Collaborative Research: Dynamic Resource Allocation Models for Law Enforcement Operations against Illegal Drug Trafficking

这项合作研究资助旨在研究新的优化模型，以确定执法部门拦截非法毒品网络的资源分配决策。这些决定的重点是安排执法活动，以便成功地监视、瞄准和逮捕非法毒品网络中的罪犯。这些决策将使用新的动态拦截框架和马尔可夫决策过程框架进行建模。拟议的模式特别纳入了平衡情报行动的需要，情报行动有助于识别新的罪犯并建立针对已知高级罪犯的案件，而物理封锁则有助于将罪犯从网络中清除。将提出专门的基于约束规划的优化算法，并将其集成到传统的整数规划技术中，以便利用与整个网络中识别、瞄准和逮捕罪犯相关的调度决策结构来解决所提出的模型。如果取得成功，这项研究的结果将提供有助于更好地了解如何利用稀缺的执法资源来减少非法贩毒的工具。这包括更好地了解：(i)如何利用各个城市的国家机构（例如，缉毒局）的资源与地方执法部门合作，（ii）如何最好地使目前受到执法部门监视的犯罪分子组合多样化，以及（iii）犯罪分子之间的信息流动如何影响拦截工作。这一政策驱动分析的结果将通过与当地执法机构的合作与从业人员分享。该项目将通过在超级计算资源上实现能够利用基于分解的解决方案方法的专门算法，让工程专业的大一学生参与前沿研究，并发展他们的高性能计算技能。

This collaborative research grant investigates new optimization models for determining resource allocation decisions of law enforcement to interdict illegal drug networks. These decisions focus on scheduling the activities of law enforcement in order to successfully monitor, target, and arrest criminals in an illegal drug network. These decisions will be modeled using a novel dynamic interdiction framework and a Markov Decision Process framework. The proposed models specifically incorporate the need to balance intelligence operations, which help to identify new criminals and build cases against known high-ranking criminals, and physical interdictions, which remove criminals from the network. Specialized constraint programming-based optimization algorithms will be proposed and integrated into traditional integer programming techniques in order to exploit the structure of the scheduling decisions associated with identifying, targeting, and arresting criminals throughout the network to solve the proposed models.If successful, the results of this research will provide tools that help to better understand how to utilize scarce law enforcement resources to mitigate illegal drug trafficking. This includes a better understanding of: (i) how to utilize resources of national agencies (e.g., the Drug Enforcement Agency) across various cities in partnering with local law enforcement, (ii) how to best diversify the portfolio of criminals that are currently under surveillance by law enforcement, and (iii) how the flow of information between criminals impacts interdiction efforts. The results of this policy-driven analysis will be shared with practitioners through collaborations with local law enforcement agencies. The project will engage freshmen engineering students in cutting-edge research and develop their high-performance computing skills by implementing the specialized algorithms on supercomputing resources capable of exploiting decomposition-based solution approaches.