李焕欢, 纪颖, 屈绍建. 非对称成本环境下两阶段随机成本共识模型的研究[J]. 电子科技大学学报社科版, 2022, 24(2): 103-112. DOI: 10.14071/j.1008-8105(2021)-3017
引用本文: 李焕欢, 纪颖, 屈绍建. 非对称成本环境下两阶段随机成本共识模型的研究[J]. 电子科技大学学报社科版, 2022, 24(2): 103-112. DOI: 10.14071/j.1008-8105(2021)-3017
LI Huan-huan, JI Ying, QU Shao-jian. Research on Two-stage Stochastic Cost Consensus Models in an Asymmetric Cost Context[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2022, 24(2): 103-112. DOI: 10.14071/j.1008-8105(2021)-3017
Citation: LI Huan-huan, JI Ying, QU Shao-jian. Research on Two-stage Stochastic Cost Consensus Models in an Asymmetric Cost Context[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2022, 24(2): 103-112. DOI: 10.14071/j.1008-8105(2021)-3017

非对称成本环境下两阶段随机成本共识模型的研究

Research on Two-stage Stochastic Cost Consensus Models in an Asymmetric Cost Context

  • 摘要:
    目的/意义针对已有的成本共识模型都是基于对称的调整成本和确定的决策环境提出的,通过引入随机情景,研究了非对称成本背景下的两阶段随机成本共识模型。
    设计/方法首先,考虑到方向约束、妥协限度和无成本调整阈值,基于决策者各种不确定性因素构建三类两阶段随机成本共识模型。其次,考虑到模型求解的困难程度,设计L形算法来进行求解。最后,将模型应用于中国“退耕还林”政策的背景下。
    结论/发现数值实验表明所提出的模型具有较强的实用性,同时,对比分析和灵敏度分析也验证模型的稳健性。

     

    Abstract: Purpose/Significance In view of the fact that the existing cost consensus models that are all based on symmetric adjustment cost and deterministic decision environment, this paper studies two-stage stochastic cost consensus models under the background of asymmetric cost by introducing stochastic scenarios. Design/Methodology Firstly, considering directional constraints, compromise limits and cost-free adjustment thresholds, three kinds of two-stage stochastic cost consensus models are constructed based on various uncertainty factors of decision-makers. Secondly, the L-shape algorithm is designed to solve the problem considering the difficulty of solving the proposed models. Finally, the model is applied to the background of the “Grains to Green” afforestation program in China. Conclusions/Findings The numerical experiments show that the models have a strong practicability. In addition, the comparative analysis and sensitivity analysis also verify the robustness of the proposed models.

     

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