US-Mexico Border: Building a Smarter Wall through Strategic Security Measure Allocation

Abstract

We offer a quantitative construct for optimizing security measure investments, to achieve the most costeffective deterrence and detection capabilities for the U.S. Customs and Border Patrol (CBP). We constructed a large-scale multiple-objective portfolio optimization integer program that rapidly returns good Pareto optimal results. The model incorporates the utility of each measure and the probability of success, along with multiple other objectives. To the best of our knowledge, our work presents the first mathematical model that optimizes security strategies for the CBP and is the first to introduce a utility factor to emphasize deterrence and detection impact. The model accommodates different resources, constraints, and various types of objectives. The solution methodologies being put in place are complex, current state-of-the-art, and very effective.