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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 89
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY
Edited by: M. Papadrakakis and B.H.V. Topping
Paper 66

Cost Optimization of Projects with Repetitive Activities Using Genetic Algorithms

E. Elbeltagi1 and E.M. ElKassas2

1Structural Engineering Department, Mansoura University, Egypt
2Construction and Building Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

Full Bibliographic Reference for this paper
E. Elbeltagi, E.M. ElKassas, "Cost Optimization of Projects with Repetitive Activities Using Genetic Algorithms", in M. Papadrakakis, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 66, 2008. doi:10.4203/ccp.89.66
Keywords: linear, repetitive projects, scheduling, cost optimization, genetic algorithms.

Summary
Scheduling of construction projects which have multiple units, wherein activities repeat from unit to another, always represents a major challenge to project managers. These projects require schedules that ensure the uninterrupted usage of resources from one unit to another and maintaining logic constraints at the same time. Such projects in the construction industry are characterized by high costs, long durations and utilization of many expensive resources. So, effective planning and scheduling of repetitive projects is very important in order to save time and cost. In this paper, a proposed method is introduced to schedule repetitive projects with the objective of optimizing the project total cost which comprises direct, indirect and interruption costs. The proposed model encompasses two modules: a resource-driven scheduling module; and an optimization module. The proposed model considers both typical and atypical repetitive activities; uses multiple crews and assigns an available crew to the next units; considers different construction methods for each activity; maintains work continuity; and allows for activity interruption. A genetic algorithm optimization module is used to search for the optimum schedule that minimizes total project costs. Details of the model development and implementation are described along with a real life case study to demonstrate the practicality and the capabilities of the approach developed.

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