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Modeling and optimization of bioethanol production planning under hybrid uncertainty: A heuristic multi-stage stochastic programming approach

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  • Li, Xinchao
  • Lu, Shan
  • Li, Zhe
  • Wang, Yue
  • Zhu, Li

Abstract

When making planning decisions, fluctuation in both product demand and yield for the bioethanol plant denotes an important factor to affect the results. In this paper, a mixed multi-stage stochastic optimization model is proposed to solve the production planning problem of a bioethanol plant with multiple raw materials simultaneously considering uncertain demand and yield. The multi-source uncertainty is described as a multi-layer hybrid scenario tree model according to the historical statistics. Due to the diversity of pretreatment approach in the bioethanol production process, the yield of saccharification liquid is different. At each node of the scenario tree, the uncertainty of demand is expressed by probability. The uncertain scenarios of the yield are integrated at each node of demand to form a mixed scenario tree. To improve the solution efficiency, a heuristic algorithm based on fast forward selection is designed to simplify the decomposition of the large-scale hybrid scenario tree in the mixed multi-stage stochastic programming problem. The effectiveness of the proposed model is verified by a practical case. The results show that the proposed approach performs better than the two-stage stochastic programming and the deterministic approaches, and the algorithm can significantly reduce the computational efforts.

Suggested Citation

  • Li, Xinchao & Lu, Shan & Li, Zhe & Wang, Yue & Zhu, Li, 2022. "Modeling and optimization of bioethanol production planning under hybrid uncertainty: A heuristic multi-stage stochastic programming approach," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001888
    DOI: 10.1016/j.energy.2022.123285
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    Cited by:

    1. Tianwen Chen & Changqing Liu & Xiang Xu, 2022. "Coordination of Perishable Product Supply Chains with a Joint Contract under Yield and Demand Uncertainty," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    2. Mojtaba Farrokh & Ehsan Ahmadi & Minghe Sun, 2023. "A robust stochastic possibilistic programming model for dynamic supply chain network design with pricing and technology selection decisions," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1082-1120, September.
    3. Zhang, Houwang & Wu, Qiuwei & Chen, Jian & Lu, Lina & Zhang, Jiangfeng & Zhang, Shuyi, 2023. "Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming," Energy, Elsevier, vol. 270(C).
    4. Wang, Bangyan & Wang, Xiuli & Wei, Fengting & Shao, Chengcheng & Zhou, Jiahao & Lin, Jintian, 2023. "Multi-stage stochastic planning for a long-term low-carbon transition of island power system considering carbon price uncertainty and offshore wind power," Energy, Elsevier, vol. 282(C).

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