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Design of construction project management technology based on project schedule cost model and swarm intelligence algorithm

Hua Tian

International Journal of Applied Decision Sciences2025https://doi.org/10.1504/ijads.2026.10069112article
AJG 1
Weight
0.50

What the paper says

To solve the problems of cost estimation and schedule control in project management, a project management technology design based on project schedule cost model and improved particle swarm algorithm is proposed. This study uses quantitative field surveys and structural equation modelling analysis methods, combined with empirical analysis of 20 repeated experiments, to verify the effectiveness of the proposed method. The results showed that the improved particle swarm algorithm improved accuracy by 30.48% compared to the standard particle swarm, shortened project cycles by 15.2%, and reduced resource waste rate by 31.4%. In addition, the risk response time has been accelerated by 50.0%, the engineering quality qualification rate has increased by 7.4%, the progress tracking error reduced to 60.0%, and the decision-making time shortened to 42.7%. The research model not only enriches the optimisation theory of project management, but also has practical value in improving the intelligence level of project management and enhancing cost control capabilities.

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https://doi.org/https://doi.org/10.1504/ijads.2026.10069112

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@article{hua2025,
  title        = {{Design of construction project management technology based on project schedule cost model and swarm intelligence algorithm}},
  author       = {Hua Tian},
  journal      = {International Journal of Applied Decision Sciences},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijads.2026.10069112},
}

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Design of construction project management technology based on project schedule cost model and swarm intelligence algorithm

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.