[CCoE Notice] Dissertation Defense: Maritime Vehicle Routing under Uncertainty: Liquefied Natural Gas Shipping and Offshore Pipeline Damage Assessment Problems

[Grayson, Audrey A]

PhD DEFENSE STUDENT: Jaeyoung Cho
DATE: Tuesday, June 14, 2016
TIME: 10:00 AM
PLACE: Industrial Engineering Conference Room (E214)
DISSERTATION CHAIR: Dr. Gino J. Lim
________________________________
TITLE:
Maritime Vehicle Routing under Uncertainty: Liquefied Natural Gas Shipping and Offshore Pipeline Damage Assessment Problems

We propose three maritime vehicle routing optimization models under uncertainty.
First, LNG ship routing optimization model considering random boil-off gas (BOG) is formulated as two-stage mixed integer program. In the first stage problem, we have an optimal single production inventory schedule and routing decisions before the realization of the random BOG. For every possible realization of the random BOG, the second-stage variables are represented by the amount of LNG surplus or shortage when a cargo vessel arrives at a regasification terminal. We solve the problem by converting to a deterministic equivalent using Monte Carlo sampling-based optimization.
Second, two types of LNG production-inventory planning and ship routing models under weather disruptions are proposed. In the first model, LNG inventory routing problem is formulated as a two-stage stochastic mixed integer program to maximize the overall expected revenue while minimizing uncertain impact of weather disruptions. Our second model differs in that a decision maker’s preference on risks is reflected by a parametric optimization technique. This model enables a decision maker to have a `what-if analysis' by varying the level of risk preference. To overcome the computational difficulties of the proposed models, two techniques have been proposed. A probing-based preprocessing technique is developed to reduce the number of binary variables utilizing the relations among time windows and the amount of boil-off gas in a path. The routing process is further simplified in the model by replacing the sub-tour elimination constraint with a logical inequality. Computational results indicate that our proposed models and techniques are well suited to solve the problem in a reasonable time.
Lastly, a two-phased multiple autonomous underwater vehicles (AUVs) assisted offshore pipeline network damage assessment model is presented to accelerate the inspection process. In the first phase, we determine optimum AUV pre-positioning locations before an extreme event to collect data how and what causes pipeline damages. This problem is formulated as a two-stage stochastic integer program considering uncertain strength of weather impact. The first stage decision is to find initial AUV positions before updating weather information. The second stage decision is to adjust these AUV pre-deployment locations at a time closer to the date of an extreme event with weather information for near future. After then, in the second phase, we generate paths to scan the designated offshore pipeline network while minimizing operating cost proportional to the number of AUVs assigned to each pre-positioning point. Computational results indicate that our proposed model and techniques are well suited to solve the problem in a reasonable time.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20160608/8737c344/attachment-0001.html 
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Announcement 0607 2016.docx
Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
Size: 16714 bytes
Desc: Announcement 0607 2016.docx
Url : http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20160608/8737c344/attachment-0001.bin