Inverse two-stage data envelopment analysis with interval measures for resource planning

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-12-14 DOI:10.1108/jm2-02-2023-0044
Yasaman Zibaei Vishghaei, Sohrab Kordrostami, Alireza Amirteimoori, Soheil Shokri
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Abstract

Purpose

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Design/methodology/approach

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

Findings

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

Originality/value

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

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用于资源规划的反两阶段数据包络分析与区间计量法
目的 评估输入和输出是决策的一个重要方面,而在许多考试中都存在复杂的多阶段过程。由于在各种实际研究中存在区间绩效衡量,本研究的目的是在保持总体效率分数的前提下,解决两阶段过程的区间输入对两阶段系统的区间输出扰动的变化问题。为了说明这一点,我们提出了一个具有外部区间输入和输出的区间两阶段网络 DEA 模型及其逆问题,以估计随着区间输入的变化而变化的整体效率和阶段效率的上下限。原创性/价值 据作者所知,在保持网络流程区间效率的情况下,还没有研究针对不精确输出的变化估算与网络结构相关的不精确输入的波动。因此,本文考虑了两级网络中存在不精确和区间措施时的资源规划问题。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
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.
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