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Balanced Optimization System of Construction Project Management Based on Improved Particle Swarm Algorithm

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International Conference on Cognitive based Information Processing and Applications (CIPA 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 84))

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Abstract

Since the end of the 20th century, the construction industry has developed rapidly. Traditional construction project management involves three control goals: schedule, cost, and quality. With the continuous deepening of the concept of sustainability, the environmental protection goals are the same as the traditional three control goals. It is of great significance to put the multi-objective balanced optimization in the same important position so that the main control objectives of the project can be achieved better. In recent years, swarm intelligence algorithms have been widely introduced into the equilibrium optimization problem of engineering project management, and relatively satisfactory results have been achieved. Particle swarm algorithm is a non-numerical optimization calculation method based on the foraging process of birds and swarm intelligence. Since its proposal, it has received a lot of attention from scholars, and its application research has been solved from purely functional numerical optimization problems. It has penetrated into other fields. In view of this, by improving the level of project management and applying multi-objective optimization technology, my country’s economic development speed can be effectively improved. At present, our country has applied multi-objective optimization to the management of substation engineering projects, and conducted in-depth research on this as the development direction. The average value of Transaction per second is 65.21, which shows that the number of transactions processed by the system per second can well simulate real information query use cases. The average transaction response time is 84.21 s, which is too long. But if the peak response time of 170 s is not considered, the system transaction processing time will eventually stabilize between 30 s, which meets the performance requirements.

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References

  1. Cajzek, R., Klansek, U.: Cost optimization of project schedules under constrained resources and alternative production processes by mixed-integer nonlinear programming. Eng. Constr. Archit. Manag. 26(10), 2474–2508 (2019)

    Article  Google Scholar 

  2. Baek, Y.S., Lee, S., Filatov, M., et al.: Optimization of three state conical intersections by adaptive penalty function algorithm in connection with the mixed-reference spin-flip time-dependent density functional theory method (MRSF-TDDFT). J. Phys. Chem. A 125(9), 1994–2006 (2021)

    Article  Google Scholar 

  3. Li, N., Wang, S.: Pricing options on investment project expansions under commodity price uncertainty. J. Ind. Manag. Optim. 15(1), 261–273 (2019)

    MathSciNet  MATH  Google Scholar 

  4. Davydova, T., Arsen’Ev, Y., Shelobaev, S.: Project and program management with optimization of their functioning. Econ. XXI Century Innov. Invest. Educ. 7(1), 38–45 (2020)

    Google Scholar 

  5. He, W., Shi, Y., Kong, D.: Optimization model calculation of construction cost and time based on genetic algorithm. IOP Conf. Ser. Earth Environ. Sci. 242(6), 062044 (7 pp) (2019)

    Google Scholar 

  6. Chengke, W., Chunjiang, C., Rui, J., et al.: Understanding laborers’ behavioral diversities in multinational construction projects using integrated simulation approach. Eng. Constr. Archit. Manag. 26(9), 2120–2146 (2019)

    Article  Google Scholar 

  7. Meneylyuk, A., Nikiforov, A.: Optimization of managerial, organizational and technological solutions of grain storages construction and reconstruction. Tehnički Glasnik 14(2), 121–134 (2020)

    Article  Google Scholar 

  8. Филимoнoвa, Л.A., Cквopцoвa, H.К.: Optimization models in project management through operating CASH FLOWS. Voprosy Regionalnoj Ekonomiki 1(42), 120–131 (2020)

    Google Scholar 

  9. Joshi, D., Mittal, M.L., Sharma, M.K., et al.: An effective teaching-learning-based optimization algorithm for the multi-skill resource-constrained project scheduling problem. J. Model. Manag. 14(4), 1064–1087 (2019)

    Google Scholar 

  10. Abhilasha, P., Kumar, T.K., Neeraj, J.K.: A qualitative framework for selection of optimization algorithm for multi-objective trade-off problem in construction projects. Eng. Constr. Archit. Manag. 26(9), 1924–1945 (2019)

    Article  Google Scholar 

  11. Mascaraque-Ramirez, C., Para-Gonzalez, L., Marco-Jornet, P.: Management of a ferry construction project using a production-oriented design methodology. J. Ship Prod. Des. 35(4), 309–316 (2019)

    Article  Google Scholar 

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Wang, Y. (2022). Balanced Optimization System of Construction Project Management Based on Improved Particle Swarm Algorithm. In: J. Jansen, B., Liang, H., Ye, J. (eds) International Conference on Cognitive based Information Processing and Applications (CIPA 2021). Lecture Notes on Data Engineering and Communications Technologies, vol 84. Springer, Singapore. https://doi.org/10.1007/978-981-16-5857-0_21

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