About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Proceedings of the 2nd International Conference on Engineering Management and Information Science, EMIS 2023, February 24-26, 2023, Chengdu, China

Research Article

Application of particle swarm optimization in multi-resource leveling optimization of engineering projects

Download347 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.4108/eai.24-2-2023.2330669,
        author={Jian  Tang and Lu  Lai},
        title={Application of particle swarm optimization in multi-resource leveling optimization of engineering projects},
        proceedings={Proceedings of the 2nd International Conference on Engineering Management and Information Science, EMIS 2023, February 24-26, 2023, Chengdu, China},
        publisher={EAI},
        proceedings_a={EMIS},
        year={2023},
        month={6},
        keywords={particle swarm optimization; relative weights; multiple resources; balanced optimization},
        doi={10.4108/eai.24-2-2023.2330669}
    }
    
  • Jian Tang
    Lu Lai
    Year: 2023
    Application of particle swarm optimization in multi-resource leveling optimization of engineering projects
    EMIS
    EAI
    DOI: 10.4108/eai.24-2-2023.2330669
Jian Tang1,*, Lu Lai1
  • 1: Jiangxi Science and Technology Normal University
*Contact email: 176024108@qq.com

Abstract

The purpose of this study is to use Particle Swarm 0ptimization (PSO) calculation to make the multiple resources in engineering projects reach global equilibrium after calculation. From the single-resource equilibrium optimization theory to the multi-resource optimization problem, the importance of engineering project resources is evaluated, appropriate evaluation indexes are selected, and a multi-resource equilibrium optimization mathematical model is established. Following that, the PSO solves the mathematical model, and the actual start time of activities (i.e., particle position) is constrained and rounded, subject to logical constraints between activities and time constraints. Finally, using the model to solve the case, the obtained results reduce the variance of resource intensity by 89.69% compared to the original solution, and the experimental results show that the PSO can effectively solve this kind of complex problem.

Keywords
particle swarm optimization; relative weights; multiple resources; balanced optimization
Published
2023-06-15
Publisher
EAI
http://dx.doi.org/10.4108/eai.24-2-2023.2330669
Copyright © 2023–2025 EAI
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL