arXiv:2605.26763v1 Announce Type: new Abstract: The Maximal Covering Location-Interdiction Problem (MCLIP) is a classic bi-level optimization problem, which is fundamental to resilient infrastructure planning yet remains computationally intractable. Specifically, the upper level determines facility locations to maximize coverage, while the lower level executes worst-case interdiction to minimize the coverage. The strong coupling between the upper and lower levels, combined with their respective high combinatorial complexity, renders traditional methods ineffective. To bridge this gap, we propo
The increasing complexity and computational demands of critical infrastructure planning, coupled with advancements in AI and optimization techniques, are driving the need for more robust solutions.
This research addresses a critical challenge in resilient infrastructure planning by proposing a novel, more effective method for optimizing facility placement against worst-case scenarios, impacting national security and economic stability.
Traditional intractable problems in infrastructure resilience may become more solvable through advanced adversarial AI training and bi-level optimization, potentially leading to more robust designs.
- · Critical infrastructure planners
- · National security organizations
- · AI/Optimization researchers
- · Governments
- · Adversaries targeting infrastructure
- · Traditional optimization methods
Improved resilience and strategic planning for critical national infrastructure through advanced AI-driven optimization.
Reduced vulnerability of essential services (e.g., energy grids, communication networks) to deliberate attacks or natural disasters.
Potential for broader adoption of AI-based adversarial training techniques in other complex strategic planning domains beyond infrastructure.
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Read at arXiv cs.LG