Using common weights and efficiency invariance principles for resource allocation and target setting
Using common weights and efficiency invariance principles for resource allocation and target setting
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DOI：
10.1080/00207543.2017.1287450
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发表时间：
2017-02
影响因子：
9.2
通讯作者：
Feng Li;Jian Song;A. Dolgui;L. Liang
中科院分区：
文献类型：
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作者：
Feng Li;Jian Song;A. Dolgui;L. Liang
数据包络分析（DEA）已被证明是一种用于评估可比且同质的决策单元（DMU）相对绩效的有用技术。近年来，基于DEA的资源分配和目标设定方法越来越受到从业者和学术研究人员的关注。在本文中，我们提出一种新的机制，在决策单元之间分配多种资源和设定多个目标时同时采用共同权重和效率不变性原则。为了得到最终方案，我们使基于共同权重的可能方案与强调效率不变性的另一个可行方案之间的偏差最小化。如果最小偏差等于零，则将确定一个最优方案。然而，在一般情况下，所提出的方法将呈现出两个偏差不为零的方案。一个是通过为所有决策单元使用一组共同权重生成的，其方式是使效率的变化最小化，而另一个是通过严格保持效率得分不变，但在最大程度上使每个决策单元的投入 - 产出指标具有相似甚至相同的权重生成的。通过先前文献中的一个数值例子以及对中国一家城市公交公司的实证应用，证明了所提出方法的有效性和实用性。
Data envelopment analysis (DEA) has proven to be a useful technique for evaluating the relative performance of comparable and homogeneous decision-making units (DMUs). In recent years, DEA-based resource allocation and target setting approaches have gained more and more attention from both practitioners and academic researchers. In this paper, we propose a new mechanism to simultaneously adopt the principles of common weights and efficiency invariance in allocating multiple resources and setting multiple targets among DMUs. To obtain the final plan, we minimise the deviation between the possible plan based on common weights and another feasible plan emphasising efficiency invariance. If the minimum deviation equals zero, one optimal plan will be determined. In general situations, however, the proposed approach will present two plans that have a non-zero deviation. One is generated using a common set of weights for all DMUs in such a way that the change of efficiencies is minimised, while the other is generated by strictly keeping efficiency scores unchanged yet having similar or even identical weights on input–output measures for each DMU to the utmost extent. The efficacy and usefulness of the proposed approach are demonstrated using a numerical example from previous literature and an empirical application to an urban bus company in China.