Adding the Balance of Disruption Impact to System Interdiction Models

Interdiction strategies in network systems traditionally focus on maximizing disruption while minimizing the cost of the interdiction. However, such a strategy often overlooks how the impact is distributed across the network, which can be strategically significant. We explore a multi-objective framework for network interdiction that incorporates not only the typical cost and impact objectives common in network interdiction models but also the balance or distribution of the impact, as measured by the Gini inequality index. This work highlights the trade-offs involved in choosing between cost-efficiency, impact magnitude, and the balance of the magnitude across the network, offering a more nuanced approach to network interdiction strategies.