Integrated range estimating and probabilistic scheduling of construction projects

One of the major goals of the construction industry today is the quantification and minimization of the risk associated with the undertaking of construction projects. When specifically considering the planning of these projects, one way to control risk is through the development of reliable project cost estimates and schedules. Two techniques available for achieving these goals are range estimating and probabilistic scheduling. This dissertation looked at the integration of these techniques as a means of further controlling the risk inherent in the undertaking of construction projects. The research had two primary objectives. The first was the development of a model to combine the results of range estimating and probabilistic scheduling in order to accurately quantify the relationship existing between these results. Having the ability to accurately quantify this relationship would enable the selection of high confidence level values for the project cost estimate and schedule, while simultaneously accounting for the existing relationship between them. The second objective was to develop a technique to perform probabilistic least-cost scheduling. Least-cost scheduling (a deterministic technique) is used to determine the shortest duration in which a project can be completed at a minimum cost. However, it can be shown that there is no absolute provable least-cost schedule because the activity duration and cost values for real world projects cannot be known exactly prior to completing those projects. Thus, a probabilistic approach to performing this exercise will allow better quantification and minimization of the risk associated with such an undertaking by accounting for the variability in activity cost and duration. Two new planning tools were developed as a result of the research. These are called “Multiple Simulation Analysis Technique (MSAT)” and “Probabilistic Optimal-Cost Scheduling (POCS)”. MSAT provides a means of relating the results of range estimating and probabilistic scheduling such that values for both the project cost estimate and schedule, each having a statistically high level of confidence, can be determined. POCS (Probabilistic optimal-cost scheduling) allows for the selection of the compressed project duration that has the highest likelihood of occurring. Additionally, when combined with MSAT, it allows a cost estimate value, having a low probability of being exceeded, to be selected. This cost estimate value is also directly related to the most likely compressed schedule duration. Finally, the application of a new concept called “Cost-Schedule Possibility Frontier (CSPF)” is presented as one possible application of MSAT and POCS. This concept allows the selection of feasible cost estimate and schedule alternatives by simultaneously considering the possible combination of cost estimate, schedule, and confidence levels values that are possible for a particular project.