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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 84
Edited by: B.H.V. Topping, G. Montero and R. Montenegro
Paper 50

A Multi-objective Genetic Algorithm Model for Time-Cost Trade-Off Analysis of Construction Projects

A. Senouci and H.R. Al-Derham

Department of Civil Engineering, University of Qatar, Qatar

Full Bibliographic Reference for this paper
A. Senouci, H.R. Al-Derham, "A Multi-objective Genetic Algorithm Model for Time-Cost Trade-Off Analysis of Construction Projects", in B.H.V. Topping, G. Montero, R. Montenegro, (Editors), "Proceedings of the Fifth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 50, 2006. doi:10.4203/ccp.84.50
Keywords: genetic algorithms, multi-objective optimization, construction projects, planning and cost control, time-cost trade-off, information technology.

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 time-cost 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 so-called time-cost trade-off analysis to lower the project cost without impact on the project duration. The results of this analysis are: (1) a time-cost trade-off 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 time-cost trade-off 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 time-cost trade-off 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 large-scale construction project scheduling problems (i.e., several hundred activities). Feng et al. [4] and Zheng et al. [5] developed genetic algorithm models for construction time-cost trade-off analysis. Both models use a simple approach for solving this multi-objective optimization problem. However, more advanced tools for multi-objective optimization need to be used to solve this problem.

This paper presents an advanced and robust multi-objective genetic algorithm model for the time-cost trade-off analysis of construction projects. The model allows construction planners to generate and evaluate optimal or near-optimal trade-offs 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 multi-objective optimization module that searches for and identifies optimal or near optimal trade-offs 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 time-cost trade-off problems and provides a practical tool for practitioners to apply the model in practice.

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