UVA researchers build robust interdiction model to disrupt drug networks under uncertainty
New simulation-driven method optimizes limited surveillance budgets to cut narcotics flow significantly.
Gupta, Marathe, and Vullikanti introduce a robust network flow interdiction framework for counter-narcotics. Using a limited real-world dataset, they generate an ensemble of plausible network realizations and develop an integer linear program to identify optimal interdiction actions. Their robust strategy achieves near-optimal flow reductions across scenarios and remains stable under structural uncertainty. Even modest budgets yield significant flow reductions, providing a principled way to maximize interdiction investments despite data limitations.
- Uses a limited real-world dataset to generate an ensemble of plausible trafficking network realizations via simulation and mathematical programming.
- Formulates a robust network flow interdiction problem as an integer linear program, achieving near-optimal flow reductions across uncertain scenarios.
- Demonstrates that even modest interdiction budgets yield significant flow reductions when using the robust strategy.
Why It Matters
This method helps law enforcement and policy makers allocate scarce interdiction resources effectively despite incomplete intelligence on trafficking networks.