A DYNAMIC SAMPLING TECHNIQUE FOR THE SIMULATION OF PROBABILISTIC AND GENERALIZED ACTIVITY NETWORKS

Most probabilistic activity networks (e.g. PERT) of any reasonable siz e are practically impossible to analyse mathematically in an acceptabl e time. This problem is augmented when stochastic branching is introdu ced to form generalized activity networks. For this reason simulation has proved to be one of tbe more popular and 'accurate' techniques ava ilable for network attribute analysis. In this paper a dynamic samplin g technique is introduced that improves on the standard simulation app roach used in popular project management software tools. A comparison is also made between the simulation requirements of standard probabili stic activity networks and a finite sample set of generalized activity networks in which activities are assigned either dependent or indepen dent probability generations.