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One of the major challenges associated with applying operations research (OR) models to disrupting human trafficking networks is the limited amount of reliable data sources readily available for public use, since operations are intentionally hidden to prevent detection, and data from known operations are often incomplete. To help address this data gap, we propose a network generator for domestic sex trafficking networks by integrating OR concepts and qualitative research. Multiple sources regarding sex trafficking in the upper Midwest of the United States have been triangulated to ensure that networks produced by the generator are realistic, including law enforcement case file analysis, interviews with domain experts, and a survivor-centered advisory group with first-hand knowledge of sex trafficking. The output models the relationships between traffickers, so-called "bottoms", and victims. This generator allows operations researchers to access realistic sex trafficking network structures in a responsible manner that does not disclose identifiable details of the people involved. We demonstrate the use of output networks in exploring policy recommendations from max flow network interdiction with restructuring. To do so, we propose a novel conceptualization of flow as the ability of a trafficker to control their victims. Our results show the importance of understanding how sex traffickers react to disruptions, especially in terms of recruiting new victims.

We focus on organizational structures in covert networks, such as criminal or terrorist networks. Their members engage in illegal activities and attempt to hide their association and interactions with these networks. Hence, data about such networks are incomplete. We introduce a novel method of rewiring covert networks parameterized by the edge connectivity standard deviation. The generated networks are statistically similar to themselves and to the original network. The higher-level organizational structures are modeled as a multi-layer network while the lowest level uses the Stochastic Block Model.Such synthetic networks provide alternative structures for data about the original network. Using them, analysts can find structures that are frequent, therefore stable under perturbations. Another application is to anonymize generated networks and use them for testing new software developed in open research facilities. The results indicate that modeling edge structure and the hierarchy together is essential for generating networks that are statistically similar but not identical to each other or the original network. In experiments, we generate many synthetic networks from two covert networks. Only a few structures of synthetics networks repeat, with the most stable ones shared by 18% of all synthetic networks making them strong candidates for the ground truth structure.

We consider a new class of multi-period network interdiction problems, where interdiction and restructuring decisions are decided upon before the network is operated and implemented throughout the time horizon. We discuss how we apply this new problem to disrupting domestic sex trafficking networks, and introduce a variant where a second cooperating attacker has the ability to interdict victims and prevent the recruitment of prospective victims. This problem is modeled as a bilevel mixed integer linear program (BMILP), and is solved using column-and-constraint generation with partial information. We also simplify the BMILP when all interdictions are implemented before the network is operated. Modeling-based augmentations are proposed to significantly improve the solution time in a majority of instances tested. We apply our method to synthetic domestic sex trafficking networks, and discuss policy implications from our model. In particular, we show how preventing the recruitment of prospective victims may be as essential to disrupting sex trafficking as interdicting existing participants.

We consider decentralized scheduling with precedence constraints (DSwP) games where a set of players are scheduling jobs to complete while a set of hard precedence constraints link the jobs of the players. The impact of these decentralized scheduling efforts can be quantified by examining the price of anarchy (PoA) and the price of stability (PoS), which measure, respectively, the ratio of social costs of the worst and best equilibrium solutions with the optimal (centralized) social costs. We demonstrate that equilibrium solutions do not, in general, exist for this class of games and prove their existence for certain classes of games. Upper bounds for the PoA and the PoS are proven to depend linearly on the sum of the processing times of the jobs across the players and we present an example to demonstrate that this bound cannot be tightened beyond the sum of the processing times divided by two.

We consider optimization problems related to the scheduling of Multi-Echelon Assembly Supply Chain (MEASC) networks that find application in the recovery from large-scale disruptive events. Each ma...

This research details the creation of a large-scale optimization approach for solving an application of a multi-period bilevel network interdiction problem (NIP). In this class of multi-period NIP, interdiction activities must be scheduled to minimize the cumulative maximum flow over a finite time horizon. A logic-based decomposition (LBD) approach is proposed that utilizes constraint programming to exploit the scheduling nature of this multi-period NIP. Computational results–comparing solutions obtained using the proposed approach versus traditional mixed-integer programming approach–suggest that the LBD approach is more efficient in finding solutions for medium to large problem instances.

We formally present the problem of scheduling tasks with effectiveness precedence relationships in order to achieve the minimum total weighted completion time. We provide the problem formulation and define the scope of the problem considered. We present computational complexity results for this problem and an approximation algorithm for it. We prove the theoretical performance of our algorithm and demonstrate its efficiency and practical performance through computational testing, which includes a comparison to the optimal results obtained with an integer programming formulation.

This paper seeks to optimize the pre-event mitigation and post-event restoration decisions for disruptive events impacting supply chain systems, while specifically accounting for how unmet demand impacts the customers within the supply chain. The pre-event mitigation strategies will prepare the supply chain for various potential disruptions and then the post-event restoration decisions are the response of it to a specific disruption. In the context of the restoration, we consider customer behaviors which require fulfillment of demand at a certain time after the disruption and those which require that a cumulative percentage of their demand must be met on-time after the disruption or, otherwise, the customer will leave the system. In addition, we then require that the system backorders unmet demand in order to maintain the customer. We build two-stage stochastic programming models to increase the resiliency of single-product, single-echelon supply chain systems that include the cost of lost customers. We further provide theoretical insights into the impact of the two distinct customer types on the restoration planning, including conditions under which the restoration planning is independent of the customers’ behaviors. Our insights from our computational analysis include that a ‘chain reaction’ occurs in most scenarios, which means the supply chain system tends to lose the same subset of customers even when they are not served by the facilities impacted by disruption. Furthermore, different sets of post-event customer behaviors tend to favor the same pre-event mitigation strategies.

This work considers modeling approaches to the problem of restoration decision making by supply chain managers. Specifically, we consider an oil supply chain network that has suffered a cyber attack. As a result of the attack, critical services to the supply chain are lost, and the manager has uncertain information regarding when these services will be available. We first consider a deterministic model where the supply chain manager has complete information about the restoration of damaged services. We extend this model to a stochastic programming model involving several different recovery scenarios. We present computational results for a realistic case study developed with an extensive literature survey. We compare results of the stochastic model with average deterministic results, and show that the average deterministic results do not produce good solutions for an uncertain setting. We conclude with general remarks on how supply chain managers should consider restoration decisions under uncertainty.