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

spatial decision support;cyberGIS;uncertainty and sensitivity analysis;supply chain optimization;spatiotemporal data analysis

Hu, Hao

关键词: spatial decision support;    cyberGIS;    uncertainty and sensitivity analysis;    supply chain optimization;    spatiotemporal data analysis;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101557/HU-DISSERTATION-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.