Subcontracting is a very common practice in the construction industry. Subcontractors perform the actual production work in most of the construction projects. Selection of subcontractors for the work packages is generally made based on a deterministic manner. However, a real-life construction project is full of risks and uncertainties. The objective of this study is to account and incorporate risks into the subcontractor selection problem by performing Monte-Carlo simulation method in order to make better decisions under uncertainty. This study is mainly based on the findings of a previous study. In the previous study, the selection of subcontractors for all work packages in the studied real-life construction project was made using genetic algorithm technique considering time, cost, and quality performances, and six different optimal subcontractor combinations and their related three deterministic project performances were identified. In this study, risk analysis of these subcontractor combinations is performed and the probabilistic distributions of time, cost, and quality performances are obtained. The probabilistic solutions of optimal subcontractor combinations are used to make a decision in a more realistic way.
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