Optimization design method for biofuel resilient supply chain considering node disruption impacts in a two-stage stochastic programming framework

Abstract

As economic globalization accelerates, biofuel supply chain systems are becoming increasingly complex and large-scale, with businesses facing rising uncertainties and an increased risk of disruptions. Designing resilient biofuel supply chains that can withstand these risks while maintaining security and competitiveness has become a major concern and an urgent issue for enterprises. However, due to the lack of effective methods for quantifying and evaluating supply chain disruption risks, existing supply chain design approaches fail to adequately address the problem of mitigating such risks. To address this issue, this paper proposes an improved Node Disruption Impact Index with adjustable parameters, based on cost changes in the supply chain caused by disruptions at different nodes. This index enables the identification of nodes with varying risk levels and provides a means for evaluating disruption impact. The adjustable parameters can be tailored to meet the needs of supply chain enterprises, facilitating a trade-off between economic benefits and supply chain resilience. Furthermore, the paper applies the index to the fluctuation range of node uncertainties and develops a two-stage stochastic programming supply chain optimization model. This model incorporates a mechanism for addressing potential high disruption risks. By applying the model to a biofuel supply chain case in Guangdong Province, the results demonstrate that, when high-risk nodes are interrupted, the proposed model outperforms traditional models in terms of cost and market delivery rate. This confirms the effectiveness of the method in the optimization design of resilient supply chain.

Graphical abstract

Keywords

supply chain disruption / biofuel / optimization design / resilient supply chain

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Competing interests

The authors declare that they have no competing interests.

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 22378304) and funding project of the State Key Laboratory of Chemical Engineering and Low-Carbon Technology (Project No. SKL-ChE-23Z02).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11705-025-2548-z and is accessible for authorized users.

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