Modeling and optimization for spatial detection to minimize abandonment rate
text Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs...
Main Author: | |
---|---|
Other Authors: | , |
Format: | Thesis |
Language: | English |
Published: |
2014
|
Subjects: | |
Online Access: | http://hdl.handle.net/2152/25998 |
id |
ftunivtexas:oai:repositories.lib.utexas.edu:2152/25998 |
---|---|
record_format |
openpolar |
spelling |
ftunivtexas:oai:repositories.lib.utexas.edu:2152/25998 2023-05-15T15:13:33+02:00 Modeling and optimization for spatial detection to minimize abandonment rate Lu, Fang, active 21st century Morton, David P. Hasenbein, John J. August 2014 application/pdf http://hdl.handle.net/2152/25998 en eng http://hdl.handle.net/2152/25998 Spatial detection Queues with abandonments Simulation Stochastic programming Multi-stage stochastic facility location problem Scheduling heuristics Thesis 2014 ftunivtexas 2020-12-23T22:06:11Z text Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. Operations Research and Industrial Engineering Thesis Arctic Arctic Ocean Sea ice The University of Texas at Austin: Texas ScholarWorks Arctic Arctic Ocean |
institution |
Open Polar |
collection |
The University of Texas at Austin: Texas ScholarWorks |
op_collection_id |
ftunivtexas |
language |
English |
topic |
Spatial detection Queues with abandonments Simulation Stochastic programming Multi-stage stochastic facility location problem Scheduling heuristics |
spellingShingle |
Spatial detection Queues with abandonments Simulation Stochastic programming Multi-stage stochastic facility location problem Scheduling heuristics Lu, Fang, active 21st century Modeling and optimization for spatial detection to minimize abandonment rate |
topic_facet |
Spatial detection Queues with abandonments Simulation Stochastic programming Multi-stage stochastic facility location problem Scheduling heuristics |
description |
text Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. Operations Research and Industrial Engineering |
author2 |
Morton, David P. Hasenbein, John J. |
format |
Thesis |
author |
Lu, Fang, active 21st century |
author_facet |
Lu, Fang, active 21st century |
author_sort |
Lu, Fang, active 21st century |
title |
Modeling and optimization for spatial detection to minimize abandonment rate |
title_short |
Modeling and optimization for spatial detection to minimize abandonment rate |
title_full |
Modeling and optimization for spatial detection to minimize abandonment rate |
title_fullStr |
Modeling and optimization for spatial detection to minimize abandonment rate |
title_full_unstemmed |
Modeling and optimization for spatial detection to minimize abandonment rate |
title_sort |
modeling and optimization for spatial detection to minimize abandonment rate |
publishDate |
2014 |
url |
http://hdl.handle.net/2152/25998 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Sea ice |
genre_facet |
Arctic Arctic Ocean Sea ice |
op_relation |
http://hdl.handle.net/2152/25998 |
_version_ |
1766344101060411392 |