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.

Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals

“…So we approximate g 5 by g 5c (x 1 , x 3 ) as defined in Equation 5. After marginalization of X 1 , we get g 6c (x 3 ), which is a 6-piece, 3-degree MOP approximation of the PDF of X 3 defined on the domain (3,27). It takes approximately 1.31 s to compute g 6c (·).…”

“…The exact marginal distribution of X 3 is N (15,13). A plot of g 6c overlaid on the plot of the PDF of N(15, 13) truncated to (3,27) is shown in Figure 4 (left).…”

Measuring cash flow and overdraft for fuzzy project networks with overlapping activities

“…The main challenge is to reduce project duration while evaluating the degree of uncertainty. Schedule compression involves uncertainties, which may arise from variability and ambiguity (Khodakarami et al 2007). Schedule compression either increases risks or requires additional resources, so risk must be taken into account (Shankar et al 2011).…”

Managing Systems Engineering Projects with Uncertain Requirements

“…Fortunately, there already exist well-established tools for making decisions optimally once uncertainty has been recognized (Ding and Zhu, 2015;Elmaghraby, 2003; Morgan and Henrion, 1992; Chapman and Ward, 1997;Clemen, 1996;Khodakarami, Fenton and Neil, 2007;Pich, Loch and DeMeyer, 2002). Many of these approaches, e.g., decision trees, influence diagrams, are already recognized in both the Systems Engineering Body of Knowledge (SEBOK, BKCASE 2019) and the Project Management Body of Knowledge (PMBOK, Project Management Institute, 2013).…”