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

点击翻译按钮获取中文摘要

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.