Quantifying the risk of project delays with a genetic algorithm

Abstract

Of interest in project management is the (i) quantification of the risk associated with project performance and the (ii) identification of the project tasks that contribute most to that risk. Risk in this work addresses delays in project completion. The tasks and precendences are represented with nodes and links, respectively, in a project network whose tasks (i) have stochastic completion times that (ii) are subject to disruptions. An optimization problem is developed to maximize project delay subject to particular stochastic task disruptions, and a genetic algorithm is introduce to identify the critical tasks which lead to the maximum risk of project delay. A small project of 40 tasks and large project of 800 tasks are analyzed. Primary conclusions are (i) that critical tasks need not necessarily be on the critical path if they are subject to considerable uncertainty, and (ii) that project complexity (network topology) matters more in the performance of the algorithm than the number of tasks (network size). In fact, the genetic algorithm solution works well for large-scale projects whose schedules cannot be resolved with conventional techniques. Focus is given to the performance of the algorithm for this project risk context.