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). We present a multi-stage version of the model, which incorporates the effect of interdictions becoming more or less successful over time. Using a genetic algorithm that uses a pseudo-utility ratio for the repair operation, we solve multiple problem instances for a case study of the road network in the Eastern Development Region of Nepal and multiple grid networks. We then discuss 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. • Combines human trafficking expertise with network interdiction models. • Network interdiction model with decision-dependent uncertainty and multiple stages. • Computational studies demonstrate the genetic algorithm's performance. • Provides two-stage policy insights for multi-stage decision-dependent probabilities. [ABSTRACT FROM AUTHOR]