Interdiction problems arise in a number of application areas, including global security, supply chains, and critical infrastructure protection - the goal is inhibit the movement of goods, people or information. An area of particular interest is counter-narcotics, where nodes or edges in a network are placed under surveillance or blocked to minimize the flow of illicit drugs from source to the destination. A fundamental challenge in this narco-traffic interdiction is data scarcity: available datasets are limited by the very nature of the problem and provide only partial and uncertain views of trafficking networks. Thus, developing robust interdiction methods that take this inherent lack of information is critical. In this paper we initiate the study of network flow interdiction problems under network uncertainty. First, using a limited real-world dataset, we generate an ensemble of plausible network realizations representing alternative trafficking scenarios. The method combines simulations with mathematical programming techniques to generate network ensembles that are consistent with the observed data. Second, we formulate the robust network flow interdiction problem and develop an integer linear program to solve the problem. We evaluate the optimal interdiction strategy and obtain the residual flows over the scenarios. Our analysis reveals that even modest budgets can yield significant flow reductions. However, optimal solutions vary substantially across scenarios, motivating the need for robust solutions. We show that the robust strategy achieves near-optimal performance across all near-real world realizations while remaining stable under structural uncertainty. This simulation-driven approach provides a principled basis for policy analysis and supports maximizing the return on interdiction investments in uncertain, data-limited environments.

翻译：阻断问题出现在众多应用领域中，包括全球安全、供应链与关键基础设施保护——其目标是阻止商品、人员或信息的流动。一个特别值得关注的领域是反毒行动，在此类场景中，对网络中的节点或边进行监控或封锁，旨在最小化非法毒品从来源地到目的地的流动。毒品贩运阻断面临的一个基本挑战是数据稀缺：受问题本质所限，可用数据集仅能提供贩毒网络的部分且不确定的视图。因此，开发能够应对这种固有信息匮乏的鲁棒阻断方法至关重要。本文首次研究了网络不确定性条件下的网络流阻断问题。首先，利用有限的真实世界数据集，我们生成了一个包含多种可信网络实现（代表不同的贩运情景）的集成集。该方法结合了模拟与数学规划技术，以生成与观测数据一致的网络集成集。其次，我们形式化了鲁棒网络流阻断问题，并开发了一个整数线性规划来求解该问题。我们评估了最优阻断策略，并获得了各情景下的残余流量。分析表明，即使是适度的预算也能带来显著的流量削减。然而，最优解在不同情景间差异显著，这催生了对鲁棒解的需求。我们证明，鲁棒策略在所有接近真实世界的实现中均能达到接近最优的性能，同时能在结构不确定性下保持稳定。这种基于模拟驱动的方法为政策分析提供了原则性基础，并支持在不确定性高、数据有限的环境中最大化阻断投资的回报。