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 |
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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. |
doi_str_mv | 10.1287/opre.2019.1890 |
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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.</description><identifier>ISSN: 0030-364X</identifier><identifier>EISSN: 1526-5463</identifier><identifier>DOI: 10.1287/opre.2019.1890</identifier><language>eng</language><publisher>Linthicum: INFORMS</publisher><subject>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</subject><ispartof>Operations research, 2020-03, Vol.68 (2), p.348-362, Article opre.2019.1890</ispartof><rights>Copyright Institute for Operations Research and the Management Sciences Mar/Apr 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c370t-691139f1bd7492259eb147f9e15096b564f3b9583c3ccb1cdcb73c41017b03ef3</citedby><cites>FETCH-LOGICAL-c370t-691139f1bd7492259eb147f9e15096b564f3b9583c3ccb1cdcb73c41017b03ef3</cites><orcidid>0000-0003-2532-4637 ; 0000-0002-5712-0308</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubsonline.informs.org/doi/full/10.1287/opre.2019.1890$$EHTML$$P50$$Ginforms$$H</linktohtml><link.rule.ids>314,776,780,3678,27903,27904,62592</link.rule.ids></links><search><creatorcontrib>Hien, Le Thi Khanh</creatorcontrib><creatorcontrib>Sim, Melvyn</creatorcontrib><creatorcontrib>Xu, Huan</creatorcontrib><title>Mitigating Interdiction Risk with Fortification</title><title>Operations research</title><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.</description><subject>Food fortification</subject><subject>fortification model</subject><subject>Integer programming</subject><subject>Iterative methods</subject><subject>Military and Homeland Security</subject><subject>military: defense systems</subject><subject>multiple sources and sinks</subject><subject>networks/graphs: stochastic</subject><subject>Operations research</subject><subject>Optimization</subject><subject>programming: stochastic</subject><subject>random demand</subject><subject>Risk factors</subject><subject>Risk management</subject><subject>robust stochastic approximation</subject><subject>Stochastic models</subject><issn>0030-364X</issn><issn>1526-5463</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkEFLAzEQhYMoWKtXzwuedzuTbJLNUYrVQkUQBW9hkyY11e6uSYr47-1S754GHt97Ax8h1wgV0kbO-iG6igKqChsFJ2SCnIqS14KdkgkAg5KJ-u2cXKS0BQDFBZ-Q2WPIYdPm0G2KZZddXAebQ98VzyF9FN8hvxeLPubgg23H_JKc-fYzuau_OyWvi7uX-UO5erpfzm9XpWUScikUIlMezVrWilKunMFaeuWQgxKGi9ozo3jDLLPWoF1bI5mtEVAaYM6zKbk57g6x_9q7lPW238fu8FJT1lDRNELigaqOlI19StF5PcSwa-OPRtCjFD1K0aMUPUo5FMpjIXS-j7v0H_8LwDxjQA</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Hien, Le Thi Khanh</creator><creator>Sim, Melvyn</creator><creator>Xu, Huan</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>K9.</scope><orcidid>https://orcid.org/0000-0003-2532-4637</orcidid><orcidid>https://orcid.org/0000-0002-5712-0308</orcidid></search><sort><creationdate>20200301</creationdate><title>Mitigating Interdiction Risk with Fortification</title><author>Hien, Le Thi Khanh ; Sim, Melvyn ; Xu, Huan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-691139f1bd7492259eb147f9e15096b564f3b9583c3ccb1cdcb73c41017b03ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Food fortification</topic><topic>fortification model</topic><topic>Integer programming</topic><topic>Iterative methods</topic><topic>Military and Homeland Security</topic><topic>military: defense systems</topic><topic>multiple sources and sinks</topic><topic>networks/graphs: stochastic</topic><topic>Operations research</topic><topic>Optimization</topic><topic>programming: stochastic</topic><topic>random demand</topic><topic>Risk factors</topic><topic>Risk management</topic><topic>robust stochastic approximation</topic><topic>Stochastic models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hien, Le Thi Khanh</creatorcontrib><creatorcontrib>Sim, Melvyn</creatorcontrib><creatorcontrib>Xu, Huan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hien, Le Thi Khanh</au><au>Sim, Melvyn</au><au>Xu, Huan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mitigating Interdiction Risk with Fortification</atitle><jtitle>Operations research</jtitle><date>2020-03-01</date><risdate>2020</risdate><volume>68</volume><issue>2</issue><spage>348</spage><epage>362</epage><pages>348-362</pages><artnum>opre.2019.1890</artnum><issn>0030-364X</issn><eissn>1526-5463</eissn><abstract>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.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/opre.2019.1890</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-2532-4637</orcidid><orcidid>https://orcid.org/0000-0002-5712-0308</orcidid></addata></record> |
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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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