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  • Towards an optimal financial investment decision in Build-Operate-Transfer projects using genetic algorithms

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    40434.pdf (153.6Kb)
    Author(s)
    Islam, MD Mainul
    Mohamed, Sherif
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Mohamed, Sherif A.
    Islam, MD Mainul M.
    Year published
    2006
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    Abstract
    Investments into large, green-field infrastructure projects under build-operate-transfer arrangements are challenging, and present complex issues for potential project promoters. To demonstrate, during the tendering stage, the two main concerns for project promoters are ensuring a certain level of profit margin, and making the financial proposal as attractive as possible to the client. Hence, from the project promoter's point of view, a state of optimality exists between selections of the right combination of key financial factors with appropriate values. Prior research in this area is limited, and has only partially addressed ...
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    Investments into large, green-field infrastructure projects under build-operate-transfer arrangements are challenging, and present complex issues for potential project promoters. To demonstrate, during the tendering stage, the two main concerns for project promoters are ensuring a certain level of profit margin, and making the financial proposal as attractive as possible to the client. Hence, from the project promoter's point of view, a state of optimality exists between selections of the right combination of key financial factors with appropriate values. Prior research in this area is limited, and has only partially addressed this optimization issue in a fragmented fashion. This paper provides a novel approach by integrating the leading financial elements pertaining to capital budgeting and project financing aspects, which in turn ensures optimum financial viability to promoters. Optimality equations and constraints, based on discounted cash flow analysis are developed, and the non-linear behavior of the objective function is accounted for. Finally, a genetic algorithms-based financial optimization model is developed to reach the near-optimal solution for maximizing the winning potential of the concession agreement under a reasonable profit margin from the equity holder's perspective. The proposed model is demonstrated through a numerical example, which will help improve the financial decision-making processes in an efficient and effective way.
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    Conference Title
    Joint International Conference on Computing and Decision Making in Civil and Building Engineering
    Publisher URI
    http://www.asce.org/
    Copyright Statement
    © The Author(s) 2006. The attached file is posted here with permission of the copyright owners for your personal use only. No further distribution permitted.
    Publication URI
    http://hdl.handle.net/10072/13130
    Collection
    • Conference outputs

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