{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/17784"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/17784","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Partially Observable Markov Process Decision Modeling for the Optimal Maintenance of Oil and Gas Pipelines","abstract":"Partially Observable Markov Decision Process (POMDP) frameworks are employed across various fields. This dissertation studies the applications of POMDP for maintaining oil and gas pipelines. Pipeline maintenance operations comprise several uncertain elements, especially when addressing deteriorations caused by corrosion. These include the deterioration rate, the effectiveness of maintenance operations, and the accuracy of inspection or monitoring methods. POMDP is proposed to model these uncertainties. The POMDP solution method plays a crucial role in formulating and solving problems. Therefore, the first part of the study reviewed the diverse solution methods pursued in the literature. Based on this review, an improved vector pruning algorithm was introduced to enhance the pruning operation for an incremental pruning solution method. It formulated the LP optimization as prime and dual with Bender’s decomposition and incorporated bootstrap to expedite the optimization process. Various experiments were performed to demonstrate the improvements attained from the proposed solution method. The second part presents a discrete model for the practical application of POMDP to oil and gas pipeline corrosion maintenance. In this model, the states were discretized to represent a range of deterioration, while the actions represented the maintenance operations. Monte Carlo simulation and a pure birth Kolmogorov's forward equations approach were used to derive the transition matrix. Tool inaccuracies for an inline inspection (ILI) method were integrated into observations and observation function formulation. Rewards considered the costs associated with maintenance and failures. The model was numerically illustrated using generated values, data from the literature, and MDPToolBox solvers & the proposed improved pruning method. In the third part of the study, the continuous deterioration feature of corrosion was directly modeled using a continuous state POMDP. States measured the pipe deterioration as continuous values, whereas the ILI inaccuracies were included as continuous variables. A Generative function was formulated to perform the transition, and the discrete formulation for action and reward was kept the same. POMCP and ARDESPOT solvers were used to demonstrate a numerical example. Altogether, the proposed POMDP models represented some of the uncertain features of corrosion maintenance, enabling better decision-making in implementing the function.","abstract_html":"Partially Observable Markov Decision Process (POMDP) frameworks are employed across various fields. This dissertation studies the applications of POMDP for maintaining oil and gas pipelines. Pipeline maintenance operations comprise several uncertain elements, especially when addressing deteriorations caused by corrosion. These include the deterioration rate, the effectiveness of maintenance operations, and the accuracy of inspection or monitoring methods. POMDP is proposed to model these uncertainties. The POMDP solution method plays a crucial role in formulating and solving problems. Therefore, the first part of the study reviewed the diverse solution methods pursued in the literature. Based on this review, an improved vector pruning algorithm was introduced to enhance the pruning operation for an incremental pruning solution method. It formulated the LP optimization as prime and dual with Bender’s decomposition and incorporated bootstrap to expedite the optimization process. Various experiments were performed to demonstrate the improvements attained from the proposed solution method. The second part presents a discrete model for the practical application of POMDP to oil and gas pipeline corrosion maintenance. In this model, the states were discretized to represent a range of deterioration, while the actions represented the maintenance operations. Monte Carlo simulation and a pure birth Kolmogorov's forward equations approach were used to derive the transition matrix. Tool inaccuracies for an inline inspection (ILI) method were integrated into observations and observation function formulation. Rewards considered the costs associated with maintenance and failures. The model was numerically illustrated using generated values, data from the literature, and MDPToolBox solvers & the proposed improved pruning method. In the third part of the study, the continuous deterioration feature of corrosion was directly modeled using a continuous state POMDP. States measured the pipe deterioration as continuous values, whereas the ILI inaccuracies were included as continuous variables. A Generative function was formulated to perform the transition, and the discrete formulation for action and reward was kept the same. POMCP and ARDESPOT solvers were used to demonstrate a numerical example. Altogether, the proposed POMDP models represented some of the uncertain features of corrosion maintenance, enabling better decision-making in implementing the function.","abstract_has_math":false,"creators":["Wari, Ezra"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Lim, Gino"],"committee_chairs":[],"committee_members":["Zhu, Weihang","Xiang, Yisha","Fan, Lei","Lin, Ying"],"year":2024,"date_issued":"2024-05-07","date_published":"2024-05-07","updated_at":"2026-07-24T02:33:06Z","subjects":["Partially Observed Markov Processes","Oil and Gas Pipeline Maintenance","Increment Pruning"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/17784","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lim, Gino"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Zhu, Weihang","Xiang, Yisha","Fan, Lei","Lin, Ying"]},{"key":"dc:creator","label":"Author","values":["Wari, Ezra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-07-27T19:11:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-05-07"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Partially