Abstract

Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal−1, considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations.

Original languageEnglish (US)
Pages (from-to)26-40
Number of pages15
JournalApplied Energy
Volume203
DOIs
StatePublished - Oct 1 2017

Fingerprint

Biomass
biomass
Supply chains
Costs
Uncertainty analysis
Sensitivity analysis
Ethanol
uncertainty analysis
sensitivity analysis
ethanol
production cost
cost
modeling
Information science
Supply chain management
Decision support systems
Information management
Logistics
Statistical methods
Information systems

Keywords

  • Biomass supply chain
  • CyberGIS
  • Geodesign
  • Optimization
  • Sensitivity analysis
  • Spatial decision support system
  • Uncertainty

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Energy(all)

Cite this

A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization. / Hu, Hao; Lin, Tao; Wang, Shaowen; Rodriguez, Luis F.

In: Applied Energy, Vol. 203, 01.10.2017, p. 26-40.

Research output: Contribution to journalArticle

Hu, Hao; Lin, Tao; Wang, Shaowen; Rodriguez, Luis F. / A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization.

In: Applied Energy, Vol. 203, 01.10.2017, p. 26-40.

Research output: Contribution to journalArticle

@article{ec6a5740390f4dbd9142fcd9be7b8519,
title = "A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization",
abstract = "Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal−1, considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations.",
keywords = "Biomass supply chain, CyberGIS, Geodesign, Optimization, Sensitivity analysis, Spatial decision support system, Uncertainty",
author = "Hao Hu and Tao Lin and Shaowen Wang and Rodriguez, {Luis F.}",
year = "2017",
month = "10",
doi = "10.1016/j.apenergy.2017.03.107",
volume = "203",
pages = "26--40",
journal = "Applied Energy",
issn = "0306-2619",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization

AU - Hu,Hao

AU - Lin,Tao

AU - Wang,Shaowen

AU - Rodriguez,Luis F.

PY - 2017/10/1

Y1 - 2017/10/1

N2 - Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal−1, considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations.

AB - Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal−1, considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations.

KW - Biomass supply chain

KW - CyberGIS

KW - Geodesign

KW - Optimization

KW - Sensitivity analysis

KW - Spatial decision support system

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85020512560&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020512560&partnerID=8YFLogxK

U2 - 10.1016/j.apenergy.2017.03.107

DO - 10.1016/j.apenergy.2017.03.107

M3 - Article

VL - 203

SP - 26

EP - 40

JO - Applied Energy

T2 - Applied Energy

JF - Applied Energy

SN - 0306-2619

ER -