Fuzzy cost, revenue efficiency assessment and target setting in fuzzy DEA: a fuzzy directional distance function approach

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-09-12 DOI:10.1108/jm2-05-2022-0121

Javad Gerami, Mohammad Reza Mozaffari, Peter Wanke, Yong Tan

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引用次数: 0

Abstract

Purpose This study aims to present the cost and revenue efficiency evaluation models in data envelopment analysis in the presence of fuzzy inputs, outputs and their prices that the prices are also fuzzy. This study applies the proposed approach in the energy sector of the oil industry. Design/methodology/approach This study proposes a value-based technology according to fuzzy input-cost and revenue-output data, and based on this technology, the authors propose an approach to calculate fuzzy cost and revenue efficiency based on a directional distance function approach. These papers incorporated a decision-maker’s (DM) a priori knowledge into the fuzzy cost (revenue) efficiency analysis. Findings This study shows that the proposed approach obtains the components of fuzzy numbers corresponding to fuzzy cost efficiency scores in the interval [0, 1] corresponding to each of the decision-making units (DMUs). The models presented in this paper satisfies the most important properties: translation invariance, translation invariance, handle with negative data. The proposed approach obtains the fuzzy efficient targets corresponding to each DMU. Originality/value In the proposed approach, by selecting the appropriate direction vector in the model, we can incorporate preference information of the DM in the process of evaluating fuzzy cost or revenue efficiency and this shows the efficiency of the method and the advantages of the proposed model in a fully fuzzy environment.

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模糊DEA中的模糊成本、收益效率评价与目标设定:一种模糊定向距离函数方法

本研究的目的在于建立模糊投入、产出及其价格存在时的数据包络分析成本与收益效率评价模型。本研究将提出的方法应用于石油工业的能源部门。本研究提出了一种基于模糊投入成本和收益产出数据的价值计算方法，并在此基础上提出了一种基于定向距离函数法的模糊成本和收益效率计算方法。本文将决策者的先验知识引入模糊成本(收益)效率分析中。研究表明，本文提出的方法在[0,1]区间内得到了每个决策单元(dmu)所对应的模糊成本效率分数所对应的模糊数分量。本文提出的模型满足平移不变性、平移不变性、处理负数据等最重要的性质。该方法得到每个DMU对应的模糊有效目标。在本文提出的方法中，通过在模型中选择合适的方向向量，我们可以将DM的偏好信息纳入模糊成本或收益效率的评估过程中，这表明了该方法的有效性和本文提出的模型在全模糊环境下的优势。

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.