Current decision-support frameworks to assist mitigation planning do not include uncertainty and complexity of network failures, either one or both. To close this research gap, this thesis walks through a demonstration of the importance of including uncertainty in the decision analysis to later propose a novel methodology that employs simulation data that encapsulates both uncertainty and complexity of failures modeled by domain experts. Thus, this work is divided in two parts.

The first part of this work examines how component importance measures fail to give the necessary intuition for mitigation planning in the light of uncertainty. The analysis is assisted by a novel component importance measure called probabilistic delta centrality that demonstrates how previously neglected stochastic considerations change decisions suggested.

In the second part, a new paradigm for stochastic network mitigation is proposed. The approach leverages realizations from scenario event simulations to develop a probabilistic framework that supports constrained decision making. This scenario event simulation framework is capable of comprising component fragilities, correlation among random variables, and other physical aspects that affect component failure probabilities. On the top of that, a statistical learning model is built to enable a rapid estimation of post-disruption impact, which permits a metaheuristic to intelligently explore feasible discrete enhancements from mitigation strategies. The search for near-optimal solutions can be restricted by limited resources and potential political, social, and safety limitations. Two examples are presented to exhibit how this method provides detailed information for mitigation. The level of complexity embedded in search along with its detailed solutions are pioneering in network mitigation planning.