Observed Markov Processes","Oil and Gas Pipeline Maintenance","Increment Pruning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/17784"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Partially Observable Markov Decision Process (POMDP) frameworks are employed across various fields. This dissertation studies the applications of POMDP for maintaining oil and gas pipelines. Pipeline maintenance operations comprise several uncertain elements, especially when addressing deteriorations caused by corrosion. These include the deterioration rate, the effectiveness of maintenance operations, and the accuracy of inspection or monitoring methods. POMDP is proposed to model these uncertainties. The POMDP solution method plays a crucial role in formulating and solving problems. Therefore, the first part of the study reviewed the diverse solution methods pursued in the literature. Based on this review, an improved vector pruning algorithm was introduced to enhance the pruning operation for an incremental pruning solution method. It formulated the LP optimization as prime and dual with Bender’s decomposition and incorporated bootstrap to expedite the optimization process. Various experiments were performed to demonstrate the improvements attained from the proposed solution method. The second part presents a discrete model for the practical application of POMDP to oil and gas pipeline corrosion maintenance. In this model, the states were discretized to represent a range of deterioration, while the actions represented the maintenance operations. Monte Carlo simulation and a pure birth Kolmogorov's forward equations approach were used to derive the transition matrix. Tool inaccuracies for an inline inspection (ILI) method were integrated into observations and observation function formulation. Rewards considered the costs associated with maintenance and failures. The model was numerically illustrated using generated values, data from the literature, and MDPToolBox solvers & the proposed improved pruning method. In the third part of the study, the continuous deterioration feature of corrosion was directly modeled using a continuous state POMDP. States measured the pipe deterioration as continuous values, whereas the ILI inaccuracies were included as continuous variables. A Generative function was formulated to perform the transition, and the discrete formulation for action and reward was kept the same. POMCP and ARDESPOT solvers were used to demonstrate a numerical example. Altogether, the proposed POMDP models represented some of the uncertain features of corrosion maintenance, enabling better decision-making in implementing the function."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Partially Observable Markov Process Decision Modeling for the Optimal Maintenance of Oil and Gas Pipelines"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lim, Gino"],"dc:contributor.committeemember":["Zhu, Weihang","Xiang, Yisha","Fan, Lei","Lin, Ying"],"dc:creator":["Wari, Ezra"],"dc:date.accessioned":["2024-07-27T19:11:17Z"],"dc:date.issued":["2024-05-07"],"dc:description.abstract":["Partially Observable Markov Decision Process (POMDP) frameworks are employed across various fields. This dissertation studies the applications of POMDP for maintaining oil and gas pipelines. Pipeline maintenance operations comprise several uncertain elements, especially when addressing deteriorations caused by corrosion. These include the deterioration rate, the effectiveness of maintenance operations, and the accuracy of inspection or monitoring methods. POMDP is proposed to model these uncertainties. The POMDP solution method plays a crucial role in formulating and solving problems. Therefore, the first part of the study reviewed the diverse solution methods pursued in the literature. Based on this review, an improved vector pruning algorithm was introduced to enhance the pruning operation for an incremental pruning solution method. It formulated the LP optimization as prime and dual with Bender’s decomposition and incorporated bootstrap to expedite the optimization process. Various experiments were performed to demonstrate the improvements attained from the proposed solution method. The second part presents a discrete model for the practical application of POMDP to oil and gas pipeline corrosion maintenance. In this model, the states were discretized to represent a range of deterioration, while the actions represented the maintenance operations. Monte Carlo simulation and a pure birth Kolmogorov's forward equations approach were used to derive the transition matrix. Tool inaccuracies for an inline inspection (ILI) method were integrated into observations and observation function formulation. Rewards considered the costs associated with maintenance and failures. The model was numerically illustrated using generated values, data from the literature, and MDPToolBox solvers & the proposed improved pruning method. In the third part of the study, the continuous deterioration feature of corrosion was directly modeled using a continuous state POMDP. States measured the pipe deterioration as continuous values, whereas the ILI inaccuracies were included as continuous variables. A Generative function was formulated to perform the transition, and the discrete formulation for action and reward was kept the same. POMCP and ARDESPOT solvers were used to demonstrate a numerical example. Altogether, the proposed POMDP models represented some of the uncertain features of corrosion maintenance, enabling better decision-making in implementing the function."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/17784"],"dc:language.iso":["en"],"dc:subject":["Partially Observed Markov Processes","Oil and Gas Pipeline Maintenance","Increment Pruning"],"dc:title":["Partially Observable Markov Process Decision Modeling for the Optimal Maintenance of Oil and Gas Pipelines"],"dc:type":["Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:33:06Z"}