Inverse data envelopment analysis optimization approaches with flexible measures

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-07-12 DOI:10.1108/jm2-06-2022-0138

Monireh Jahani Sayyad Noveiri, S. Kordrostami, M. Ghiyasi

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引用次数: 0

Abstract

Purpose The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change. Design/methodology/approach A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them. Findings The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements. Originality/value The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.

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具有柔性测度的逆数据包络分析优化方法

目的本研究的目的是在相对效率值不变的情况下，当输出（输入）发生变化时，估计输入（输出）和灵活的措施。设计/方法论/方法本研究提出了一种新的具有柔性测度的反数据包络分析（DEA）方法，用于在相对效率分数不变的情况下，当输出（输入）受到扰动时，评估输入（输出）和柔性测度。此外，本文提出的灵活的逆DEA方法被用于文献中的一个数值例子和伊朗银行业的应用，以澄清和验证它们。调查结果表明，在调查中纳入灵活的衡量标准会影响绩效衡量标准的变化，并导致更合理的结果。原创性/价值传统的反DEA模型通常在假设效率值不变的情况下，针对其他给定投入产出指标的变化，研究一些确定投入产出因素的变化。然而，在某些情况下，绩效指标的变化应该得到解决，而一些被称为灵活指标的指标可以发挥投入或产出的作用。因此，本文提出了具有柔性测度的反DEA优化模型来解决这些问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.