CyberGIS-enabled spatial decision support for supply chain optimization with uncertainty quantification

Hu, Hao

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https://hdl.handle.net/2142/101557 Copy

Description

Title

CyberGIS-enabled spatial decision support for supply chain optimization with uncertainty quantification

Author(s)

Hu, Hao

Issue Date

2018-07-13

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Shaowen

Doctoral Committee Chair(s)

Wang, Shaowen

Committee Member(s)

• Li, Bo
• Rodriguez, Luis F.
• Kwan, Mei-Po
• Ouyang, Yanfeng

Department of Study

Geography & Geographic InfoSci

Discipline

Geography

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2018-08

Type of Resource

text

Permalink

http://hdl.handle.net/2142/101557

Copyright and License Information

Copyright 2018 Hao Hu

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