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Title:CyberGIS-enabled spatial decision support for supply chain optimization with uncertainty quantification
Author(s):Hu, Hao
Director of Research:Wang, Shaowen
Doctoral Committee Chair(s):Wang, Shaowen
Doctoral Committee Member(s):Li, Bo; Rodríguez, Luis F; Kwan, Mei-Po; Ouyang, Yanfeng
Department / Program:Geography & Geographic InfoSci
Discipline:Geography
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):spatial decision support
cyberGIS
uncertainty and sensitivity analysis
supply chain optimization
spatiotemporal data analysis
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.
Issue Date:2018-07-13
Type:Thesis
URI:http://hdl.handle.net/2142/101557
Rights Information:Copyright 2018 Hao Hu
Date Available in IDEALS:2018-09-27
Date Deposited:2018-08


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