{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101557"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101557","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"CyberGIS-enabled spatial decision support for supply chain optimization with uncertainty quantification","abstract":"Spatial decision support systems have made extensive progress on taking advantage of geographic information science and systems (GIS) for the synthesis of geospatial data and analysis, domain-specific knowledge and models, and advanced computing technologies. However, a major challenge revolving around the synthesis remains to systematically quantify uncertainties of complex data, models, and computation. For example, the state of the art of supply chain optimization does not adequately address uncertainty in the context of spatial decision support. This challenge is caused in part by the computational intensity of uncertainty quantification and propagation through optimization models. This research aims to establish a novel cyberGIS framework for resolving the computational intensity to incorporate uncertainty quantification into spatial decision support. Specifically, the cyberGIS framework seamlessly integrates uncertainty quantification and supply chain optimization modeling into a CyberGIS Gateway application that represents a cutting-edge online cyberGIS environment for users to perform interactive spatial decision-making enabled by advanced cyberinfrastructure. Furthermore, an innovative method combining Bayesian hierarchical modeling with stochastic programming is proposed to explicitly account for spatiotemporal uncertainties in supply chain optimization. The cyberGIS framework and related method are evaluated based on a case study of the biomass-to-bioenergy supply chain optimization at the county level in the United States to resolve the synthesis challenge in multiple spatial decision support scenarios.","abstract_html":"Spatial decision support systems have made extensive progress on taking advantage of geographic information science and systems (GIS) for the synthesis of geospatial data and analysis, domain-specific knowledge and models, and advanced computing technologies. However, a major challenge revolving around the synthesis remains to systematically quantify uncertainties of complex data, models, and computation. For example, the state of the art of supply chain optimization does not adequately address uncertainty in the context of spatial decision support. This challenge is caused in part by the computational intensity of uncertainty quantification and propagation through optimization models. This research aims to establish a novel cyberGIS framework for resolving the computational intensity to incorporate uncertainty quantification into spatial decision support. Specifically, the cyberGIS framework seamlessly integrates uncertainty quantification and supply chain optimization modeling into a CyberGIS Gateway application that represents a cutting-edge online cyberGIS environment for users to perform interactive spatial decision-making enabled by advanced cyberinfrastructure. Furthermore, an innovative method combining Bayesian hierarchical modeling with stochastic programming is proposed to explicitly account for spatiotemporal uncertainties in supply chain optimization. The cyberGIS framework and related method are evaluated based on a case study of the biomass-to-bioenergy supply chain optimization at the county level in the United States to resolve the synthesis challenge in multiple spatial decision support scenarios.","abstract_has_math":false,"creators":["Hu, Hao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Geography","degree_department":null,"school":null,"contributors":["Wang, Shaowen","Li, Bo","Rodriguez, Luis F.","Kwan, Mei-Po","Ouyang, Yanfeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:17:47Z","date_published":"2018-09-27T16:17:47Z","updated_at":"2026-07-22T22:24:40Z","subjects":["spatial decision support","cyberGIS","uncertainty and sensitivity analysis","supply chain optimization","spatiotemporal data analysis"],"languages":["en"],"rights":["Copyright 2018 Hao Hu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101557","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Shaowen","Li, Bo","Rodriguez, Luis F.","Kwan, Mei-Po","Ouyang, Yanfeng"]},{"key":"dc:creator","label":"Author","values":["Hu, Hao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:17:47Z","2018-07-13","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["spatial decision support","cyberGIS","uncertainty and sensitivity analysis","supply chain optimization","spatiotemporal data analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Hao Hu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101557"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Spatial decision support systems have made extensive progress on taking advantage of geographic information science and systems (GIS) for the synthesis of geospatial data and analysis, domain-specific knowledge and models, and advanced computing technologies. However, a major challenge revolving around the synthesis remains to systematically quantify uncertainties of complex data, models, and computation. For example, the state of the art of supply chain optimization does not adequately address uncertainty in the context of spatial decision support. This challenge is caused in part by the computational intensity of uncertainty quantification and propagation through optimization models. This research aims to establish a novel cyberGIS framework for resolving the computational intensity to incorporate uncertainty quantification into spatial decision support. Specifically, the cyberGIS framework seamlessly integrates uncertainty quantification and supply chain optimization modeling into a CyberGIS Gateway application that represents a cutting-edge online cyberGIS environment for users to perform interactive spatial decision-making enabled by advanced cyberinfrastructure. Furthermore, an innovative method combining Bayesian hierarchical modeling with stochastic programming is proposed to explicitly account for spatiotemporal uncertainties in supply chain optimization. The cyberGIS framework and related method are evaluated based on a case study of the biomass-to-bioenergy supply chain optimization at the county level in the United States to resolve the synthesis challenge in multiple spatial decision support scenarios.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Hao Hu, accepted the attached license on 2018-07-10 at 16:17.","The student, Hao Hu, submitted this Dissertation for approval on 2018-07-10 at 16:32.","This Dissertation was approved for publication on 2018-07-13 at 11:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12806 on 2018-09-27 at 10:47:42","Made available in DSpace on 2018-09-27T16:17:47Z (GMT). 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However, a major challenge revolving around the synthesis remains to systematically quantify uncertainties of complex data, models, and computation. For example, the state of the art of supply chain optimization does not adequately address uncertainty in the context of spatial decision support. This challenge is caused in part by the computational intensity of uncertainty quantification and propagation through optimization models. This research aims to establish a novel cyberGIS framework for resolving the computational intensity to incorporate uncertainty quantification into spatial decision support. Specifically, the cyberGIS framework seamlessly integrates uncertainty quantification and supply chain optimization modeling into a CyberGIS Gateway application that represents a cutting-edge online cyberGIS environment for users to perform interactive spatial decision-making enabled by advanced cyberinfrastructure. Furthermore, an innovative method combining Bayesian hierarchical modeling with stochastic programming is proposed to explicitly account for spatiotemporal uncertainties in supply chain optimization. The cyberGIS framework and related method are evaluated based on a case study of the biomass-to-bioenergy supply chain optimization at the county level in the United States to resolve the synthesis challenge in multiple spatial decision support scenarios.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Hao Hu, accepted the attached license on 2018-07-10 at 16:17.","The student, Hao Hu, submitted this Dissertation for approval on 2018-07-10 at 16:32.","This Dissertation was approved for publication on 2018-07-13 at 11:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12806 on 2018-09-27 at 10:47:42","Made available in DSpace on 2018-09-27T16:17:47Z (GMT). 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