{"id":{"repo_id":"arizona-thes","oai_identifier":"oai:repository.arizona.edu:10150/623182"},"canonical_url":"https://search.dev.ndltd.org/etd/arizona-thes/oai:repository.arizona.edu:10150/623182","repository":{"repo_id":"arizona-thes","name":"University of Arizona","base_url":"https://repository.arizona.edu/oai/request"},"display":{"title":"Investigating the Maximal Coverage by Point-based Surrogate Model for Spatial Facility Location Problem","abstract":"Spatial facility location problems (SFLPs) involve the placement of facilities in continuous demand regions. One approach to solving SFLPs is to aggregate demand into discrete points, and then solve the point-based model as a conventional facility location problem (FLP) according to a surrogate model. Solution performance is measured in terms of the percentage of continuous space actually covered in the original SFLP. In this dissertation I explore this approach and examine factors contributing to solution quality. Three error sources are discussed: point representation spacing, multiple possible solutions to the surrogate point-based model, and round-off errors induced by the computer representation of numbers. Some factors—including boundary region surrogate points and surrogate point location—were also found to make significant contributions to coverage errors. A surrogate error measure using a point-based surrogate model was derived to characterize relationships among spacing, facility coverage area, and spatial coverage error. Locating continuous space facilities with full coverage is important but challenging. Demand surrogate points were initially used as a continuous space for constructing the MIP model, and a point-based surrogate FLP was enhanced for extracting multiple solutions with additional constraints that were found to reduce coverage error. Next, a best initial solution was applied to a proposed heuristic algorithm to serve as an improvement procedure. Algorithm performance was evaluated and applied to a problem involving the location of emergency warning sirens in the city of Dublin, Ohio. The effectiveness of the proposed method for solving this and other facility location/network design problems was demonstrated by comparing the results with those reported in recently published papers.","abstract_html":"Spatial facility location problems (SFLPs) involve the placement of facilities in continuous demand regions. One approach to solving SFLPs is to aggregate demand into discrete points, and then solve the point-based model as a conventional facility location problem (FLP) according to a surrogate model. Solution performance is measured in terms of the percentage of continuous space actually covered in the original SFLP. In this dissertation I explore this approach and examine factors contributing to solution quality. Three error sources are discussed: point representation spacing, multiple possible solutions to the surrogate point-based model, and round-off errors induced by the computer representation of numbers. Some factors—including boundary region surrogate points and surrogate point location—were also found to make significant contributions to coverage errors. A surrogate error measure using a point-based surrogate model was derived to characterize relationships among spacing, facility coverage area, and spatial coverage error. Locating continuous space facilities with full coverage is important but challenging. Demand surrogate points were initially used as a continuous space for constructing the MIP model, and a point-based surrogate FLP was enhanced for extracting multiple solutions with additional constraints that were found to reduce coverage error. Next, a best initial solution was applied to a proposed heuristic algorithm to serve as an improvement procedure. Algorithm performance was evaluated and applied to a problem involving the location of emergency warning sirens in the city of Dublin, Ohio. The effectiveness of the proposed method for solving this and other facility location/network design problems was demonstrated by comparing the results with those reported in recently published papers.","abstract_has_math":false,"creators":["Hsieh, Pei-Shan"],"institution":"The University of Arizona.","degree_name":"Ph.D.","degree_level":"doctoral","degree_discipline":"Graduate College","degree_department":null,"school":null,"contributors":[],"advisors":["Lin, Wei Hua"],"committee_chairs":[],"committee_members":["Lin, Wei Hua","Son, Young-Jun","Fan, Neng","An, Lingling"],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-24T00:56:34Z","subjects":["facility location","maximal coverage","continuous space"],"languages":["en_US"],"rights":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10150/623182","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lin, Wei Hua"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lin, Wei Hua","Son, Young-Jun","Fan, Neng","An, Lingling"]},{"key":"dc:creator","label":"Author","values":["Hsieh, Pei-Shan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-04-20T01:09:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-04-20T01:09:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2016"]},{"key":"dc:publisher","label":"Institution","values":["The University of Arizona."]