Financing - Scheduling Optimization for Construction Projects by using Genetic Algorithms

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.




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.