An Application of GIS-Linked Biofuel Supply Chain Optimization Model for Various Transportation Network Scenarios in Northern Great Plains (NGP), USA

Jeong, Hyunju; Karim, Rifat A.; Sieverding, Heidi L.; Stone, Jeff

doi:10.1007/s12155-020-10223-7

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…Pipelines are more economical and have less GHG emissions compared with road and rail transportation modes 38,39 . However, only Jeong et al 29 . included pipelines in their GIS‐based supply chain model.…”

Section: Introductionmentioning

confidence: 99%

“…Mixed‐integer linear programming (MILP) is the most widely used technique for modeling BSC 23 . Several contemporary BSC modeling studies combined geographical information system (GIS) with MILP models to configure spatially distributed supply chains 27–33 . In a centralized supply chain configuration, higher economies of scale can be obtained with fewer and larger processing facilities, which also implies higher upstream transportation costs owing to the mobilization of biomass from dispersed and distanced feedstock locations 27,34 .…”

Section: Introductionmentioning

confidence: 99%

“…37 Pipelines are more economical and have less GHG emissions compared with road and rail transportation modes. 38,39 However, only Jeong et al 29 included pipelines in their GIS-based supply chain model. As a result, the majority of the GIS-based SAF supply chain studies in the USA might have potentially missed the minimum cost and emission route options to transfer the refined SAF from the distributed biorefineries to the centralized airports.…”

mentioning

confidence: 99%

“…23 Several contemporary BSC modeling studies combined geographical information system (GIS) with MILP models to configure spatially distributed supply chains. [27][28][29][30][31][32][33] In a centralized supply chain configuration, higher economies of scale can be obtained with fewer and larger processing facilities, which also implies higher upstream transportation costs owing to the mobilization of biomass from dispersed and distanced feedstock locations. 27,34 Therefore, a distributed supply chain is often suggested as a trade-off between the economies of scale and transportation costs by establishing smaller facilities closer to the biomass resources, 34 which can also be upscaled with a higher number of or larger facilities.…”

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confidence: 99%

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Designing a GIS‐based supply chain for producing carinata‐based sustainable aviation fuel in Georgia, USA

Ullah

Masum

Field

et al. 2023

Biofuels Bioprod Bioref

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Carinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost-effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata-based SAF production. This study aims to design a supply chain model for carinata-based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed-integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GISbased network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Designing a GIS‐based supply chain for producing carinata‐based sustainable aviation fuel in Georgia, USA

Ullah

Masum

Field

et al. 2023

Biofuels Bioprod Bioref

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“…Some scholars have employed the Back Propagation Neural Network (BPNN) to evaluate the risk of specific links in the supply chain. Jeong et al believed that the BPNN risk prediction model dramatically improved the work efficiency in product logistics transmission and ameliorated problems, such as missed and wrong shipments [5]. Shao et al analyzed fruit and vegetable production, logistics, and sales risk indicators through the BPNN model.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

2022

Wireless Communications and Mobile Computing

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The present work is aimed at solving the investment risks in the supply chain management (SCM) process of enterprises. Therefore, the Back Propagation Neural Network (BPNN) algorithm, logistic regression analysis, and other related theories are used for the risk prediction analysis of supply chain samples. Firstly, 40 pieces of supply chain training data are collected as research samples. Secondly, the examples are trained using the BPNN algorithm. Meanwhile, a logistic regression model is constructed based on Principal Component Analysis (PCA). Finally, the two models conduct risk prediction on the test samples. The results indicate that the BPNN model can effectively predict various risks in the SCM process. It achieves an excellent evaluation effect of single risks, consistent with the actual results. Still, there are some deviations between the prediction results and the actual results of mixed risks. When the significance P value is more than 0.5, the sample is predicted to be of high risk. When it is less than 0.5, the sample is predicted to be of low risk. The prediction accuracy of the logistic regression model is as high as 92.8%, demonstrating brilliant applicability and popularization in the investment risk prediction of the supply chain. The BPNN algorithm and logistic regression model can precisely predict the investment risk in SCM and provide a reference for the improved SCM and the sustainable and stable development of enterprises in the supply chain.

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A detailed GIS based assessment of bioenergy plant locations using location-allocation algorithm

Akca,

Sarikaya,

Doker

et al. 2023

Applied Energy

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An Application of GIS-Linked Biofuel Supply Chain Optimization Model for Various Transportation Network Scenarios in Northern Great Plains (NGP), USA

Cited by 6 publications

References 29 publications

Designing a GIS‐based supply chain for producing carinata‐based sustainable aviation fuel in Georgia, USA

Designing a GIS‐based supply chain for producing carinata‐based sustainable aviation fuel in Georgia, USA

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

A detailed GIS based assessment of bioenergy plant locations using location-allocation algorithm

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