A Genetic Algorithm Based Layout Optimization Method for Arbitrarily Shaped Construction Sites and Facilities
Date Issued
2004
Date
2004
Author(s)
Sun, Kuo-Chih
DOI
zh-TW
Abstract
This thesis presents a study of current practices on solving the facility layout problem, incorporating a genetic algorithms (GA) procedure. This research presents a non-grid-based plane model that is more readily applicable to construction sites. This new facility layout model allows user to determine several parameters, such as irregular user-defined polygon of arbitrarily shapes, reference coordinates, and place orientation, which is more realistic in the construction engineering. Furthermore, previous research uses GA to solve the facility layout problem focus on grid-based site representation. They encode the chromosome by the position index of two-dimensional grid, namely discrete encoding. By contrast, this research applied real number coded genetic algorithms to define the application model.
Based on the proposed model, a comprehensive system for a GA-based Facility Layout Planning system for construction site (GAFLP) is developed. GAFLP uses C# programming language, MS .Net Framework, and Evolver API to represent the site and the facilities, and automate the solution evolved. This set of algorithms employ the specific coding of facility shapes provides a more efficient tool to draw the shapes on man-machine interface. This proposed model also provides alternative settings for solve different construction layout problems, such as closeness relationship values, types of facilities, etc. The results show that the present model can be suitable for loosely arrangement in large base as construction site and it can quickly generates an appropriate planning of construction site layout.
Subjects
實數型遺傳演算法
營建工地
任意外形
設施佈置
遺傳演算法
非格位為基平面
construction site
genetic algorithms
real number coded genetic algorithms
non-grid-based plane
arbitrarily shaped
facility layout
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-93-R91546008-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):0ecb1520a997612285bbbfb97947eaf1