A cutting-edge approach to prefabricated construction site layouts using multi-objective artificial hummingbird algorithm solution

Construction site layout planning (CSLP) for prefabricated construction projects differs significantly from traditional construction projects, particularly in the use of tower cranes and the arrangement of locations for precasting components. Previous research has introduced various models to optimi...

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Vydáno v:International journal of construction management Ročník 25; číslo 16; s. 2063 - 2081
Hlavní autoři: Pham, Vu Hong Son, Pham, Duy Hieu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis 10.12.2025
Témata:
Construction site layout planning (CSLP)
hoisting time
prefabricated construction
safety risk
transportation cost
ISSN:1562-3599, 2331-2327
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Shrnutí:Construction site layout planning (CSLP) for prefabricated construction projects differs significantly from traditional construction projects, particularly in the use of tower cranes and the arrangement of locations for precasting components. Previous research has introduced various models to optimize CSLP problems. However, these studies often consider only one or two objective functions related to safety, transportation costs, environmental impacts, or crane performance. Additionally, many models employ long-standing metaheuristic algorithms, resulting in suboptimal solutions. This article proposes the application of the multi-objective artificial hummingbird algorithm (MOAHA) to address the CSLP by integrating objectives related to safety risks, transportation costs, and efficient tower crane operation. A case study derived from previous research, as well as a real construction site, is used to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms existing models in terms of objective function values and the number of optimal solutions found. Moreover, in real projects, the solutions from the model outperform the actual plan by 38-50% when comparing objective function values. This demonstrates that the model has the potential to effectively address more complex CSLP challenges in the future.
ISSN:1562-3599
2331-2327
DOI:10.1080/15623599.2025.2502697