Strategic and analytics-driven inspection operations for critical infrastructure resilience

Abstract

Resilience of infrastructure networks is a key requirement for a functioning modern society. These networks work continuously to enable the delivery of critical services such as water, natural gas, and transportation. However, recent natural disasters and cyber-physical security attacks have demonstrated that the lack of effective failure detection and identification capabilities is one of the main contributors of economic losses and safety risks faced by service utilities. This thesis focuses on both strategic and operational aspects of inspection processes for large-scale infrastructure networks, with the goal of improving their resilience to reliability and security failures. We address three combinatorial problems: (i) Strategic inspection for detecting adversarial failures; (ii) Strategic interdiction of malicious network flows; (iii) Analytics-driven inspection for localizing post-disaster failures.

We exploit the structural properties of these problems to develop new and practically relevant solutions for inspection of large-scale networks, along with approximation guarantees. Firstly, we address the question of determining a randomized inspection strategy with minimum number of detectors that ensures a target detection performance against multiple adversarial failures in the network. This question can be formulated as a mathematical program with constraints involving the Nash equilibria of a large strategic game. We solve this inspection problem with a novel approach that relies on the submodularity of the detection model and solutions of minimum set cover and maximum set packing problems. Secondly, we consider a generic network security game between a routing entity that sends its flow through the network, and an interdictor who simultaneously interdicts multiple edges.

By proving the existence of a probability distribution on a partially ordered set that satisfies a set of constraints, we show that the equilibrium properties of the game can be described using primal and dual solutions of a minimum-cost circulation problem. Our analysis provides a new characterization of the critical network components in strategic flow interdiction problems. Finally, we develop an analytics-driven approach for localizing failures under uncertainty. We utilize the information provided by failure prediction models to calibrate the generic formulation of a team orienteering problem with stochastic rewards and service times. We derive a compact mixed-integer programming formulation of the problem that computes an optimal a-priori routing of the inspection teams. Using the data collected by a major gas utility after an earthquake, we demonstrate the value of predictive analytics for improving their response operations.