Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 132, Issue 5
Abstract
This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers gratefully acknowledge the financial support provided by the National Science Foundation for this research project under the NSF CAREER Award No. NSFCMS-0238470. The writers also acknowledge the technical support provided by the National Center for Supercomputing Applications and the Department of Computational Science and Engineering at the University of Illinois at Urbana-Champaign.
References
Adeli, H. (2000). “High-performance computing for large-scale analysis, optimization, and control.” J. Aerosp. Eng., 13(1), 1–10.
Cantú-Paz, E. (1997). “A survey of parallel genetic algorithms.” ILLGAL Report No. 97003, Illinois Genetic Algorithms Laboratory, Univ. of Illinois at Urbana Champaign, Urbana, Ill.
Cantú-Paz, E. (2000). Efficient and accurate parallel genetic algorithms, Kluwer Academic, Boston, Mass.
De Santiago, E., and Law, K. H. (2000). “A distributed implementation of an adaptive finite element method for fluid problems.” Comput. Struct., 74(1), 97–119.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, New York.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2001). “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization.” KANGAL Rep. No. 200001, Genetic Algorithm Laboratory, Indian Institute of Technology, Kanpur, India.
Ellis, R., and Amos, S. (1996). “Development of work zone lighting standards for nighttime highway work.” Transportation Research Record 1529, Transportation Research Board, Washington, D. C.
El-Rayes, K. (2001). “Optimum planning of highway construction under the bidding method.” J. Constr. Eng. Manage., 127(4), 261–269.
El-Rayes, K., and Hyari, K. (2005a). “CONLIGHT: Lighting design model for nighttime highway construction.” J. Constr. Eng. Manage., 131(4), 467–476.
El-Rayes, K., and Hyari, K. (2005b). “Optimal lighting arrangements for nighttime highway construction projects.” J. Constr. Eng. Manage., 131(12), 1292–1300.
El-Rayes, K., and Kandil, A. (2005). “Time-cost-quality trade-off analysis for highway construction.” J. Constr. Eng. Manage., 131(4), 477–486.
El-Rayes, K., and Khalafallah, A. (2005). “Trade-off between safety and cost in planning construction site layouts.” J. Constr. Eng. Manage., 131(11), 1186–1195.
Feng, C., Liu, L., and Burns, S. (2000). “Stochastic construction time-cost trade-off analysis.” J. Comput. Civ. Eng., 14(2), 117–126.
Girianna, M. (2002). “Dynamic signal coordination model for a network with oversaturated intersections.” Ph.D. thesis, Univ. of Illinois at Urbana Champaign, Urbana, Ill.
Gropp, W., Lusk, E., and Thakur, R. (1999). Using MPI-2: Advanced features of the message passing interface, Massachusetts Institute of Technology, Cambridge, Mass.
Hegazy, T., and Kassab, M. (2003). “Resource optimization using combined simulation and genetic algorithms.” J. Constr. Eng. Manage., 129(6), 698–705.
Hegazy, T., and Petzold, K. (2003). “Genetic optimization for dynamic project control.” J. Constr. Eng. Manage., 129(4), 396–404.
Herbsman, Z. (1995). “ bidding method—Hidden success story for highway construction.” J. Constr. Eng. Manage., 121(4), 430–437.
Hyari, K., and El-Rayes, K. (2006) “Multiobjective optimization for scheduling repetitive construction projects.” J. Manage. Eng., in press.
Kandil, A., and El-Rayes, K. (2005). “Parallel computing framework for optimizing construction planning in large-scale projects.” J. Comput. Civ. Eng., 19(3), 304–312.
Marzouk, M., and Moselhi, O. (2004). “Multiobjective optimization of earthmoving operations.” J. Constr. Eng. Manage., 130(1), 105–113.
National Center For Super Computing Applications (NCSA). (2005). “The xeon linux cluster.” Univ. of Illinois at Urbana Champaign ⟨http://www.mcsa/ioic/edi⟩ June 6, 2005.
Noda, E., Coelho, A., Ricarte, I., Yamakami, A., and Freitas, A. (2002). “Devising adaptive migration policies for cooperative distributed genetic algorithms.” Proc., 2002 IEEE Int. Conf. on Systems, Man and Cybemetics, Yasmine Hammamet, Tunisia.
Quinn, M. J. (2004). Parallel programming in C with MPI and OpenMP, McGraw–Hill, New York.
Reed, P., Minsker, B. S., and Goldberg, D. E. (2003). “Simplifying multiobjective optimization: An automated design methodology for the non-dominated sorted genetic algorithm-II.” Water Resour. Res., 39(7), TNN21–TNN25.
Snir, M., Otto, S., Huss-Lederman, S., Walker, D., and Dongarra, J. (1998). MPI: The complete reference, Vol. 1, The MPI Core, Massachusetts Institute of Technology, Cambridge, Mass.
Zheng, D., and Ng, T. (2005). “Stochastic time-cost optimization model incorporating fuzzy sets theory and nanreplaceable fronts.” J. Constr. Eng. Manage., 131(2), 176–186.
Jaraiedi, M., Plummer, R., and Aber, M. (1995). “Incentive/disincentive guidelines for highway construction contracts.” J. Constr. Eng. Manage., 121(1), 112–120.
Zheng, D., Ng, T., and Kumaraswamy, M. (2005). “Applying Pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization.” J. Constr. Eng. Manage., 131(1), 81–91.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 132 • Issue 5 • May 2006
Pages: 491 - 498
Copyright
© 2006 ASCE.
History
Received: Jun 9, 2005
Accepted: Sep 14, 2005
Published online: May 1, 2006
Published in print: May 2006
Authors
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.