Abstract

Adopting the most suitable PDS (Project Delivery System) is a process that entails thorough analysis of multiple criteria and does not follow a “one size fits all” approach. In most cases, the development agencies in post-conflict states resort to informal procedures in selecting a project delivery approach. There is an oversimplification of the decision making process in such a way that conclusions are often drawn in absence of careful review, and consideration of alternatives, or all determinant factors. The overarching objective of this research was to develop a scalable and site adaptable decision framework to facilitate objective selection of project delivery systems in post-conflict construction projects. This objective was primarily pursued through identifying the most pertinent selection factors in post-conflict projects. The research at hand consists of two PDS selection models. These models differ in their modality of judgment elicitation and score aggregation. At the output level, both models produce suitability index (SI) scores for the PDS options being considered. The SI Score is a sum product function of the relative importance weight (RIW) of the selection factors and the relative effectiveness values (REV) of the PDS options. In both models, the RIW’s were obtained through Analytic Network Process while the REV’s were directly assigned from a predefined measurement scale. The first model is predicated on individual assessment of the parameters leading to calculation of the suitability indices. This model applies Monte Carlo simulation to define a range for the suitability indices. The second model however, is hinged upon consensus-based assessment of the components of the suitability index. In the latter case, judgments are elucidated through successive decision conferencing workshops. The bottom line results of this research allude to Construction Manager at Risk (CM-R) as the more viable option. Ultimately, the research provides a comparative analysis of the results obtained from both models and tests the veracity of the models by confirming their utility and applicability outside the universe formed by the case study projects.