Abstract
Uncertainties among time and cost items are important to consider because of their immense impacts on the overall performance of any project. In this paper, a project’s cost and time elements are improved by crashing and a Bayesian Belief Network (BBN) model is developed to handle the uncertainties. BBN provides a framework for presenting causal relationships and enables probabilistic inference among a set of variables. The approach used in this paper can explicitly quantify the cost amount and time taken in various circumstances considering all the relevant uncertainties among the elements. The total cost and time of any project can be projected in this approach very effectively with certain probabilities. Sensitivity analysis is also performed to find the most important tasks of the project in terms of cost and time. The capabilities of the proposed approach are explained by an empirical example and the results of the proposed method are compared with those of other popular project management tools.