Human Trafficking Interdiction with Decision Dependent Success
This paper presents a bi-level network interdiction model to increase the effectiveness of attempting to disrupt a human trafficking network under a resource constrained environment. To model the behavior of the trafficker, we present a new interpretation of the traditional maximum flow network problem in which the arc capacity parameter serves as a proxy for the trafficker's desirability to travel along segments of the network. The objective for the anti-human trafficking stakeholder is to invest resources in detection and intervention efforts throughout the network in a manner that minimizes the trafficker's expected maximum desirability of operating on the network. Interdictions are binary, and their effects are stochastic (i.e., there is a positive probability that a disruption attempt is unsuccessful). A multi-stage version of the model is presented, which incorporates the effect of interdictions becoming more or less successful over time. Model insights are presented using a case study of the road network in the Eastern Development Region of Nepal. Multiple problem instances are solved with a genetic algorithm that uses a pseudo-utility ratio for the repair operation. Observations regarding the impact of probabilistic interdiction success and the implications it has for optimal policies to disrupt a human trafficking network with limited resources are discussed.