Optimal interdiction of an adaptive smuggler
Bessman, Daniel L.
Royset, Johannes O.
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Counterdrug operations are of national interest to the U.S. and our allies because the illegal production and trafficking of drugs threatens U.S. national security and undermines security and stability in Latin America. Since law enforcement tasked with counterdrug operations is not given enough platforms to search every location at all times, they must decide how to employ their scarce platforms. To assist law enforcement, we develop a defender-attacker optimization model that utilizes actionable intelligence to coordinate the simultaneous, cooperative disposition of law enforcement platforms in an optimal manner against a smuggler. The model utilizes stochastic dynamic programming to represent an intelligent smuggler, who has the ability to reevaluate his remaining path at decision points along his journey, based on knowledge obtained en route and expectations previously derived. The model employs Global Benders' Decomposition to determine the optimal placement of three different types of law enforcement platforms simultaneously prosecuting one of three possible types of smuggler. We show that such computations cannot be performed fast enough to be used in a tactical decision aid, since they typically require in excess of two hours. Upon further analysis using our model, we determine a large number of defender missions do not have a substantial impact on the attacker's risk. Based on the results of our model, we believe further algorithmic development is needed for implementation into a tactical decision aid to assist in counter drug operations.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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