A new data envelopment analysis method for ranking decision making units: an application in industrial parks

Izadikhah, Mohammad; Saen, Reza Farzipoor

doi:10.1111/exsy.12112

Cited by 32 publications

(17 citation statements)

References 30 publications

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“…Typical positional features-based ranking methods include super efficiency (SE)-based methods [21][22][23][24], cross efficiency (CE)-based methods [25][26][27], alternative frontier-based methods [28][29][30], efficiency change of inefficient DMU-based methods [31][32][33], virtual DMU-based methods [34][35][36][37][38], and benchmarking importance-based methods [39][40][41].…”

Section: Ranking Methods Using Positional Featuresmentioning

confidence: 99%

“…In a similar vein, Shetty and Pakkala [35] defined a virtual DMU representing the average of inefficient DMUs and evaluate the impact of efficient DMUs on it. Similarly, Izadikhah and Farzipoor Saen [36] proposed a method for fully ranking all DMUs based on the influence of efficient DMUs on other efficient DMUs and a virtual DMU, a proxy of all inefficient DMUs. Additionally, Wang and Yang [37] used interval efficiency of which the lower bound is determined by a virtual anti-ideal DMU, and Azizi and Wang [38] improved the method introduced in [37] to be capable for handling zero-valued output.…”

Section: Ranking Methods Using Positional Featuresmentioning

confidence: 99%

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A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

Lee

Choi

2019

Sustainability

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Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU’s positional features use either the superiority, which considers the efficient DMUs’ relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs’ role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.

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Section: Ranking Methods Using Positional Featuresmentioning

confidence: 99%

Section: Ranking Methods Using Positional Featuresmentioning

confidence: 99%

A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

Lee

Choi

2019

Sustainability

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“…Esmaeilzadeh and Hadi-Vencheh (2015) proposed a procedure for complete ranking of DMUs in DEA. Izadikhah and Saen (2015) also developed a new model for ranking DMUs in DEA.…”

Section: Literature Reviewmentioning

confidence: 99%

Practical benchmarking in DEA using artificial DMUs

2018

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Data envelopment analysis (DEA) is one of the most efficient tools for efficiency measurement which can be employed as a benchmarking method with multiple inputs and outputs. However, DEA does not provide any suggestions for improving efficient units, nor does it provide any benchmark or reference point for these efficient units. Impracticability of these benchmarks under environmental conditions is another challenge of benchmarking by DEA. The current study attempts to extend basic models for benchmarking of efficient units under practical conditions. To this end, we construct the practical production possibility set (PPPS) by employing the concept of artificial decision-making units and adding these decisionmaking units to the production possibility set (PPS) such that these artificial units satisfy all environmental constraints. Then, the theorems related to PPPS and their proofs are provided. Moreover, as a secondary result of this study, efficient units can be ranked according to their practical efficiency scores.

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“…Banker and Morey (1986a) were the first researchers to specifically carry out an investigation on this subject. Many researchers have developed their black box models or network DEA models by considering some of the input-output data as nondiscretionary factors (Banker & Morey, 1986b;Lovell et al, 1994;Ruggiero, 1998;FQrsund, 2002;Tavassoli, Farzipoor Saen, & Faramarzi, 2015;Izadikhah & Farzipoor Saen, 2015). An example of network generalization can be seen in work by Mavi, Saen, and Goh (2018).…”

mentioning

confidence: 99%

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

2019

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Many studies have recently been conducted on the evaluation of system performance with a two‐stage network structure in data envelopment analysis (DEA) literature. One of the topics of interest to researchers has been the mitigation of undesirable products or nondiscretionary factors into their corresponding possible production set (PPS) and their impact on overall efficiency calculations. Determination of decision‐making units (DMUs) with Pareto–Koopmans efficiency status is decisive in identifying benchmark units. The calculated overall efficiency status is compromised when both undesirable products and nondiscretionary factors are present. This work utilizes an axiomatic approach. A novel PPS for a two‐stage network in presence of undesirable intermediate products and nondiscretionary exogenous inputs is introduced. Based on this PPS and by focusing on the principle of mathematical dominance, new models for evaluating overall and divisional efficiencies are presented. In addition, by proposing a two‐step network DEA approach, a necessary and sufficient condition for detection of DMUs with Pareto–Koopmans efficiency status is provided. And by introducing a two‐step algorithm, a novel technique for determining overall efficiency conditions is produced. Finally, the proposed technology is applied to a practical example, and outcomes are discussed.

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A new data envelopment analysis method for ranking decision making units: an application in industrial parks

Cited by 32 publications

References 30 publications

A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

Practical benchmarking in DEA using artificial DMUs

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

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