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...

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Bibliographic Details
Main Author: Lu, Fang, active 21st century
Other Authors: Morton, David P., Hasenbein, John J.
Format: Thesis
Language:English
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/2152/25998
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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
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