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Warianty tytułu
Języki publikacji
Abstrakty
Engineering management is an extremely important aspect of construction engineering, and a better management approach can greatly enhance the production profits of enterprises. Traditional management optimization schemes cannot adapt to current technological needs due to their inability to effectively consider the impact of each factor. Therefore, a construction management optimization scheme combining improved particle algorithm and multi-objective optimization was proposed. The improved particle algorithm enhances its performance by introducing adaptive weight and multi-objective optimization ideas. These studies confirmed that the predicted direct cost savings for the project were around 1 million yuan. The total construction period of project was optimized to 380 days, saving 34 days. The optimization technology not only reduced construction costs, but also reflected the problems that could be improved during this construction process. This study contributes to achieving multi-objective balance in the construction management process, effectively improving project efficiency, reducing project costs and risks, and providing scientific support for construction decision-making.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
359--372
Opis fizyczny
Bibliogr. 18 poz., il., tab.
Twórcy
autor
- Management of State-owned Assets Department, Sichuan Water Conservancy Vocational College, Chongzhou, China
Bibliografia
- [1] X. Bai and C. Qian, “Factor validity and reliability performance analysis of human behavior in green architecture construction engineering”, Ain Shams Engineering Journal, vol. 12, no. 4, pp. 4291-4296, 2021, doi: 10.1016/j.asej.2021.04.009.
- [2] S.U. Jie, “Analysis of construction engineering quality accidents based on enterprise benefits”, Accounting and Corporate Management, vol. 3, no. 1, pp. 61-66, 2021, doi: 10.23977/acccm.2021.030109.
- [3] E. Nsugbe, “Toward a self-supervised architecture for semen quality prediction using environmental and lifestyle factors”, Artificial Intelligence and Applications, vol. 1, no. 1, pp. 35-42, 2023, doi: 10.47852/bonviewAIA2202303.
- [4] Y. Fang, B. Luo, T. Zhao, D. He, B. Jiang, and Q. Liu, “ST-SIGMA: Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting”, CAAI Transactions on Intelligence Technology, vol. 7, no. 4, pp. 744-757, 2022, doi: 10.1049/cit2.12145.
- [5] M. Zhang, “Research on construction management in construction project management”, Industrial Engineering and Innovation Management, vol. 6, no. 4, pp. 53-58, 2023, doi: 10.23977/ieim.2023.060408.
- [6] Y. Liu, A. Zub, and S. Zha, “Impact of attracting intellectual capital on the innovative development of construction engineering enterprises”, Revista Gestão & Tecnologia, vol. 22, no. 4, pp. 153-168, 2022.
- [7] M. Almashhadaniand and H.A. Almashhadani, “The impact of education on construction management: A comprehensive review”, International Journal of Business and Management Invention, vol. 12, no. 6, pp. 284-290, 2023, doi: 10.35629/8028-1206284290.
- [8] N. Kasim, S.A. Razali, and R. Kasim, “Reinforce technology IR 4.0 implementation for improving safety management in construction site”, International Journal of Sustainable Construction Engineering and Technology, vol. 12, no. 3, pp. 289-298, 2021, doi: 10.30880/ijscet.2021.12.03.028.
- [9] J.B.H. Yap, P.L. Goay, Y.B. Woon, and M. Skitmore, “Revisiting critical delay factors for construction: Analysing projects in Malaysia”, Alexandria Engineering Journal, vol. 60, no. 1, pp. 1717-1729, 2021, doi: 10.1016/j.aej.2020.11.021.
- [10] B. Zhong, H. Wu, L. Ding, H. Luo, Y. Luo, and X. Pan, “Hyperledger fabric-based consortium blockchain for construction quality information management”, Frontiers of Engineering Management, vol. 7, no. 4, pp. 512-527, 2020, doi: 10.1007/s42524-020-0128-y.
- [11] A.G. Gad, “Particle swarm optimization algorithm and its applications: A systematic review”, Archives of Computational Methods in Engineering, vol. 29, no. 5, pp. 2531-2561, 2022, doi: 10.1007/s11831-021-09694-4.
- [12] Y.V.R.N. Pawan, K.B. Prakash, et al., “Particle swarm optimization performance improvement using deep learning techniques”, Multimedia Tools and Applications, vol. 81, no. 19, pp. 27949-27968, 2022, doi: 10.1007/s11042-022-12966-1.
- [13] N. Zeng, Z. Wang, W. Liu, et al., “A dynamic neighborhood-based switching particle swarm optimization algorithm”, IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9290-9301, 2022, doi: 10.1109/TCYB.2020.3029748.
- [14] C. Pozna, R.E. Precup, E. Horváth, and E.M. Petriu, “Hybrid particle filter-particle swarm optimization algorithm and application to fuzzy controlled servo systems”, IEEE Transactions on Fuzzy Systems, vol. 30, no. 10, pp. 4286-4297, 2022, doi: 10.1109/TFUZZ.2022.3146986.
- [15] X. Luo, Y. Yuan, S. Chen, et al., “Position-transitional particle swarm optimization-incorporated latent factor analysis”, IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 8, pp. 3958-3970, 2022, doi: 10.1109/TKDE.2020.3033324.
- [16] M.G. Meharie, W.J. Mengesha, Z.A. Gariy, and R.N. Mutuku, “Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects”, Engineering, Construction and Architectural Management, vol. 29, no. 7, pp. 2836-2853, 2022, doi: 10.1108/ECAM-02-2020-0128.
- [17] M. Otair, O.T. Ibrahim, L. Abualigah, M. Altalhi, and P. Sumari, “An enhanced grey wolf optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks”, Wireless Networks, vol. 28, no. 2, pp. 721-744, 2022, doi: 10.1007/s11276-021-02866-x.
- [18] X. Wang, M. Cheng, J. Eaton, C.J. Hsieh, and S.F. Wu, “Fake node attacks on graph convolutional networks”, Journal of Computational and Cognitive Engineering, vol. 1, no. 4, pp. 165-173, 2022, doi: 10.47852/bonviewJCCE2202321.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-95f77b5d-8340-4772-9a6a-bfa0234aa1af