TECHNICAL PAPERS
Sep 1, 2008

Optimizing Construction Time and Cost Using Ant Colony Optimization Approach

Publication: Journal of Construction Engineering and Management
Volume 134, Issue 9

Abstract

Time and cost are the most important factors to be considered in every construction project. In order to maximize the return, both the client and contractor would strive to optimize the project duration and cost concurrently. Over the years, many research studies have been conducted to model the time–cost relationships, and the modeling techniques range from the heuristic methods and mathematical approaches to genetic algorithms. Despite that, previous studies often assumed the time being constant leaving the analyses based purely on a single objective—cost. Acknowledging the significance of time–cost optimization, an evolutionary-based optimization algorithm known as ant colony optimization is applied to solve the multiobjective time–cost optimization problems. In this paper, the basic mechanism of the proposed model is unveiled. Having developed a program in the Visual Basic platform, tests are conducted to compare the performance of the proposed model against other analytical methods previously used for time–cost modeling. The results show that the ant colony system approach is able to generate better solutions without utilizing much computational resources which provides a useful means to support planners and managers in making better time–cost decisions efficiently.

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References

Adeli, H., and Karim, A. (1997). “Scheduling/cost optimization and neural dynamics model for construction.” J. Constr. Eng. Manage., 123(4), 450–458.
Ahmed, B. S., and Eldin, N. N. (2004). “Use of genetic algorithms in resource scheduling of construction project.” J. Constr. Eng. Manage., 130(6), 869–877.
Burns, S., Liu, L., and Feng, C. (1996). “The LP/IP hybrid method for construction time-cost trade-off analysis.” Constr. Manage. Econom., 14(3), 265–276.
Christodoulou, S. (2005). “Ant colony optimization in construction scheduling.” Proc., 2005 ASCE Int. Conf. on Computing in Civil Engineering, L. Soibelman and F. Pena-Mora, eds., ASCE, Reston, Va., 1787–1797.
Colorni, A., Dorigo, M., and Mamiezzo, V. (1991). “Distributed optimization by ant colonies.” Proc., 1st European Conf. on Artificial Life: Toward a Practice of Autonomous Systems, F. J. Varela and P. Bourgine, eds., MIT Press, Cambridge, Mass., 134–142.
De, P., Dunne, E. J., Ghosh, J. B., and Wells, C. E. (1995). “The discrete time-cost tradeoff problem revisited.” Eur. J. Oper. Res., 81(2), 225–238.
Dorigo, M. (1992). “Optimization, learning and natural algorithms.” Ph.D. thesis, Politecnico di Milano, Italy.
Dorigo, M., Di Caro, G., and Gambardella, L. M. (1999). “Ant algorithms for discrete optimization.” Artif. Life, 5(1), 137–172.
Dorigo, M., and Gambardella, L. M. (1996). “Ant colonies for the traveling salesman problem.” TR/IRIDIA/1996-3, Université Libre de Bruxelles, Belgium.
Dorigo, M., and Gambardella, L. M. (1997). “Ant colony system: A cooperative learning approach to the traveling salesman problem.” IEEE Trans. Evol. Comput., 1(1), 53–66.
Dorigo, M., Maniezzo, V., and Colorni, A. (1996). “Ant system: Optimization by a colony of cooperating agents.” IEEE Trans. Syst., Man, Cybern., Part B: Cybern., 26(1), 29–41.
Dorigo, M., and Stützle, T. (2004). Ant colony optimization, MIT Press, Cambridge, Mass.
Duan, H. B. (2005). Ant colony algorithms: Theory and application, Science Press, Beijing (in Chinese).
Elbeltagi, E., Hegazy, T., and Grierson, D. (2005). “Comparison among five evolutionary-based optimization algorithms.” Adv. Eng. Inf., 19(1), 43–53.
Elmaghraby, S. E. (1993). “Resource allocation via dynamic programming in activity networks.” Eur. J. Oper. Res., 64(2), 199–215.
Eusuff, M. M., and Lansey, K. E. (2003). “Optimization of water distribution network design using the shuffled frog leaping algorithm.” J. Water Resour. Plann. Manage., 129(3), 210–225.
Feng, C., Liu, L., and Burn, S. (1997). “Using genetic algorithms to solve construction time-cost trade-off problems.” J. Comput. Civ. Eng., 11(3), 184–189.
Feng, C., Liu, L., and Burns, S. (2000). “Stochastic construction time-cost trade-off analysis.” J. Comput. Civ. Eng., 14(2), 117–126.
Fondahl, J. W. (1961). “A noncomputer approach to the critical path method for the construction industry.” Technical Rep. No. 9, The Construction Institute, Dept. of Civil Engineering, Stanford Univ., Stanford, Calif.
