Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30982
Financing - Scheduling Optimization for Construction Projects by using Genetic Algorithms

Authors: Hesham Abdel-Khalek, Sherif M. Hafez, Abdel-Hamid M. el-Lakany, Yasser Abuel-Magd

Abstract:

Investment in a constructed facility represents a cost in the short term that returns benefits only over the long term use of the facility. Thus, the costs occur earlier than the benefits, and the owners of facilities must obtain the capital resources to finance the costs of construction. A project cannot proceed without an adequate financing, and the cost of providing an adequate financing can be quite large. For these reasons, the attention to the project finance is an important aspect of project management. Finance is also a concern to the other organizations involved in a project such as the general contractor and material suppliers. Unless an owner immediately and completely covers the costs incurred by each participant, these organizations face financing problems of their own. At a more general level, the project finance is the only one aspect of the general problem of corporate finance. If numerous projects are considered and financed together, then the net cash flow requirements constitute the corporate financing problem for capital investment. Whether project finance is performed at the project or at the corporate level does not alter the basic financing problem .In this paper, we will first consider facility financing from the owner's perspective, with due consideration for its interaction with other organizations involved in a project. Later, we discuss the problems of construction financing which are crucial to the profitability and solvency of construction contractors. The objective of this paper is to present the steps utilized to determine the best combination of minimum project financing. The proposed model considers financing; schedule and maximum net area .The proposed model is called Project Financing and Schedule Integration using Genetic Algorithms "PFSIGA". This model intended to determine more steps (maximum net area) for any project with a subproject. An illustrative example will demonstrate the feature of this technique. The model verification and testing are put into consideration.

Keywords: Optimization, Investment, Scheduling, Project Management, interest, cash flow, Large-scale ConstructionProjects, Loan, Financing and Genetic Algorithms

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060183

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1861

References:


[1] Cui, Q., Hastak, M., Halpin, D. W. 2010. Systems analysis of project cash flow management strategies. Construction Management and Economics In print: 1-16.
[2] Senouci, A. B., El-Rayes, K. A. 2009. Time-profit trade-off analysis for construction projects. Journal of Construction Engineering and Management 135(8): 718-725.
[3] Elazouni, A. M., Metwally, F. G. 2005. Finance-based scheduling: Tool to maximize project profit using improved genetic algorithms. Journal of Construction Engineering and Management 131(4): 400- 412.
[4] Elazouni, A. M., Metwally, F. G. 2007. Expanding finance-based scheduling to devise overall-optimized project schedules. Journal of Construction Engineering and Management 133(1): 86-90
[5] Elazouni, A. M. 2009. Heuristic method for multi-project finance-based scheduling. Construction Management and Economics 27(2): 199-211.
[6] Liu, S.-S., Wang, C.-J. 2008. Resource-constrained construction project scheduling model for profit maximization considering cash flow. Automation in Construction 17(8): 966-974
[7] Barbosa, P. S. F., Pimentel, P. R. 2001. A linear programming model for cash flow management in the Brazilian construction industry. Construction Management and Economics 19(5): 469-479.
[8] Halpin, D. W., Woodhead, R. W. 1998. Construction management. New York, NY: John Wiley & Sons.
[9] Garner, D. R., Owen, R. R., Conway, R. P. 1994. The Ernst & Young guide to financing for growth. New York, NY: John Wiley & Sons
[10] Elazouni, A. M., Metwally, F. G. 2005. Finance-based scheduling: Tool to maximize project profit using improved genetic algorithms. Journal of Construction Engineering and Management 131(4): 400- 412
[11] EL-Beltagi E., Hegazy T., and Grierson D. (2005). "Comparison among five evolutionary-based optimization algorithms" Advanced Engineering Informatics (19), 43-53.
[12] Goldberg D. E. (1989). "Genetic Algorithm in Search Optimization and Machine Learning" Addison-Wesley reading, University of Alabama, U.S.A.