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Research on Risk-Averse Procurement Optimization of Emergency Supplies for Mine Thermodynamic Disasters

Author

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  • Weimei Li

    (School of Business Administration, Liaoning Technical University, Huludao 125105, China
    Institute for Optimization and Decision Analytics, Liaoning Technical University, Fuxin 123000, China)

  • Leifu Gao

    (Institute for Optimization and Decision Analytics, Liaoning Technical University, Fuxin 123000, China)

Abstract

Reducing procurement risks to ensure the supply of emergency supplies is crucial for mitigating the losses caused by mine thermodynamic disasters. The risk preference of decision-makers and supply chain collaboration are the important aspects for this reductiom. In this study, a novel P-CVaR (Piecewise conditional risk value) distributionally robust optimization model is proposed to accurately assist the decision-makers’ decision of risk preference for reducing procurement risks. Meanwhile, the role of cooperation between procurement and reserves are considered for the weakening procurement risks. A risk-averse bi-level optimization model is proposed to obtain the optimal procurement strategy. Furthermore, by applying the Lagrange duality theorem, the complexity of the bi-level optimization model is simplified then solved using a PSO algorithm. Using empirical analysis, it has been verified that the model presented in this paper serves as a valuable guideline for mine thermodynamic pre-disaster emergency material procurement strategies for the prevention of thermodynamic disasters.

Suggested Citation

  • Weimei Li & Leifu Gao, 2024. "Research on Risk-Averse Procurement Optimization of Emergency Supplies for Mine Thermodynamic Disasters," Mathematics, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2222-:d:1436344
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    References listed on IDEAS

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    1. Yu Feng, 2019. "Theory and Application of Model Risk Quantification," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2019, January-A.
    2. Jiang-Hua Zhang & Xiao-Qing Sun & Rui Zhu & Ming Li & Wang Miao, 2017. "Solving an emergency rescue materials problem under the joint reserves mode of government and framework agreement suppliers," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-20, October.
    3. Yuhong Xu, 2014. "Robust valuation and risk measurement under model uncertainty," Papers 1407.8024, arXiv.org.
    4. Thevenin, Simon & Ben-Ammar, Oussama & Brahimi, Nadjib, 2022. "Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1199-1215.
    5. Cornilly, D. & Rüschendorf, L. & Vanduffel, S., 2018. "Upper bounds for strictly concave distortion risk measures on moment spaces," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 141-151.
    6. Mnatsakanov, Robert M., 2008. "Hausdorff moment problem: Reconstruction of probability density functions," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1869-1877, September.
    7. Lu, Jie & Gupte, Akshay & Huang, Yongxi, 2018. "A mean-risk mixed integer nonlinear program for transportation network protection," European Journal of Operational Research, Elsevier, vol. 265(1), pages 277-289.
    8. Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
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