Mitigating Interdiction Risk with Fortification

Critical infrastructure is inevitably subject to the risk of interdictions, which may potentially cripple its ability to channel resources to fulfill demands at various destinations. Mitigating such risks are therefore important questions faced by the stakeholders. In “Mitigating Interdiction Risk w...

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Veröffentlicht in:Operations research 2020-03, Vol.68 (2), p.348-362, Article opre.2019.1890
Hauptverfasser: Hien, Le Thi Khanh, Sim, Melvyn, Xu, Huan
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Xu, Huan
description Critical infrastructure is inevitably subject to the risk of interdictions, which may potentially cripple its ability to channel resources to fulfill demands at various destinations. Mitigating such risks are therefore important questions faced by the stakeholders. In “Mitigating Interdiction Risk with Fortification,” Hien, Sim, and Xu incorporate the defender–attacker–defender sequential game model with stochastic programming to propose a preventive approach to achieving network resilience by allocating resources—to build additional capacity or protect extant capacity - and thus fortifying the network against interdiction/disruption. The unique fortification model accounts simultaneously for three components: adversarial disruption, stochastic demand, and the decision maker’s attitude toward risk, and it can be solved efficiently via a robust stochastic approximation approach. We study a network fortification problem on a directed network that channels single-commodity resources to fulfill random demands delivered to a subset of the nodes. For given a realization of demands, the malicious interdictor would disrupt the network in a manner that would maximize the total demand shortfalls subject to the interdictor’s constraints. To mitigate the risk of such shortfalls, a network’s operator can fortify it by providing additional network capacity and/or protecting the nominal capacity. Given the stochastic nature of the demand uncertainty, the goal is to fortify the network, within the operator’s budget constraint, to minimize the expected disutility of the shortfalls in events of interdiction. We model this as a three-level, nonlinear stochastic optimization problem that can be solved via a robust stochastic approximation approach under which each iteration involves solving a linear mixed-integer program. We provide favorable computational results that demonstrate how our fortification strategy effectively mitigates interdiction risks. We also extend the model to multicommodity network with multiple sources and multiple sinks.
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subjects Food fortification
fortification model
Integer programming
Iterative methods
Military and Homeland Security
military: defense systems
multiple sources and sinks
networks/graphs: stochastic
Operations research
Optimization
programming: stochastic
random demand
Risk factors
Risk management
robust stochastic approximation
Stochastic models
title Mitigating Interdiction Risk with Fortification
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