id |
ijac202119312 |
authors |
Chen, Chen; Chacón Vega, Ricardo Jose; Kong, Tiong Lee |
year |
2021 |
title |
Using genetic algorithm to automate the generation of an open-plan office layout |
source |
International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 449–465 |
summary |
Today, the concept of open plan is more and more widely accepted that many companies have switched to open-plan offices. Their design is an issue in the scope of space layout planning. Although there are many professional architectural layout design software in the market, in the real life, office designers seldom use these tools because their license fees are usually expensive and using them to solve an open-plan office design is like using an overly powerful and expensive tool to fix a minor problem. Therefore, manual drafting through a trial and error process is most often used. This article attempts to propose a lightweight tool to automate open-plan office layout generation using a nested genetic algorithm optimization with two layers, where the inner layer algorithm is embedded in the outer one. The result is enhanced by a local search. The main objective is to maximize space utilization by maximizing the size of the open workspace. This approach is different from its precedents, in that the location search is conducted on a grid map rather than several pre-selected candidate locations. Consequently, the generated layout design presents a less rigid workstation arrangement, inviting a casual and unrestrictive work environment. The real potential of the approach is reflected in the productivity of test fits. Automating and simplifying the generation of layouts for test fits can tremendously decrease the amount of time and resources required to generate them. The experimental case study shows that the developed approach is powerful and effective, making it a totally automated process. |
keywords |
Automated process, office design, genetic algorithm, open-plan office, space layout planning |
series |
journal |
email |
|
references |
Content-type: text/plain
|
Anderson C, Beiley C, Heumann A, et al. (2018)
Augmented space planning: using procedural generation to automate desk layouts
, Int J Arch Comput; 16(2): 164–177
|
|
|
|
Araki Y and Osana Y. (2012)
Office layout support system for polygonal space using interactive genetic algorithm
, Proceedings of the 2012 IEEE international conference on systems, man, and cybernetics (SMC), Seoul, Korea, 14–17 October 2012
|
|
|
|
Arvin SA and House DH. (1999)
Making designs come alive: using physically based modeling techniques in space layout planning
, Augenbroe G and Eastman C (eds) Computers in building. Boston, MA: Springer, pp. 245–262
|
|
|
|
Arvin SA and House DH. (2002)
Modeling architectural design objectives in physically based space planning
, Automat Const; 11(2): 213–225
|
|
|
|
Asefi M, Haghparast F and Sharifi E. (2019)
Comparative study of the factors affecting the generativity of office spaces
, Front Arch Res; 8(1): 106–119
|
|
|
|
Brooks A. (1998)
Ergonomic approaches to office layout and space planning
, Facilities; 16(3/4): 73–78
|
|
|
|
Buffa ES, Armour GC and Vollmann TE. (1964)
Allocating facilities with CRAFT
, Havard Bus Rev; 42(2): 136–158
|
|
|
|
Calixto V and Celani G. (2015)
A literature review for space planning optimization using an evolutionary algorithm approach: 1992-2014
, XIX Congresso Da Sociedade Ibero-americana De Gráfica Digital 2015; 2(3): 662–671
|
|
|
|
Dino IG and Ûçoluk G. (2017)
Multiobjective design optimization of building space layout, energy, and daylighting performance
, J Comput Civil Eng; 31(5): 04017025
|
|
|
|
Elezkurtaj T and Franck G. (2002)
Algorithmic support of creative architectural design
, Weimar, Germany: Umbau 19, pp. 129–137
|
|
|
|
Gero JS and Kazakov VA. (1997)
Learning and re-using information in space layout planning problems using genetic engineering
, Artif Intell Eng; 11(3): 329–334
|
|
|
|
Gilbert JP. (2004)
Construction office design with systematic layout planning
, Proceedings of the 2nd world confer-ence on POM 15th annual POM conference, Cancun, Mexico, 30 April–3 May 2004
|
|
|
|
Grefenstette JJ. (1986)
Optimization of control parameters for genetic algorithms
, IEEE Trans Syst Man Cybern; 16(1): 122–128
|
|
|
|
Haapakangas A, Hongito V, Varjo J, et al. (2018)
Surface roughness prediction model for CNC machining of polypropylene
, J Environ Psychol; 56: 63–75
|
|
|
|
Hillier B, Hanson J and Graham H. (1987)
Ideas are in things: an application of the space syntax method to discovering house genotypes
, Env Plann B Plann Design; 14(4): 363–385
|
|
|
|
Jagielski R and Gero JS. (1997)
A genetic programming approach to the space layout planning problem
, CAAD Futures; 1997: 875–884
|
|
|
|
Jo JH and Gero JS. (1995)
A genetic search approach to space layout planning
, Arch Sci Rev; 38(1): 37–46
|
|
|
|
Li S-P, Frazer JH and Tang M-X. (2000)
A constraint based generative system for floor layouts
, Proceedings of the Fifth Conference on Computer Aided Architectural Design Research in Asia (CAADRIA 2000), Singapore, 18–19 May 2000, pp. 441–450. Singapore: CuminCAD
|
|
|
|
Liggett RS. (2000)
Automated facilities layout: past, present and future
, Automat Constr; 9(2): 197–215
|
|
|
|
Lobos D and Donath D. (2010)
The problem of space layout in architecture: a survey and reflections
, Arquitetura Revista; 6(2): 136–161
|
|
|
|
last changed |
2024/04/17 14:29 |
|