Multi-objective Comprehensive Optimal Management of Construction Projects Based on Particle Algorithm

Authors

  • Zijing Wang

DOI:

https://doi.org/10.31449/inf.v43i3.2914

Abstract

Construction industry is one of the pillars of rapid economic development. The optimization of construction project management can greatly optimize the cycle, cost and quality of projects. In this paper, the multi-objective management optimization model of construction projects and the particle swarm optimization (PSO) algorithm for calculating the optimal solution of the model  are briefly introduced,  genetic operators are  introduced into the PSO algorithm to prevent "premature", so as to improve the accuracy of the solution, and then case analysis is performed on a single-storey building project. The results show that the algorithm converges to stability and obtains the optimum solution set after 400 times of iterations and a total of 63250 s.  The construction period of each process of  the solution with the shortest total construction period in the optimum solution set is shorter than that before optimization, the total construction period reduces by 56 days, the total cost reduces by 520,000 yuan, and the total quality increases by 3.58. In summary, the improved PSO algorithm can effectively optimize the management of construction projects.

References

Prayogo D, Cheng MY, Wong FT, Tjandra D, Tran

HD. (2018). Optimization model for construction

project resource leveling using a novel modified

symbiotic organisms search. Asian Journal of Civil

Engineering, 19 (2), pp. 1-14.

https://doi.org/10.1007/s42107-018-0048-x.

Shahriari MR. (2016). Multi-objective optimization

of discrete time-cost Tradeoff Problem in project

networks using non-dominated sorting genetic

algorithm. Journal of Industrial Engineering

International, 12 (2), pp. 159-169.

https://doi.org/10.1007/s40092-016-0148-8.

Mavrotas G, Figueira JR, Siskos E. (2015).

Robustness analysis methodology for multiobjective combinatorial optimization problems and

application to project selection. Omega, 52

(Complete), pp. 142-155.

https://doi.org/10.1016/j.omega.2014.11.005.

Elbeltagi E, Ammar M, Sanad H, Kassab M. (2016).

Overall multiobjective optimization of construction

projects scheduling using particle swarm.

Engineering, Construction and Architectural

Management, 23 (3), pp. 265-282.

https://doi.org/10.1108/ECAM-11-2014-0135.

Senouci AB, Mubarak SA. (2016). Multiobjective

optimization model for scheduling of construction

projects under extreme weather. Journal of Civil

Engineering and Management, 22 (3), pp. 373-381.

https://doi.org/10.3846/13923730.2014.897968.

Cheng MY, Tran DH. (2015). Opposition-based

Multiple Objective Differential Evolution

(OMODE) for optimizing work shift schedules.

Automation in Construction, 55, pp. 1-14.

https://doi.org/10.1016/j.autcon.2015.03.021.

Elbeltagi E, Ammar M, Sanad H, et al. (2016).

Overall multiobjective optimization of construction

projects scheduling using particle swarm.

Engineering Construction & Architectural

Management, 23 (3), pp. 265-282.

https://doi.org/10.1108/ECAM-11-2014-0135.

Shahsavar A, Najafi A, Niaki STA. (2015). Three

self-adaptive multi-objective evolutionary

algorithms for a triple-objective project scheduling

problem. Computers & Industrial Engineering, 87,

pp. 4-15.

https://doi.org/10.1016/j.cie.2015.04.027.

Cong M, Qu L. (2015). Multiobjective optimization

of switched reluctance motors based on design of

experiments and particle swarm optimization. IEEE

Transactions on Energy Conversion, 30 (3), pp.

-1153.

https://doi.org/10.1109/tec.2015.2411677.

Cheng S, Hao Z, Shu Z. (2016). An innovative

hybrid multi-objective particle swarm optimization

with or without constraints handling. Applied Soft

Computing, 47, pp. 370-388.

https://doi.org/10.1016/j.asoc.2016.06.012.

Zhang H, Yang Z. (2018). Accelerated particle

swarm optimization to solve large-scale network

plan optimization of resource-leveling with a fixed

duration. Mathematical Problems in Engineering,

pp. 1-11.

Masuda K, Kurihara K. (2015). A constrained

global optimization method based on multiobjective particle swarm optimization. Electronics

& Communications in Japan, 95 (1), pp. 43-54.

https://doi.org/10.1002/ecj.10385.

Lin Q, Li J, Du Z, Chen J. (2015). A novel multiobjective particle swarm optimization with multiple

search strategies. European Journal of Operational

Research, 247 (3), pp. 732-744.

https://doi.org/10.1016/j.ejor.2015.06.071.

Cao B, Zhao J, Lv Z, Liu X, Yang S, Kang X, Kang

K. (2017). Distributed Parallel Particle Swarm

Optimization For Multi-Objective And ManyObjective Large-Scale Optimization. IEEE Access,

PP (99), pp. 1-1.

https://doi.org/10.1109/ACCESS.2017.2702561.

Wood DA. (2017). Gas and oil project Time-costquality tradeoff: Integrated stochastic and fuzzy

multi-objective optimization applied a memetic,

nondominated, sorting algorithm. Journal of

Natural Gas Science and Engineering, pp.

S187551001730224X.

https://doi.org/10.1016/j.jngse.2017.04.033.

Xue Y, Chen WN, Gu T, Zhang H, Yuan H, Kwong

S, Zhang J. (2017). Set-based discrete particle

swarm optimization based on decomposition for

permutation-based multiobjective combinatorial

optimization problems. IEEE Transactions on

Cybernetics, PP (99), pp. 1-15.

https://doi.org/10.1016/j.jngse.2017.04.033.

Downloads

Published

2019-09-30

How to Cite

Wang, Z. (2019). Multi-objective Comprehensive Optimal Management of Construction Projects Based on Particle Algorithm. Informatica, 43(3). https://doi.org/10.31449/inf.v43i3.2914

Issue

Vol. 43 No. 3 (2019)

Section

Regular papers

License

I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.

I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.

In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.

I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal.  The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.


Copyright ©  Slovenian Society Informatika