Gambardella, L. M., and Dorigo, M. (1996). “Solving symmetric and asymmetric TSPs by ant colonies.” Proc., IEEE Conf. on Evolutionary Computation, T. Baeck, T. Fukuda, and Z. Michalewicz, eds., IEEE Press, Piscataway, N.J., 622–627.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, Mass.
Henderickson, C., and Au, T. (1989). Project management for construction, Prentice-Hall, Englewood Cliffs, N.J.
Holland, J. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, MIT Press, Cambridge, Mass.
Kelly, J. E., Jr. (1961). “Critical path planning and scheduling: Mathematical basis.” Oper. Res., 9(3), 167–179.
Kennedy, J., and Eberhart, R. (1995). “Particle swarm optimization.” Proc., IEEE Int. Conf. on Neural Networks, H. Berenji, ed., IEEE Service Center, Piscataway, N.J., 1942–1948.
Li, H., and Love, P. E. D. (1997). “Using improved genetic algorithms to facilitate time-cost optimization.” J. Constr. Eng. Manage., 123(3), 233–237.
Liu, L., Burns, S., and Feng, C. (1995). “Construction time-cost trade-off analysis using LP/IP hybrid method.” J. Constr. Eng. Manage., 121(4), 446–454.
Lovbjerg, M. I. (2002). “Improving particle swarm optimization by hybridization of stochastic search heuristics and self-organized criticality.” Master’s thesis, Aarhus Univertet, Denmark.
Lu, M., and Li, H. (2003). “Resource-activity critical-path method for construction planning.” J. Constr. Eng. Manage., 129(4), 412–420.
Mekle, D., Middendorf, M., and Schmeck, H. (2002). “Ant colony optimization for resource-constrained project scheduling.” IEEE Trans. Evol. Comput., 6(4), 333–346.
Meyer, W. L., and Shaffer, L. R. (1963). “Extensions of the critical path method through the application of integer programming.” Civil Engineering Construction Research Series 2, Univ. of Illinois, Urbana, Ill.
Moscato, P. (1989). “On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms.” Technical Rep. No. 826, Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, Calif.
Moselhi, O. (1993). “Schedule compression using the direct stiffness method.” Can. J. Civ. Eng., 20(1), 65–72.
Ng, S. T., Deng, M. Z., Skitmore, R. M., and Lam, K. C. (2000). “A conceptual case-based decision module for mitigating construction delays.” Int. J. Constr. Inf. Technol., 8(2), 1–20.
Pagnoni, A. (1990). Project engineering: Computer-oriented planning and operational decision making, Springer, Berlin.
Patterson, J. H., and Huber, D. (1974). “A horizon-varying, zero-one approach to project scheduling.” Manage. Sci., 20(6), 990–998.
Pena-Mora, F., and Park, M. (2001). “Dynamic planning for fast-tracking building construction projects.” J. Constr. Eng. Manage., 127(6), 445–456.
Prager, W. (1963). “A structural method of computing project cost polygons.” Manage. Sci., 9(3), 394–404.
Robinson, D. R. (1975). “A dynamic programming solution to cost-time tradeoff for CPM.” Manage. Sci., 22(2), 158–166.
Shyu, S. J., Lin, B. M. T., and Yin, P. Y. (2004). “Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time.” Comput. Ind. Eng., 47(2), 181–193.
Siemens, N. (1971). “A simple CPM time-cost tradeoff algorithm.” Manage. Sci., 17(6), 354–363.
Solimanpur, M., Vrat, P., and Shankar, R. (2005). “An ant algorithm for the single row layout problem in flexible manufacturing systems.” Comput. Oper. Res., 32(3), 583–598.
Waugh, L. M., and Froese, T. M. (1990). “Constraint knowledge for construction scheduling.” Proc., 1st Int. Conf. on Expert Planning Systems, IEE Press, London, 114–118.
Zheng, D. X. M., Ng, S. T., and Kumaraswamy, M. M. (2004). “Applying a genetic algorithm-based multiobjective approach for time-cost optimization.” J. Constr. Eng. Manage., 130(2), 168–176.
Zheng, D. X. M., Ng, S. T., and Kumaraswamy, M. M. (2005). “Applying Pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization.” J. Constr. Eng. Manage., 131(1), 81–91.

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 134Issue 9September 2008
Pages: 721 - 728

History

Received: Feb 9, 2007
Accepted: Nov 16, 2007
Published online: Sep 1, 2008
Published in print: Sep 2008

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Authors

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S. Thomas Ng
Associate Professor, Dept. of Civil Engineering, The Univ. of Hong Kong, Pokfulam Rd., Hong Kong (corresponding author). E-mail: [email protected]
Yanshuai Zhang
MPhil Candidate, Dept. of Civil Engineering, The Univ. of Hong Kong, Pokfulam Rd., Hong Kong. E-mail: [email protected]

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