On simulating nondeterministic stochastic activity networks

TL;DR

This paper introduces NonDeterministic Stochastic Activity Networks (NDSANs), a flexible simulation model for complex processes with stochastic durations and nondeterministic choices, along with a recursive simulation algorithm and real-world case studies.

Contribution

It presents a novel NDSAN model incorporating nondeterminism and stochasticity, and develops a recursive simulation algorithm for approximating completion time distributions.

Findings

The simulation algorithm effectively approximates completion time distributions.

NDSANs can model complex real-world processes with nondeterministic behaviors.

Case studies demonstrate practical applicability and accuracy.

Abstract

In this work we deal with a mechanism for process simulation called a NonDeterministic Stochastic Activity Network (NDSAN). An NDSAN consists basically of a set of activities along with precedence relations involving these activities, which determine their order of execution. Activity durations are stochastic, given by continuous, nonnegative random variables. The nondeterministic behavior of an NDSAN is based on two additional possibilities: (i) by associating choice probabilities with groups of activities, some branches of execution may not be taken; (ii) by allowing iterated executions of groups of activities according to predetermined probabilities, the number of times an activity must be executed is not determined a priori. These properties lead to a rich variety of activity networks, capable of modeling many real situations in process engineering, project design, and troubleshooting. We describe a recursive simulation algorithm for NDSANs, whose repeated execution produces a close approximation to the probability distribution of the completion time of the entire network. We also report on real-world case studies.