Journal of Engineering, Project, and Production Management, 2026, 16(1), 2025-152
Integrating Particle Swarm, Genetic Algorithms, and Differential Evolution for Prefabricated Formwork Optimization
1 Associate
Professor, School of Civil Engineering, Zhengzhou College of Finance and
Economics, Zhengzhou, 450000, China, E-mail: luo_luofang@outlook.com
(corresponding author).
2 Senior Engineer, Henan Zhengtong
Construction and Installation Engineering Co., Ltd., Zhengzhou, 450000,
China
Project Management
Received August 8, 2025; revised September 23, 2025; accepted September 24, 2025
Available online December 24, 2025
Abstract: The increasing adoption of prefabricated construction has made formwork a key factor that directly affects project cost, construction time, and quality. Traditional optimization methods, however, often struggle to address the variability of prefabricated components and the complexity of production environments. To overcome these limitations, this study develops an optimization model for prefabricated formwork by combining particle swarm optimization, genetic algorithm, and differential evolution within a three-swarm cooperative framework. The model considers objectives related to cost, construction period, and quality, and applies the analytic hierarchy process to support multi-criteria decision-making. Experimental validation with real production data shows that the model reduced the production time of a single component from 34.93 hours to 29.89 hours. For a complete order of 17 components, total labor hours decreased by 27.32% and costs by 23.56%. These results confirm that the proposed approach not only improves optimization performance but also delivers practical value for scheduling and resource allocation in prefabricated construction projects.
Keywords: AHP, architecture, formwork, optimization, prefabrication.
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Citation: Luo, F. and Fang, S. (2026). Integrating Particle Swarm, Genetic Algorithms, and Differential Evolution for Prefabricated Formwork Optimization. Journal of Engineering, Project, and Production Management, 16(1), 2025-152.
DOI:
10.32738/JEPPM-2025-152
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