},{"key":"dc:type","label":"Dc Type","values":["text","Electronic Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Graduate College","Systems & Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Arizona"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["facility location","maximal coverage","continuous space"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10150/623182"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Spatial facility location problems (SFLPs) involve the placement of facilities in continuous demand regions. One approach to solving SFLPs is to aggregate demand into discrete points, and then solve the point-based model as a conventional facility location problem (FLP) according to a surrogate model. Solution performance is measured in terms of the percentage of continuous space actually covered in the original SFLP. In this dissertation I explore this approach and examine factors contributing to solution quality. Three error sources are discussed: point representation spacing, multiple possible solutions to the surrogate point-based model, and round-off errors induced by the computer representation of numbers. Some factors—including boundary region surrogate points and surrogate point location—were also found to make significant contributions to coverage errors. A surrogate error measure using a point-based surrogate model was derived to characterize relationships among spacing, facility coverage area, and spatial coverage error. Locating continuous space facilities with full coverage is important but challenging. Demand surrogate points were initially used as a continuous space for constructing the MIP model, and a point-based surrogate FLP was enhanced for extracting multiple solutions with additional constraints that were found to reduce coverage error. Next, a best initial solution was applied to a proposed heuristic algorithm to serve as an improvement procedure. Algorithm performance was evaluated and applied to a problem involving the location of emergency warning sirens in the city of Dublin, Ohio. The effectiveness of the proposed method for solving this and other facility location/network design problems was demonstrated by comparing the results with those reported in recently published papers."]},{"key":"dc:title","label":"Title","values":["Investigating the Maximal Coverage by Point-based Surrogate Model for Spatial Facility Location Problem"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lin, Wei Hua"],"dc:contributor.committeemember":["Lin, Wei Hua","Son, Young-Jun","Fan, Neng","An, Lingling"],"dc:creator":["Hsieh, Pei-Shan"],"dc:date.accessioned":["2017-04-20T01:09:24Z"],"dc:date.available":["2017-04-20T01:09:24Z"],"dc:date.issued":["2016"],"dc:description.abstract":["Spatial facility location problems (SFLPs) involve the placement of facilities in continuous demand regions. One approach to solving SFLPs is to aggregate demand into discrete points, and then solve the point-based model as a conventional facility location problem (FLP) according to a surrogate model. Solution performance is measured in terms of the percentage of continuous space actually covered in the original SFLP. In this dissertation I explore this approach and examine factors contributing to solution quality. Three error sources are discussed: point representation spacing, multiple possible solutions to the surrogate point-based model, and round-off errors induced by the computer representation of numbers. Some factors—including boundary region surrogate points and surrogate point location—were also found to make significant contributions to coverage errors. A surrogate error measure using a point-based surrogate model was derived to characterize relationships among spacing, facility coverage area, and spatial coverage error. Locating continuous space facilities with full coverage is important but challenging. Demand surrogate points were initially used as a continuous space for constructing the MIP model, and a point-based surrogate FLP was enhanced for extracting multiple solutions with additional constraints that were found to reduce coverage error. Next, a best initial solution was applied to a proposed heuristic algorithm to serve as an improvement procedure. Algorithm performance was evaluated and applied to a problem involving the location of emergency warning sirens in the city of Dublin, Ohio. The effectiveness of the proposed method for solving this and other facility location/network design problems was demonstrated by comparing the results with those reported in recently published papers."],"dc:identifier.uri":["http://hdl.handle.net/10150/623182"],"dc:language.iso":["en_US"],"dc:publisher":["The University of Arizona."],"dc:rights":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author."],"dc:subject":["facility location","maximal coverage","continuous space"],"dc:title":["Investigating the Maximal Coverage by Point-based Surrogate Model for Spatial Facility Location Problem"],"dc:type":["text","Electronic Dissertation"],"thesis:degree_discipline":["Graduate College","Systems & Industrial Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Arizona"]},"updated_at":"2026-07-24T00:56:34Z"}