Tower Crane Layout Planning: Multi-Optimal Solutions Algorithm

Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum, leaving room for doubt. This paper introduces the competitor algorithm, a novel multi-optimal solution approach inspired by the competitive learning paradigm within classroom settings. This algorithm is designed to provide users with a diverse set of competitive solutions, while avoiding falling into local optima. This strategic diversification ensures that users are equipped with a comprehensive range of options, empowering them to make confident, informed decisions. Furthermore, we have streamlined the positioning range for tower cranes, transitioning from a two-dimensional plane to a one-dimensional segmented line, thus eliminating the need to explore extensive, non-competitive regions. The competitor algorithm’s performance was validated through practical application, showcasing both its stability and optimization prowess, thereby confirming its reliable utility in real-world scenarios.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2024-11-26
DOI
https://doi.org/10.3390/buildings14123760
Citations
3
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
1.05
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tower Crane Layout Planning: Multi-Optimal Solutions Algorithm

Huazhou Chen, Xiaokang Huang, Shuai Li, Pengfei Zhang et al.
3 citations
Buildings
Advanced Manufacturing and Logistics Optimization
1.05
article

Tower Crane Layout Planning: Multi-Optimal Solutions Algorithm

Huazhou Chen, Xiaokang Huang, Shuai Li, Pengfei Zhang, Rui Wang, Heng Wang, Zhen Yang
article en
3 citations

Abstract

Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum, leaving room for doubt. This paper introduces the competitor algorithm, a novel multi-optimal solution approach inspired by the competitive learning paradigm within classroom settings. This algorithm is designed to provide users with a diverse set of competitive solutions, while avoiding falling into local optima. This strategic diversification ensures that users are equipped with a comprehensive range of options, empowering them to make confident, informed decisions. Furthermore, we have streamlined the positioning range for tower cranes, transitioning from a two-dimensional plane to a one-dimensional segmented line, thus eliminating the need to explore extensive, non-competitive regions. The competitor algorithm’s performance was validated through practical application, showcasing both its stability and optimization prowess, thereby confirming its reliable utility in real-world scenarios.

BuildingsVol. 14(12)
China Aerospace Science and Industry Corporation (China) (CN), Taiyuan University of Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Advanced Manufacturing and Logistics Optimization
1.05
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Tower Crane Layout Planning: Multi-Optimal Solutions Algorithm — Huazhou Chen, Xiaokang Huang, et al. · Buildings (2024) | TGRS Research Map | TGRS