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We consider a bilevel network interdiction problem where the follower aims to maximize the amount of flow from the source node to the sink node, and the leader aims to minimize the number of arcs from a critical set that have positive flow on them, that is, active arcs, in the maximum flow solution obtained by the follower. This problem is motivated by an application in human trafficking disruption. We consider both the optimistic and pessimistic variants of this bilevel optimization problem and develop their respective single-level reformulations. We present a tailored solution method to the pessimistic problem, which solves the problem to optimality for one practically important class of networks. Through computational experiments on randomly generated layered network instances, we show the effectiveness of the proposed methods and demonstrate that the tailored method is orders of magnitude faster than existing approaches in the literature. We also conduct computational experiments on randomly generated test instances inspired by domestic human trafficking networks and draw domain-specific insights. 

Abstract In this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters. 

Human trafficking is a serious crime and violation of human rights that results in numerous harms. Although the phenomenon is not new, scholarship on the issue has grown substantially since the first legal framework was passed in 2000. However, the existing literature has been criticized for its skewed focus on victims, among other things. The dearth of information on traffickers and their operations limits our ability to reduce or prevent perpetration. The current study presents a comprehensive and critical review of the existing literature focused on traffickers to synthesize what is already known and highlight the key gaps. Twenty-nine articles met the inclusion criteria of (1) focusing on traffickers and their operations and (2) relying on data either directly from traffickers or sources that contained detailed information about criminal cases against traffickers. We used an iterative process to identify relevant studies, which included collecting articles of which we were already familiar or were identified in existing reviews, searching their reference lists, and conducting cited-by searches until saturation was reached. Topics found in the extant literature included: characteristics of traffickers, relationships between traffickers and victims, organizational characteristics and networks, operations, connections with other crimes, motivations, perceptions of behavior, and risks associated with trafficking. It concludes with recommendations for future research and a discussion of how bridging gaps in the literature could support more rigorous mathematical modeling that is needed to identify and assess promising perpetration prevention and intervention strategies. 

In this paper, we study an integrated hurricane relief logistics and evacuation planning (HRLEP) problem. We propose stochastic optimization models and methods that integrate the hurricane relief item pre-positioning problem and the hurricane evacuation planning problem, which are often treated as separate problems in the literature, by incorporating the forecast information as well as the forecast errors (FE). Specifically, we fit historical FE data into an auto-regressive model of order one (AR-1), from which we generate FE realizations to create evacuation demand scenarios. We compare a static decision policy based on the proposed stochastic optimization model with a dynamic policy obtained by applying this model in a rolling-horizon (RH) procedure. We conduct a preliminary numerical experiment based on real-world data to validate the value of stochastic optimization and the value of the dynamic policy based on the RH procedure.