A concessionaire selection decision model development and application for the PPP project procurement

The public-private partnership (PPP) arrangements require the optimization of risk allocation between the public and private sectors in order to achieve the best net present value (NPV). Many researchers mentioned that the risk events of a PPP infrastructure projects are interdependent over project life cycle. Sterman (1992) stated that a large-scale construction project that is complex and has highly dynamic and interdependent risks and uncertainties over long-term project life cycle. Williams (2002) also mentioned that the risk usually interact each other with nonlinear relationships over time in a complex project. Dey and Ogunlana (2004) contended that there is a need to analyze risk interactions of complex infrastructure projects such as build-operate-transfer (BOT) projects over their long-term project life. In modern approaches to PPP project risk management, experts assume risk factors are independent and ignore the risk interaction effects over project life cycle, so the project risks cannot be effectively managed and controlled. The researcher proposed a modelling approach that used a risk network model applying System Dynamics (SD) techniques to estimate risk interaction effects on project NPV over time. The researcher used another SD model built on the risk network model to estimate the beneficial effects of bidding proposals on project NPV over time and to see how efficiently the risk effects can be reduced and the NPV performance can be improved. Then, the researcher applied appropriate stochastic analyses including mean-variance, mean semi-variance, stochastic dominance and expected-loss ratio to compare range values of NPV among different bidding proposals. A capable PPP concessionaire with the best project NPV performance can hence be selected. An industry case was applied to demonstrate SD decision models. The SD decision models have been validated through the behaviour reproduction test and multivariate sensitivity analysis. This proved that the proposed approach is robust and applicable to address real world problems to evaluate the longterm performance of a PPP project concessionaire

Name dc:type.qualificationname

Ph.D.

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

University of Southampton

Year dc:date.issued

2011

Author dc:creator

• Jang, Steve

Advisors dc:contributor.advisor

• Williams, T.M.
• Brailsford, S.C.