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초고층 건축공사의 생산성 향상을 위한 Jump Point Search 기반 작업층 자재배치 최적화 모델

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Author(s)
권재범 김태훈 강경인
Issued Date
2016
Keyword
Tall building construction Floor-level layout planning Global search Jump point search
Abstract
In tall building construction, time for transporting materials on a floor is one of the factors which largely affect work productivity. Thus, the necessity of optimizing the floor-level layout plan has increased, and path finding algorithms to find an optimal path by considering various obstacles have been required to provide a more reliable floor-level layout planning. Jump point search (JPS) algorithm has high accuracy of optimal paths through global search by minimizing the computational problem, compared to heuristic search algorithms that find paths at the moderate level to reduce the computational time and effort. Therefore, this study proposes a floor-level layout planning model for tall building construction based on optimal path finding by the JPS algorithm. The applicability of the JPS algorithm to the construction field was verified by increasing work productivity of the floor-level layout planning of the heuristic search by approximately 14%. The proposed path finding algorithm will contribute to preparing a more reliable construction layout planning model.
Alternative Title
Floor-level Layout Planning Optimization Model using Jump Point Search for Improving Productivity of Tall Building Construction
Alternative Author(s)
Tall building construction Floor-level layout planning Global search Jump point search
Publisher
조선대학교 공학기술연구원
Citation
권재범. (2016). 초고층 건축공사의 생산성 향상을 위한 Jump Point Search 기반 작업층 자재배치 최적화 모델, 공학기술논문지 | Vol.9, No.2 p.143 ~ p.150
Type
Laboratory article
ISSN
2005-3142
URI
https://oak.chosun.ac.kr/handle/2020.oak/17657
http://www.chosun.ac.kr/user/indexSub.do?codyMenuSeq=23376167&siteId=riet&dum=dum&boardId=168878&page=17&command=view&boardSeq=199590&chkBoxSeq=&categoryId=&categoryDepth=&search=&column=null&searchDate1=&searchDate2=&selColumn=&myList=
Appears in Collections:
2016 > Vol.9, No.2
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