Activity networks are a powerful tool for modeling and analysis in project management, and in many other applications, such as circuit design and parallel computing. An activity network can be represented by a directed acyclic graph with one source node and one sink node. The directed arcs between nodes in an activity network represent the precedence relationships between different activities in the project. In a stochastic activity network (SAN), the arc lengths are random variables.

This dissertation studies stochastic gradient estimators for SANs using Monte Carlo simulation, and the application of stochastic gradient estimators to network optimization problems. A new algorithm called Threshold Arc Criticality (TAC) for estimating the arc criticalities of stochastic activity networks is proposed. TAC is based on the following result: given the length of all arcs in a SAN except for the one arc of interest, that arc is on the critical path (longest path) if and only if its length is greater than a threshold. By applying Infinitesimal Perturbation Analysis (IPA) to TAC, an unbiased estimator of the derivative of the arc criticalities with respect to parameters of arc length distributions can be derived. The stochastic derivative estimator can be used for sensitivity analysis of arc criticalities via simulation.

Using TAC, a new IPA gradient estimator of the first and second moments of project completion time (PCT) is proposed. Combining the new PCT stochastic gradient estimator with a Taylor series approximation, a functional estimation procedure for estimating the change in PCT moments caused by a large perturbation in an activity duration's distribution parameter is proposed and applied to optimization problems involving time-cost tradeoffs.

In activity networks, crashing an activity means reducing the activity's duration (deterministic or stochastic) by a given percentage with an associated cost. A crashing plan of a project aims to shorten the PCT by reducing the duration of a set of activities under a limited budget. A disruption is an event that occurs at an uncertain time. Examples of disruptions are natural disasters, electrical outages, labor strikes, etc. For an activity network, a disruption may cause delays in unfinished activities. Previous work formulates finding the optimal crashing plan of an activity network under a single disruption as a two-stage stochastic mixed integer programming problem and applies a sample average approximation technique for finding the optimal solution. In this thesis, a new stochastic gradient estimator is derived and a gradient-based simulation optimization algorithm is applied to the problem of optimizing crashing under disruption.