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Article: A distributionally robust joint chance constrained optimization model for the dynamic network design problem under demand uncertainty

TitleA distributionally robust joint chance constrained optimization model for the dynamic network design problem under demand uncertainty
Authors
Issue Date2014
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1566-113x
Citation
Networks and Spatial Economics, 2014, v. 14 n. 3-4, p. 409-433 How to Cite?
AbstractThis paper develops a distributionally robust joint chance constrained optimization model for a dynamic network design problem (NDP) under demand uncertainty. The major contribution of this paper is to propose an approach to approximate a joint chance-constrained Cell Transmission Model (CTM) based System Optimal Dynamic Network Design Problem with only partial distributional information of uncertain demand. The proposed approximation is tighter than two popular benchmark approximations, namely the Bonferroni’s inequality and second-order cone programming (SOCP) approximations. The resultant formulation is a semidefinite program which is computationally efficient. A numerical experiment is conducted to demonstrate that the proposed approximation approach is superior to the other two approximation approaches in terms of solution quality. The proposed approximation approach may provide useful insights and have broader applicability in traffic management and traffic planning problems under uncertainty.
Persistent Identifierhttp://hdl.handle.net/10722/202639
ISSN
2015 Impact Factor: 3.25
2015 SCImago Journal Rankings: 2.120

 

DC FieldValueLanguage
dc.contributor.authorSun, Hen_US
dc.contributor.authorGao, Zen_US
dc.contributor.authorSzeto, WYen_US
dc.contributor.authorLong, Jen_US
dc.contributor.authorZhao, F-
dc.date.accessioned2014-09-19T09:14:10Z-
dc.date.available2014-09-19T09:14:10Z-
dc.date.issued2014en_US
dc.identifier.citationNetworks and Spatial Economics, 2014, v. 14 n. 3-4, p. 409-433en_US
dc.identifier.issn1566-113X-
dc.identifier.urihttp://hdl.handle.net/10722/202639-
dc.description.abstractThis paper develops a distributionally robust joint chance constrained optimization model for a dynamic network design problem (NDP) under demand uncertainty. The major contribution of this paper is to propose an approach to approximate a joint chance-constrained Cell Transmission Model (CTM) based System Optimal Dynamic Network Design Problem with only partial distributional information of uncertain demand. The proposed approximation is tighter than two popular benchmark approximations, namely the Bonferroni’s inequality and second-order cone programming (SOCP) approximations. The resultant formulation is a semidefinite program which is computationally efficient. A numerical experiment is conducted to demonstrate that the proposed approximation approach is superior to the other two approximation approaches in terms of solution quality. The proposed approximation approach may provide useful insights and have broader applicability in traffic management and traffic planning problems under uncertainty.-
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1566-113x-
dc.relation.ispartofNetworks and Spatial Economicsen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA distributionally robust joint chance constrained optimization model for the dynamic network design problem under demand uncertaintyen_US
dc.typeArticleen_US
dc.identifier.emailSzeto, WY: ceszeto@hku.hken_US
dc.identifier.authoritySzeto, WY=rp01377en_US
dc.description.naturepostprint-
dc.identifier.doi10.1007/s11067-014-9236-8-
dc.identifier.hkuros236069en_US
dc.identifier.volume14-
dc.identifier.issue3-4-
dc.identifier.spage409-
dc.identifier.epage433-
dc.publisher.placeUnited States-

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