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.
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Multi-period max flow network interdiction with restructuring for disrupting domestic sex trafficking networks
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.
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- Award ID(s):
- 2039584
- PAR ID:
- 10385451
- Publisher / Repository:
- Springer US
- Date Published:
- Journal Name:
- Annals of Operations Research
- Volume:
- 335
- Issue:
- 2
- ISSN:
- 0254-5330
- Page Range / eLocation ID:
- 797-860
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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