利用网络交叉效率 DEA 和后悔理论对伊朗航空公司进行评估

IF 2.4 Q3 TRANSPORTATION
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引用次数: 0

摘要

网络数据包络分析法(NDEA)已被广泛应用于航空运输业的评估。NDEA 为评估决策单元(DMU)的内部流程提供了一种工具。优化网络交叉效率(ONCE)最近已扩展到基本的两阶段系统。然而,仍有两个主要缺陷需要解决。首先,ONCE 仅从乐观角度评估 DMU,忽略了悲观角度。乐观观点假定只有一组参考点,其中包括最佳实践 DMU。本研究的第一个贡献是开发了一种新的悲观网络交叉效率(PNCE)方法。该方法基于一组新的参考点,其中包括表现最差的 DMU。PNCE 是作为 ONCE 的扩展而开发的。其次,ONCE 和新开发的 PNCE 方法都可能导致不切实际的结果,因为它们忽视了决策者(DMs)的主观偏好。这些 NDEA 模型采用算术平均法(AM)作为交叉评价汇总方法,这不仅低估了自我评价的重要性,也高估了同行评价的重要性。因此,ONCE 和 PNCE 可能会导致效率结果出现偏差。针对这一缺陷,本研究的第二个贡献是开发了一种基于后悔理论和共识(ARC)的新聚合方法。该方法旨在估算交叉评估权重时反映 DM 的心理偏好。为实现这一目标,我们利用新开发的 ONCE-ARC 和 PNCE-ARC 方法获得了新的乐观和悲观效率。随后,作为一种更全面的 NDEA,我们开发了一种带 ARC 的双前沿网络交叉效率(DFNCE-ARC)。最后,对一组伊朗航空公司的性能进行了实际应用评估,证明了 DFNCE-ARC 的有用性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.

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