"Coverage under a disruption" refers to the extent to which a system, network, or service can continue to operate effectively in the face of disruptions. This concept is relevant in fields such as telecommunications, transportation, and emergency services, where maintaining uninterrupted service is crucial. This paper presents a two-part decision-support framework integrating Voronoi-based spatial analysis with fairness-oriented performance metrics to enhance coverage analysis. First, we introduce a disruption impact assessment framework that evaluates the criticality of existing facilities by quantifying changes in coverage and fairness when individual facilities become non-functional. This framework supports resilience analysis by identifying facilities whose failure leads to disproportionate coverage degradation. Second, we propose a bi-objective optimization model for coverage planning, aimed at identifying cost-effective and spatially fair strategies for expanding facilities under different system configurations, including normal and disrupted operating conditions. Spatial fairness is quantified using the Kolm–Pollak equally-distributed equivalent (EDE) measure applied to the distribution of distances between demand points and assigned facilities . The resulting Pareto-optimal frontier enables decision-makers to explore trade-offs between investment cost and spatial fairness. In particular, by minimizing the fairness index of the distances from demand points to their respective facilities, we aim to achieve a more equitable distribution of coverage across the geographic area. A case study based on the electric power network of Shelby County, Tennessee illustrates the proposed framework and its applicability to infrastructure coverage planning under disruption.
Rocco et al. (Wed,) studied this question.