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

合作研究：打击非法贩毒执法行动的动态资源分配模型

• 批准号: 1266056
• 负责人: Chase Rainwater
• 金额: $ 14.76万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1266056&HistoricalAwards=false
• 关键词: Collaborative; Research; Dynamic; Resource; Allocation

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.

这项合作研究资助调查了新的优化模型，用于确定执法部门的资源分配决策，以阻断非法毒品网络。 这些决定的重点是安排执法活动，以便成功地监测，瞄准和逮捕非法毒品网络中的罪犯。 这些决定将使用一种新的动态阻断框架和马尔可夫决策过程框架建模。 拟议的模式特别考虑到需要平衡情报行动和物理拦截，前者有助于识别新的罪犯并对已知的高级罪犯立案，后者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的识别、瞄准和逮捕罪犯相关联的调度决策的结构来求解所提出的模型。这项研究的结果将提供工具，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。 这包括更好地了解：（一）如何利用国家机构的资源（例如，（二）如何最好地使目前受执法部门监视的犯罪分子的组合多样化，以及（三）犯罪分子之间的信息流动如何影响阻截工作。 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。 该项目将吸引大一工程专业的学生参与前沿研究，并通过在能够利用基于分解的解决方案方法的超级计算资源上实施专门的算法来发展他们的高性能计算技能。