Delays and cost overruns are common problems in the construction industry. Despite extensive research on their causes and mitigations, these problems persist, suggesting that the challenge is not identifying possible mitigation measures, but rather selecting the optimal combination. Recently, a decision-support tool, Mit-C, was developed to help find the optimal strategy by combining a Monte Carlo simulation with mathematical optimization. However, the tool has a relevant limitation: it does not consider the resources required by the mitigation measures, potentially leading to unrealistic or infeasible solutions.
Consequently, this master thesis addresses this limitation by further developing the project management decision-support tool, Mit-C, through the inclusion of the resources availability and demand required by the mitigation measures. For this purpose, the addition of a significant number of variables and constraints into the original mathematical model was needed, increasing the computational time of the program but improving the realism of the results. The altered model was then validated using a case study: the construction of a warehouse. The tool was used with both simplified and detailed project data to test its performance.
The results demonstrated that including resource constraints has a significant effect on the optimal mitigation strategy and leads to a lower and more realistic probability of finishing the project on time. This difference is more noticeable when using detailed data. It is therefore concluded that while the resource-constrained model produces more pessimistic results, it offers a significantly more realistic, reliable and therefore valuable decision-making tool for project managers.