Project Scheduling: Improved Approach to Incorporate Uncertainty Using Bayesian Networks

Abstract

Project scheduling inevitably involves uncertainty. The basic inputs (i.e., time, cost, and resources for each activity) are not deterministic and are affected by various sources of uncertainty. Moreover, there is a causal relationship between these uncertainty sources and project parameters; this causality is not modeled in current state-of-the-art project planning techniques (such as simulation techniques). This paper introduces an approach, using Bayesian network modeling, that addresses both uncertainty and causality in project scheduling. Bayesian networks have been widely used in a range of decision-support applications, but the application to project management is novel. The model presented empowers the traditional critical path method (CPM) to handle uncertainty and also provides explanatory analysis to elicit, represent, and manage different sources of uncertainty in project planning.