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dc.contributor.advisorLim, Gino J.
dc.creatorBiobaku, Taofeek
dc.date.accessioned2018-12-03T20:54:13Z
dc.date.available2018-12-03T20:54:13Z
dc.date.created2016-08
dc.date.issuedAugust 2016
dc.date.submittedAugust 2016
dc.identifier.citationPortions of this document appear in: Biobaku, Taofeek, Gino Lim, Jaeyoung Cho, Hamid Parsaei, and Seonjin Kim. "Liquefied natural gas ship route planning: A risk analysis approach." Procedia Manufacturing 3 (2015): 1319-1326. And in: Biobaku, Taofeek, Gino J. Lim, Selim Bora, Jaeyoung Cho, and Hamid Parsaei. "An optimal sonar placement approach for detecting underwater threats under budget limitations." Journal of transportation security 9, no. 1-2 (2016): 17-34.
dc.identifier.urihttp://hdl.handle.net/10657/3611
dc.description.abstractConsidering that unprotected assets and infrastructures in the Maritime industry are vulnerable to attacks, we present models and methodologies for protecting these maritime resources from malicious or terrorist attacks. Using risk-based analysis, we use conditional probabilities to establish relationships between consequences, vulnerabilities and threat incidences of maritime events. In the first part of this dissertation, we address safety/security of maritime assets. We consider vessel routing and scheduling in LNG vessels as a hazardous cargo, and present a risk-based methodology in the choice of alternate vessel routes between a liquefaction terminal and receiving depot(s). While derivations are presented for the quantification of each constituent of the risk-based model, actual historical data of terrorist/piracy attacks made available by a national consortium on the study of terrorism are used in the analysis approach. With a multivehicle routing model, we test our methodology and present results using a practical test case involving delivery of LNG. In the second part of this dissertation, we address safety/security of maritime infrastructures and use underwater sonars for threat detection. Models and algorithms are developed for providing surveillance to maritime infrastructures such as ports, harbors, jetties, etc. The methodologies in these models include a quantitative risk analysis approach, a network fortification approach, a greedy-based heuristic approach, and a robust optimization approach. The network fortification approach considers the ability of an intending ‘attacker’ to possess information related to resource limitations and protection procedure of a ‘defender’. Consequently, the ‘attacker’ attempts to use this information to evade detection, thus compromising safety and security of maritime infrastructures. In developing greedy-based algorithms to solve large scale problems in our placement methodology, we exploit the principle of submodularity to propose efficient solution algorithms with some theoretical guarantees. Lastly, we developed a robust formulation for our placement methodology to address uncertainties related to some modeling parameters. To illustrate that the new sonar placement methodologies developed help to improve protection coverage plans for maritime infrastructures, we use practical case studies to provide safety and security to ports. In addition, we provide analytical and experimental results on each of these studies.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMaritime Safety
dc.subjectMaritime Security
dc.titleRisk-based Optimization Models for Maritime Safety and Security
dc.date.updated2018-12-03T20:54:13Z
dc.type.genreThesis
thesis.degree.nameDoctor of Philosophy
thesis.degree.levelDoctoral
thesis.degree.disciplineIndustrial Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.departmentIndustrial Engineering, Department of
dc.contributor.committeeMemberFeng, Qianmei
dc.contributor.committeeMemberPeng, Jiming
dc.contributor.committeeMemberMay, Elebeoba E.
dc.contributor.committeeMemberHan, Zhu
dc.type.dcmiText
dc.format.digitalOriginborn digital
dc.description.departmentIndustrial Engineering, Department of
thesis.degree.collegeCullen College of Engineering


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