Maritime Vehicle Routing under Uncertainty: Liquefied Natural Gas Shipping and Offshore Pipeline Damage Assessment Problems
| dc.contributor.advisor | Lim, Gino J. | |
| dc.contributor.committeeMember | Peng, Jiming | |
| dc.contributor.committeeMember | Tekin, Eylem | |
| dc.contributor.committeeMember | Vipulanandan, Cumaraswamy | |
| dc.contributor.committeeMember | Nikolaou, Michael | |
| dc.creator | Cho, Jaeyoung | |
| dc.date.accessioned | 2018-11-30T21:43:02Z | |
| dc.date.available | 2018-11-30T21:43:02Z | |
| dc.date.created | August 2016 | |
| dc.date.issued | 2016-08 | |
| dc.date.submitted | August 2016 | |
| dc.date.updated | 2018-11-30T21:43:02Z | |
| dc.description.abstract | Maritime vehicle routing and scheduling problem has been studied extensively in the context of risk mitigation. This dissertation addresses three maritime vehicle routing problems and its mathematical frameworks considering environmental uncertainty. First, LNG shipping problem is investigated considering LNG market change, ship construction technology advances and random boil-off gas (BOG) generation. This is formulated as a two-stage stochastic mixed integer program. In the initial stage, a single production-inventory plan and routing schedule is determined before the realization of the random BOG generation. For every possible realization of the random BOG, the second-stage variables are represented by the amount of LNG surplus or shortage when an LNG carrier arrives at a regasification plant. This model provides a flexible transportation strategy reflecting LNG market trend and diversified LNG carrier specifications. Second, LNG production-inventory planning and ship routing under random weather disruptions is discussed. This problem is formulated to two optimization models: a two-stage stochastic mixed integer programming model and a parametric optimization model. The first one maximizes the overall expected revenue while minimizing disruption cost which results from extreme weathers. The second one, a parametric optimization model, attempts to reflect the decision maker's preference on risks by varying the ratio of revenue to on-time delivery. Therefore, a decision maker can have a 'what-if analysis' to compare multiple options for the final planning decision. Stochastic production-inventory control constraints set is also developed which synchronizes production-inventory plan and LNG carrier routing schedule under weather disruption. Lastly, offshore pipeline networks damage assessment problem is discussed. In order to collect how/what might have caused pipeline damages by a weather disruption, multiple AUVs are pre-positioned at some selected underwater locations before the beginning of the extreme weather. Once the weather clears up, the pre-deployed AUVs start pipeline damage assessment. This problem is formulated as a two-phased multiple AUVs pre-positioning and routing model. The first phase problem is to determine optimum AUVs' pre-positioning locations considering maximum AUV operating distance and random weather impact. In the second phase, AUV paths are generated to scan the designated offshore pipeline networks while minimizing operating cost proportional to the number of pre-deployed AUVs. | |
| dc.description.department | Industrial Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10657/3579 | |
| dc.language.iso | eng | |
| dc.rights | The 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. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Vehicle Routing Problem | |
| dc.subject | Stochastic Programming | |
| dc.title | Maritime Vehicle Routing under Uncertainty: Liquefied Natural Gas Shipping and Offshore Pipeline Damage Assessment Problems | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Industrial Engineering, Department of | |
| thesis.degree.discipline | Industrial Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |