang (1413) Other built environment and design not elsewhere classified untagged Built Environment and Design not elsewhere classified Large-scale building design is a constantly evolving discipline. Design managers are consistently trying to identify means for producing a better product in a shorter period of time. Hence there is a need for design assistant tools that can help designers understand the big picture. It is becoming hard to improve the system performance of building design based merely on advances in individual disciplines. In other words, improvements in individual disciplines alone are not sufficient to affect the improvements in the whole system. To achieve higher quality, system-orientated, holistic, multidisciplinary approaches to building design are needed (NSF, 1996). For this reason, this research investigates the applicability of multidisciplinary disciplinary optimisation (MDO) methodology in building design. The MDO methods divide a single system into a group of smaller sub-systems and effectively manage interactions between sub-systems. In the context of building design, the single system refers to the whole building design, and sub-system could be each disciplinary design. Such approaches could reduce the time and cost associated with the multidisciplinary design cycle. This thesis describes the work of developing collaborative optimisation framework with a Pareto based genetic algorithm (COPGA). [Continues.] 2008-01-01T00:00:00Z Text Thesis 2134/35017 https://figshare.com/articles/thesis/Collaborative_optimisation_in_building_design_with_a_Pareto-based_genetic_algorithm/9456584 CC BY-NC-ND 4.0
institution	Loughborough University
collection	Figshare
topic	Other built environment and design not elsewhere classified untagged Built Environment and Design not elsewhere classified
spellingShingle	Other built environment and design not elsewhere classified untagged Built Environment and Design not elsewhere classified Fan Yang Collaborative optimisation in building design with a Pareto-based genetic algorithm
description	Large-scale building design is a constantly evolving discipline. Design managers are consistently trying to identify means for producing a better product in a shorter period of time. Hence there is a need for design assistant tools that can help designers understand the big picture. It is becoming hard to improve the system performance of building design based merely on advances in individual disciplines. In other words, improvements in individual disciplines alone are not sufficient to affect the improvements in the whole system. To achieve higher quality, system-orientated, holistic, multidisciplinary approaches to building design are needed (NSF, 1996). For this reason, this research investigates the applicability of multidisciplinary disciplinary optimisation (MDO) methodology in building design. The MDO methods divide a single system into a group of smaller sub-systems and effectively manage interactions between sub-systems. In the context of building design, the single system refers to the whole building design, and sub-system could be each disciplinary design. Such approaches could reduce the time and cost associated with the multidisciplinary design cycle. This thesis describes the work of developing collaborative optimisation framework with a Pareto based genetic algorithm (COPGA). [Continues.]
format	Default Thesis
author	Fan Yang
author_facet	Fan Yang
author_sort	Fan Yang (1413)
title	Collaborative optimisation in building design with a Pareto-based genetic algorithm
title_short	Collaborative optimisation in building design with a Pareto-based genetic algorithm
title_full	Collaborative optimisation in building design with a Pareto-based genetic algorithm
title_fullStr	Collaborative optimisation in building design with a Pareto-based genetic algorithm
title_full_unstemmed	Collaborative optimisation in building design with a Pareto-based genetic algorithm
title_sort	collaborative optimisation in building design with a pareto-based genetic algorithm
publishDate	2008
url	https://hdl.handle.net/2134/35017
_version_	1798277775356854272

Collaborative optimisation in building design with a Pareto-based genetic algorithm

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