TECHNICAL PAPERS
Dec 1, 2006

Particle Swarm Optimization-Supported Simulation for Construction Operations

Publication: Journal of Construction Engineering and Management
Volume 132, Issue 12

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.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

The research has been supported by a Competitive Earmarked Research Grant for 2005/06 under Grant Number PolyU 5209/05E.

References

AbouRizk, S., and Shi, J. (1994). “Automated construction-simulation optimization.” J. Constr. Eng. Manage., 120(2), 374–385.
Ahmed, M. A., Alkhamis, T. M., and Hasan, M. (1997). “Optimizing discrete stochastic systems using simulated annealing and simulation.” Comput. Ind. Eng., 32, 832–836.
Alberto, I., Azcarate, C., Mallor, F., and Mateo, P. M. (2002). “Optimization with simulation and multiobjective analysis in industrial decision-making: A case study.” Eur. J. Oper. Res., 140(2), 373–383.
Azadivar, F., and Tompkins, G. (1999). “Simulation optimization with qualitative variables and structural model changes: a genetic algorithm approach.” Eur. J. Oper. Res., 113, 169–182.
Bulgak, A. A., and Sanders, J. L. (1988). “Integrating a modified simulated annealing algorithm with the simulation of a manufacturing system to optimize buffer sizes in automatic assembly systems.” Proc., 1985 Summer Simulation Conf., 711–715.
Catherine, E. D., and Laughery, K. R. (1996). “Advanced uses for Micro Saint simulation software.” Proc., 1996 Winter Simulation Conf., 510–516.
Cheng, T. M., and Feng, C. W. (2003). “An effective simulation mechanism for construction operations.” Autom. Constr., 12(3), 227–244.
Clerc, M., and Kennedy, J. (2002). “The particle swarm-explosion, stability, and convergence in a multidimensional complex space.” IEEE Trans. Evol. Comput., 6(1), 58–73.
Damerdji, H., and Nakayama, M. (1999). “Two-stage multiple-comparison procedures for steady-state simulations.” ACM Trans. Model. Comput. Simul., 9(1), 1–30.
Eberhart, R. C., and Kennedy, Y. J. (1995). “A new optimizer using particle swarm theory.” Proc., of the Sixth International Symposium on Micro Machine and Human Science [C], Nagoya (Japan), IEEE Press, Piscataway, N.J., 39–43.
Eberhart, R. C., and Shi, Y. (1998). “Comparison between genetic algorithms and particle swarm optimization.” Evolutionary Programming VII: Proc., 7th Annual Conf. on Evolutionary Programming, San Diego, 611–616.
Fu, M. (2002). “Optimization for simulation: Theory and practice.” INFORMS J. Comput., 14(3), 192–215.
Glover, F., Kelly, J. P., and Laguna, M. (1996). “New advances and applications of combining simulation and optimization.” Proc., 1996 Winter Simulation Conf., 144–152.
Goldsman, L., and Nelson, B. (1990). “Batch-size effects on simulation optimization using multiple comparisons with the best in computer simulation.” Proc., Winter Conf. on simulation, IEEE Press, Piscataway, N.J., 288–293.
Haddock, J., and Mittenthal, J. (1992). “Simulation optimization using simulated annealing.” Comput. Ind. Eng., 22(4), 387–395.
Halpin, D. W. (1977). “CYCLONE/: Method for modeling job site processes.” J. Constr. Div., 103(3), 489–499.
Halpin, D. W., and Riggs, L. S. (1992). Planning and analysis of construction operations, Wiley, New York.
Hills, P. R. (1971). HOCUS, P. E. Group, Egham, Surrey, U.K.
Kennedy, J., and Eberhart, R. C. (1995). “Particle swarm optimization.” Proc., IEEE Conf. on Neural Networks, IV, Piscataway, N.J., 1942–1948.
Kennedy, J., Eberhart, R. C., and Shi, Y. (2001). Swarm intelligence, Morgan Kaufmann, San Francisco.
Law, A. M., and Kelton, W. D. (2000). Simulation modeling and analysis, 3rd Ed., McGraw-Hill, New York.
Liu, L. Y., and Ioannou, P. G. (1992). “Graphical object-oriented discrete-event simulation system.” Proc., 1992 Winter Simulation Conf. IEEE, Piscataway, N.J., 1285–1291.
Magoulas, G., Eldabi, T., and Paul, R. (2002). “Global search strategies for simulation optimization.” Proc., 2002 Winter Simulation Conf., 1978–1985.
Martinez, J., and Ioannou, P. G. (1994). “General purpose simulation with stroboscope.” Proc., 1994 Winter Simulation Conf., IEEE, Piscataway, N.J., 1159–1166.
Matejcik, F., and Nelson, B. (1995). “Two-stage multiple comparisons with the best for computer simulation.” Oper. Res., 43(4), 633–640.
Nakayama, M. (1997). “Multiple-comparison procedures for steady-state simulations.” Ann. Stat. 25, 2433–2450.
Paul, R. J., and Chanev, T. S. (1998). “Simulation optimization using a genetic algorithm.” Simulation Practice and Theory, 6(6), 601–611.
Pierreval, H., and Tautou, L. (1997). “Using evolutionary algorithms and simulation for the optimization of manufacturing systems.” IIE Trans., 29(3), 181–189.
Shannon, R. E. (1975). Systems simulation: The art and science, Prentice-Hall, Englewood Cliffs, N.J.
Shi, J. (1999). “Activity-based construction (ABC) modeling and simulation method.” J. Constr. Eng. Manage., 125(5), 354–360.
Shi, J., and AbouRizk, S. M. (1995). “An optimization method for simulating large complex systems.” Eng. Optimiz., 25(3), 213–229.
Shi, Y., and Eberhart, R. C. (1998). “Parameter selection in particle swarm optimization.” Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, eds., Berlin.
Swisher, J. R., and Jacobson, S. H. (1999). “A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation.” Proc., 1999 Winter Simulation Conf., 492–501.
Tommelein, I. D., and Odeh, A. M. (1994). “Knowledge-based assembly of simulation networks using construction designs, plans, and methods.” Proc., 1994 Winter Simulation Conf., IEEE, New York, 1145–1158.
Trelea, I. C. (2003). “The particle swarm optimization algorithm: convergence analysis and parameter selection.” Inf. Process. Lett., 85(6), 317–325.
Zhang, H., and Li, H. (2004). “Simulation-based optimization for dynamic resource allocation.” Autom. Constr., 13(3), 407–418.
Zhang, H., Tam, C. M., and Heng, L. (2005). “Activity object-oriented simulation strategy for modeling construction operations.” J. Comput. Civ. Eng., 19(3), 313–322.

Information & Authors

Information

Published In

Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 132Issue 12December 2006
Pages: 1267 - 1274

History

Received: Nov 19, 2004
Accepted: Jan 23, 2006
Published online: Dec 1, 2006
Published in print: Dec 2006

Permissions

Request permissions for this article.

Authors

Affiliations

Hong Zhang
Lecturer, Dept. of Building and Construction, City Univ. of Hong Kong, Tat Chee Ave., Kowloon, Hong Kong.
C. M. Tam
Professor, Dept. of Building and Construction, City Univ. of Hong Kong, Tat Chee Ave., Kowloon, Hong Kong.
Heng Li
Professor, Dept. of Building and Real Estate, Hong Kong Polytechnic Univ., Hung Hom, Kowloon, Hong Kong.
Jonathan Jingsheng Shi
Associate Professor, Civil and Architectural Engineering Dept., Illinois Institute of Technology, USA.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share