A new approximation algorithm for obtaining the probability distribution function for project completion time
This paper focuses on the application of the techniques of discretization to obtain an approximated probability density function (pdf) for the completion time of large-size projects, in which we allow any type of pdf for the duration of activities. In this study, we improve the techniques of discretization in the following two ways: first, we propose to replace the max operation with an approximation procedure to save significant computational loading; and second, to reduce the error from assuming independence between paths using a simple heuristic rule. To evaluate the performance of our proposed algorithm, we randomly generated 20 sets of 100-node instances in our numerical experiments. Taking the results from a Monte Carlo simulation using 20,000 samples as a benchmark, we demonstrate that the proposed algorithm significantly outperforms the PERT model and Dodins [B.M. Dodin, Approximating the distribution function in stochastic networks, Comput. Oper. Res. 12 (3) (1985) 251264] algorithm in both the running time and the precision aspects.
approximation probability distribution function completion time discretization stochastic activity networks