Optimizing employment of search platforms to counter self-propelled semi-submersibles

Abstract

Self-Propelled Semi-Submersibles now transport an estimated 75% of cocaine originating from Colombia and headed for the United States. There are several types of search platforms (i.e., units to detect, classify, and interdict) being employed by the Joint Interagency Task Force South to combat the semi-submersibles. We use a defender-attacker optimization model to maximize the defender's probability of successful detection and classification of the semisubmersible through the advantageous disposition of these search platforms against an intelligent attacker operating the semi-submersible. We assume the attacker has imperfect knowledge of defender platform disposition but is aware that there are defenders that must be avoided. Given this assumption, the solution to the defender-attacker model is a mixed (i.e., probabilistic) strategy for the defender and a least-risk path for the attacker. We demonstrate our defender-attacker model with both an Eastern Pacific and a Caribbean scenario using five representative search platform types whose detection and classification performance vary by platform, and by geography. In each of these cases, we find that our model prescribes a face-valid defensive plan; defenders take advantage of geography by positioning at chokepoints in constrained waterways, and they provide coverage near attacker origins and destinations in the less geographically-constrained scenarios.