Applying Pareto Ranking and Niche Formation to Genetic Algorithm-Based Multiobjective Time–Cost Optimization
Publication: Journal of Construction Engineering and Management
Volume 131, Issue 1
Abstract
Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms -driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the -based multiobjective TCO model.
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References
Feng, C., Liu, L., and Burns, S. (1997). “Using genetic algorithms to solve construction time–cost trade-off problems.” J. Comput. Civ. Eng., 11(3), 184–189.
Fondahl, J. W. (1961). “A noncomputer approach to the critical path method for the construction industry.” Tech. Rep No. 9, Dept. of Civil Engineering, The Construction Institute, Stanford Univ., Stanford, Calif.
Gen, M., and Cheng, R. (2000). Genetic algorithms and engineering optimization, Wiley–Interscience, New York.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addision–Wesley, Reading, Mass.
Hegazy, T. (1999). “Optimization of construction time–cost trade-off analysis using genetic algorithms.” Can. J. Civ. Eng., 26, 685–697.
Henderickson, C., and Au, T. (1989). Project management for construction, Prentice–Hall, Englewood Cliffs, N.J.
Kasprowicz, T. (1994). “Multiobjective optimization of constrution schedules.” Proc., 1st Congress on Computing in Civil Engineering, Technical Council on Computing Practices, K. Khozeimeh, ed., ASCE, Washington, D.C., June 20–22, 185–190.
Kelly, J. E. (1961). “Critical path planning and scheduling: Mathematical basis ” Oper. Res., 9(3), 167–179.
Knowles, J. D., Watson, R. A., and Corne, D. W. (2001). “Reducing local optima in single-objective problems by multiobjection.” Lecture Notes in Computer Science, Vol. 1993, E. Zitzler et al., eds., Springer, Berlin, 269–283.
Laptali, E., Bouchlagham, N., and Wild, S. (1997). “Planning and estimating in practice and the use of integrated computer modules.” Autom. Constr., 7(1), 71–76.
Li, H., and Love, P. E. (1997). “Using improved genetic algorithms to facilitate time–cost optimization.” J. Constr. Eng. Manage., 123(3), 233–237.
Li, H., Cao, J. N., and Love, P. E. (1999). “Using machine learning and GA to solve time–cost trade-off problems.” J. Constr. Eng. Manage., 125 (5), 347–353.
Liu, L., Burns, S., and Feng, C. (1995). “Construction time–cost trade-off analysis using LP/IP hybrid model.” J. Constr. Eng. Manage., 121(4), 446–454.
Meyer, W. L., and Shaffer, L. R. (1963). “Extensions of the critical path method through the application of integer programming.” Civ. Eng. Constr. Res. Ser 2, Univ. of Illinois, Urbana, Ill.
Moselhi, O. (1993). “Schedule compression using the direct stiffness method.” Can. J. Civ. Eng., 20, 65–72.
Ng, S. T., Deng, M. Z. M., Skitmore, R. M., and Lam, K. C. (2000). “A conceptual case-based decision module for mitigating construction delays.” Int. J. Constr. Inf. Technol., 8, 1–20.
Nkasu, M. M., and Leung, K. H. (1997). “Resources scheduling decision support system for concurrent project management.” Int. J. Prod. Res., 35(11), 3107–3132.
Osyczka, A. (2002). Evolutionary algorithms for single and multicriteria design optimization, Physica, New York.
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.
Prager, W. (1963). “A structural method of computing project cost polygons.” Manage. Sci., 9(3), 394–404.
Siemens, N. (1971). “A simple CPM time–cost trade-off algorithm.” Manage. Sci., 17(6), 354–363.
Wang, C. H., and Huang, Y. C. (1998). “Optimization module for construction project durations using a multistage decision process.” Eng. Optimiz., 30(2), 155–173.
Zheng, D. X. M., Ng, T. S. T., and Kumaraswamy, M. M. (2002). “Applying genetic algorithms techniques for time-cost optimization.” Proc., 18th Annual Conf. ARCOM, D. Greenwood, ed., Univ. of Northumbria, Middleborough, U.K., September 2–4, 801–810.
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Information
Published In
Journal of Construction Engineering and Management
Volume 131 • Issue 1 • January 2005
Pages: 81 - 91
Copyright
© 2004 ASCE.
History
Received: Mar 18, 2003
Accepted: Jan 20, 2004
Published online: Jan 1, 2005
Published in print: Jan 2005
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