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Efficiency measurement in multi-period network DEA model with feedback

机译:具有反馈的多时期网络DEA模型中的效率测量

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摘要

Decision-making unit (DMU) internal structure simulation is the basis for network Data Envelopment Analysis (DEA) to open & ldquo;black box & rdquo; and evaluate system efficiency with complex internal structure. Based upon summarizing and analyzing the existing model assumptions in network DEA, this paper proposes a hybrid multiperiod DEA model with feedback to open the internal structure of the DMU system, as well as to provide horizontal comparison of the efficiency change of a same DMU at different time periods. In the model construction, the global production frontier is used for multi-period evaluation, Chebyshev distance is used to construct an unbiased two-stage model. Under the cooperation hypothesis, it is considered that the two stages are equally important, which solves the defect that the current two-stage method is not unique in its optimal solution and has two-stage contribution bias. A binary heuristic algorithm is proposed to reduce the time complexity of model solving while maintaining relatively high accuracy. The correctness and feasibility of the algorithm are demonstrated through the investigation of the relevant properties. Finally, the 5-year ecological data of China is used for illustrative application, providing suggestions for future environmental governance. Several comparative experiments are conducted to demonstrate the advantages of our proposed model.
机译:决策单位(DMU)内部结构模拟是网络数据包络分析(DEA)到开放的基础和LDQUO;黑匣子和rdquo;并评估复杂内部结构的系统效率。基于总结和分析网络DEA的现有模型假设,提出了一种具有反馈的混合多极DEA模型,以打开DMU系统的内部结构,并提供不同DMU在不同的效率变化的水平比较时间段。在模型结构中,全球生产前沿用于多时期评估,Chebyshev距离用于构建一个非偏见的两级模型。在合作假设下,认为这两个阶段同样重要,这解决了当前两级方法在其最佳解决方案中并不具有独特的缺陷,并且具有两阶段的贡献偏差。提出了二元启发式算法,以减少模型解决的时间复杂性,同时保持相对高的精度。通过调查相关性质来证明算法的正确性和可行性。最后,中国的5年生态数据用于说明应用,为未来的环境治理提供建议。进行了几个比较实验以证明我们提出的模型的优势。

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