元数据包络分析:寻找边际利润最大化的方向

Chia-Yen Lee
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引用次数: 51

摘要

本文讨论了一种新的元dea方法来解决估计方向距离函数时方向向量的选择问题。该模型强调寻找生产率提高的“方向”,而不是估计效率的“分数”;重“规划”轻“评价”。实际上,边际利润最大化的方向意味着一个逐步改进和“观望”的决策过程,这更符合实际的决策过程。对2011年运行的美国燃煤电厂的实证研究验证了所提出的模型。结果表明,除利润最大化方向外,采用该方向的效率测度与其他指标基本一致。我们得出结论,边际利润最大化是确定定向距离函数方向的有用指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meta-Data Envelopment Analysis: Finding a Direction Towards Marginal Profit Maximization
This paper discusses a new meta-DEA approach to solve the problem of choosing direction vectors when estimating the directional distance function. The proposed model emphasizes finding the “direction” for productivity improvement rather than estimating the “score” of efficiency; focusing on “planning” over “evaluation”. In fact, the direction towards marginal profit maximization implies a step-by-step improvement and “wait-and-see” decision process, which is more consistent with the practical decision-making process. An empirical study of U.S. coal-fired power plants operating in 2011 validates the proposed model. The results show that the efficiency measure using the proposed direction is consistent with all other indices with the exception of the direction towards the profit-maximized benchmark. We conclude that the marginal profit maximization is a useful guide for determining direction in the directional distance function.
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