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CivilComp Proceedings
ISSN 17593433 CCP: 84
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping, G. Montero and R. Montenegro
Paper 50
A Multiobjective Genetic Algorithm Model for TimeCost TradeOff Analysis of Construction Projects A. Senouci and H.R. AlDerham
Department of Civil Engineering, University of Qatar, Qatar A. Senouci, H.R. AlDerham, "A Multiobjective Genetic Algorithm Model for TimeCost TradeOff Analysis of Construction Projects", in B.H.V. Topping, G. Montero, R. Montenegro, (Editors), "Proceedings of the Fifth International Conference on Engineering Computational Technology", CivilComp Press, Stirlingshire, UK, Paper 50, 2006. doi:10.4203/ccp.84.50
Keywords: genetic algorithms, multiobjective optimization, construction projects, planning and cost control, timecost tradeoff, information technology.
Summary
Construction planners face the decisions of selecting appropriate resources,
including crew sizes, equipment, methods, and technologies to perform the activities
of a project. These decisions will ultimately decide the duration and cost of a
project. For example, using more productive equipment or hiring more workers may
save time, but the cost could increase. Finding the most cost effective way to
complete a project within time limits is desirable for project planners. Because of the
timecost relationships among activities, it usually takes several iterations to select
the proper methods, equipment, and crew sizes to obtain an acceptable overall
project duration within the contractual time limit. Since not all activities are critical,
some activities can be delayed without impact on the overall project duration.
Schedulers can perform the socalled timecost tradeoff analysis to lower the
project cost without impact on the project duration. The results of this analysis are:
(1) a timecost tradeoff curve showing the relationship between project duration and
cost; and (2) the selection of construction resources and methods, which provide
the optimal balance of time and cost.
Significant research advancements have been made in the area of timecost tradeoff analysis of construction projects. A number of optimization models have been developed using a variety of methods, including heuristics methods, mathematical programming, and genetic algorithms. Heuristic methods, which are based on rules of thumb, do not guarantee optimal solutions [1]. Mathematical programming methods convert the timecost tradeoff program to mathematical models and utilize linear programming, integer programming, or dynamic programming to solve them [2,3]. Mathematical models are usually not suitable for solving largescale construction project scheduling problems (i.e., several hundred activities). Feng et al. [4] and Zheng et al. [5] developed genetic algorithm models for construction timecost tradeoff analysis. Both models use a simple approach for solving this multiobjective optimization problem. However, more advanced tools for multiobjective optimization need to be used to solve this problem. This paper presents an advanced and robust multiobjective genetic algorithm model for the timecost tradeoff analysis of construction projects. The model allows construction planners to generate and evaluate optimal or nearoptimal tradeoffs between project durations and total costs. Each of these plans identifies, from a set of feasible alternatives, an optimal crew formation for each activity in the project. To accomplish this, the model incorporates (1) a scheduling module that calculate the project duration; (2) a cost module that computes the project direct, indirect, and total costs; and (3) a multiobjective optimization module that searches for and identifies optimal or near optimal tradeoffs between project durations and total costs. The accuracy of the model was verified by many test cases. The development of the new model provides an attractive alternative to solving construction timecost tradeoff problems and provides a practical tool for practitioners to apply the model in practice. References
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