Article ID Journal Published Year Pages File Type
385353 Expert Systems with Applications 2011 11 Pages PDF
Abstract

Public–private partnership (PPP) in infrastructure development is a principal-agent maximization problem that requires a win–win solution for the two partners, the public sector client and the private sector concessionaire. A variety of construction and market risks are involved, which if not properly managed, can significantly affect the economic, financial and social performance of a PPP project. The determination of a suitable concession period is one of the critical issues that have to be carefully examined for effective risk management toward successful PPP project development. This paper introduces an improved concession period determination methodology and develops a web-based concession period analysis system (WCPAS) based on this methodology. Integrating project scheduling tools, financial analysis methods and the Monte Carlo simulation technique, the WCPAS provides a systematic framework and organized modules that provide automatic support for data input and simulation-based analyses for construction cost, construction period, operation period and concession period. The WCPAS facilitates public clients in reasoning and quantifying construction and market risks in order to determine an appropriate concession period and consequently to minimize the potential social, economic and financial problems. A case study is carried out to illustrate the application and usefulness of the WCPAS.

► Public-private partnership (PPP) is a principal-agent maximization problem that requires a win-win solution. ► We introduce an improved concession period determination methodology. ► We develop a web-based concession period analysis system (WCPAS). ► The WCPAS integrates project scheduling tools, financial analysis methods and the Monte Carlo simulation technique. ► The WCPAS provides a systematic framework and organized modules for data input and simulation-based analysis of the concession period.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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