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East, E W and Liu, L Y (2006) Multiproject Planning and Resource Controls for Facility Management. Journal of Construction Engineering and Management, 132(12), 1294–305.

Elazouni, A M (2006) Classifying Construction Contractors Using Unsupervised-Learning Neural Networks. Journal of Construction Engineering and Management, 132(12), 1242–53.

Elmisalami, T, Walters, R and Jaselskis, E J (2006) Construction IT Decision Making Using Multiattribute Utility Theory for Use in a Laboratory Information Management System. Journal of Construction Engineering and Management, 132(12), 1275–83.

Menches, C L and Hanna, A S (2006) Conceptual Planning Process for Electrical Construction. Journal of Construction Engineering and Management, 132(12), 1306–13.

Menches, C L and Hanna, A S (2006) Quantitative Measurement of Successful Performance from the Project Manager’s Perspective. Journal of Construction Engineering and Management, 132(12), 1284–93.

Moussa, M, Ruwanpura, J and Jergeas, G (2006) Decision Tree Modeling Using Integrated Multilevel Stochastic Networks. Journal of Construction Engineering and Management, 132(12), 1254–66.

Sharma, V, Al-Hussein, M and AbouRizk, S M (2006) Residential Construction Lot Grading Approval Process Optimization: Case Study of City of Edmonton. Journal of Construction Engineering and Management, 132(12), 1225–33.

Song, Y and Chua, D K H (2006) Modeling of Functional Construction Requirements for Constructability Analysis. Journal of Construction Engineering and Management, 132(12), 1314–26.

Su, Y Y, Hashash, Y M A and Liu, L Y (2006) Integration of Construction As-Built Data Via Laser Scanning with Geotechnical Monitoring of Urban Excavation. Journal of Construction Engineering and Management, 132(12), 1234–41.

Zhang, H, Tam, C M, Li, H and Shi, J J (2006) Particle Swarm Optimization-Supported Simulation for Construction Operations. Journal of Construction Engineering and Management, 132(12), 1267–74.

  • Type: Journal Article
  • Keywords: Optimization; Simulation; Construction management; Particles;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:12(1267)
  • Abstract:
    This study proposes an integration of particle swarm optimization (PSO) and a construction simulation so as to determine efficiently the optimal resource combination for a construction operation. The particle-flying mechanism is utilized to guide the search process for the PSO-supported simulation optimization. A statistics method, i.e., multiple-comparison procedure, is adopted to compare the random output performances resulting from the stochastic simulation model so as to rank the alternatives (i.e., particle-represented resource combinations) during the search process. The indifference zone and confidence interval facilitate consideration of the secondary performance measure (e.g., productivity) when the main performance measures (e.g., cost) of the competing alternatives are close. The experimental analyses demonstrate the effectiveness and efficiency of the proposed simulation optimization. The study aims to providing an alternative combination of optimization methodology and general construction simulation by utilizing PSO and a statistics method so as to improve the efficiency of simulation in planning construction operations.