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Article: Stochastic maximum flow interdiction problems under heterogeneous risk preferences

TitleStochastic maximum flow interdiction problems under heterogeneous risk preferences
Authors
KeywordsConditional value-at-Risk (CVar)
Maximum flow interdiction
Mixed-integer linear programming
Risk-averse optimization
Stochastic programming
Issue Date2018
Citation
Computers and Operations Research, 2018, v. 90, p. 97-109 How to Cite?
AbstractWe consider a generic maximum flow interdiction problem that involves a leader and a follower who take actions in sequence. Given an interdiction budget, the leader destroys a subset of arcs to minimize the follower's maximum flows from a source to a sink node. The effect from an interdiction action taken on each arc is random, following a given success rate of decreasing the arc's capacity to zero. The follower can add additional arc capacities for mitigating flow losses, after knowing the leader's interdiction plan but before realizing the uncertainty. We consider risk-neutral and risk-averse behaviors of the two players and investigate five bi-level/tri-level programming models for different risk-preference combinations. The models incorporate the expectation, left-tail, and right-tail Conditional Value-at-Risk (CVaR) as commonly used convex risk measures for evaluating random maximum flows in the leader's and follower's objectives. We reformulate each model as an equivalent mixed-integer linear program and test them on real-world network instances to demonstrate interactions between the leader and the follower under various risk-preference settings.
Persistent Identifierhttp://hdl.handle.net/10722/323126
ISSN
2021 Impact Factor: 5.159
2020 SCImago Journal Rankings: 1.506

 

DC FieldValueLanguage
dc.contributor.authorLei, Xiao-
dc.contributor.authorShen, Siqian-
dc.contributor.authorSong, Yongjia-
dc.date.accessioned2022-11-18T11:54:55Z-
dc.date.available2022-11-18T11:54:55Z-
dc.date.issued2018-
dc.identifier.citationComputers and Operations Research, 2018, v. 90, p. 97-109-
dc.identifier.issn0305-0548-
dc.identifier.urihttp://hdl.handle.net/10722/323126-
dc.description.abstractWe consider a generic maximum flow interdiction problem that involves a leader and a follower who take actions in sequence. Given an interdiction budget, the leader destroys a subset of arcs to minimize the follower's maximum flows from a source to a sink node. The effect from an interdiction action taken on each arc is random, following a given success rate of decreasing the arc's capacity to zero. The follower can add additional arc capacities for mitigating flow losses, after knowing the leader's interdiction plan but before realizing the uncertainty. We consider risk-neutral and risk-averse behaviors of the two players and investigate five bi-level/tri-level programming models for different risk-preference combinations. The models incorporate the expectation, left-tail, and right-tail Conditional Value-at-Risk (CVaR) as commonly used convex risk measures for evaluating random maximum flows in the leader's and follower's objectives. We reformulate each model as an equivalent mixed-integer linear program and test them on real-world network instances to demonstrate interactions between the leader and the follower under various risk-preference settings.-
dc.languageeng-
dc.relation.ispartofComputers and Operations Research-
dc.subjectConditional value-at-Risk (CVar)-
dc.subjectMaximum flow interdiction-
dc.subjectMixed-integer linear programming-
dc.subjectRisk-averse optimization-
dc.subjectStochastic programming-
dc.titleStochastic maximum flow interdiction problems under heterogeneous risk preferences-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.cor.2017.09.004-
dc.identifier.scopuseid_2-s2.0-85029820229-
dc.identifier.volume90-
dc.identifier.spage97-
dc.identifier.epage109-

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