Composition versus decomposition in two-stage network DEA: a reverse approach

Despotis, Dimitris K.; Koronakos, Gregory; Sotiros, Dimitris

doi:10.1007/s11123-014-0415-x

Cited by 109 publications

(67 citation statements)

References 16 publications

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“…Chen et al (2009) introduce the additive efficiency decomposition approach wherein the OE is expressed as a sum of the efficiencies of the individual stages, which can be applied under both constant returns to scale and variable returns to scale assumptions. However, Chen's model has the drawback that biased toward the second stage (Despotis, Koronakos, & Sotiros, ). A new approach to estimate the unique and unbiased efficiency scores for the individual stages is developed by Despotis et al () and is extended by Ang and Chen ().…”

Section: Literature Reviewmentioning

confidence: 99%

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

Kuo

Nguyen

et al. 2020

Manage Decis Econ

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Section: Literature Reviewmentioning

confidence: 99%

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

Kuo

Nguyen

et al. 2020

Manage Decis Econ

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“…The network models are composed of intermediate measures among the divisions. Put differently, the divisions in the network models are interdependent and intermediate measures produced by the preceded division may be consumed as an input by other divisions (see e.g., Prieto and Zofio, 2007;Kao and Hwang, 2008;Chen et al, 2010;Fukuyama and Weber, 2010;Herrera-Restrepo et al, 2016;Despotis et al, 2016). The network DEA models have been initially proposed by Färe and Whittaker (1995) and Färe and Grosskopf (1996) based on the two-stage process and later generalized to multiple processes by Färe and Grosskopf (2000).…”

Section: Introductionmentioning

confidence: 99%

“…Agrell and Hatami-Marbini (2013) zeroed in on performance analysis in SCM, particularly the methodological studies made by way of two-stage models and the related state-of-the-art was categorized into three groups; (i) two-stage process DEA models, (ii) game theory DEA models, and (iii) bi-level programming. The two-stage models are the special case of multi-stage framework where each DMU is composed of two divisions (see e.g., Chen et al, 2009;Wang and Chin, 2010;Kao and Hwang, 2008;Despotis et al, 2016). The game theory DEA models use the concept of non-cooperative and cooperative games in game theory to treat the network structure of operations (see e.g., Liang et al 2006;Zha et al 2010, Du et al 2011.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking with network dea in a fuzzy environment

Hatami-Marbini

2019

RAIRO-Oper. Res.

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Abstract:Benchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has practically and theoretically developed in distinct fields such as banking, education, health and so on, supply chain benchmarking across multiple echelons that includes certain characteristics such as intermediate measure differs from other fields. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying the organizations. We finally provide a case study of the airport and travel sector to elucidate the details of the proposed method in this study. All additions and changes to the original manuscript are highlighted in this revision. AbstractBenchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has practically and theoretically developed in distinct fields such as banking, education, health and so on, supply chain benchmarking across multiple echelons that includes certain characteristics such as intermediate measure differs from other fields. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying ...

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“…In general, DEA researchers employ two different approaches to evaluating efficiency of two‐stage systems: defining ratio efficiency or using possible production set (PPS). Various models have been developed for multiplier and envelopment two‐stage networks (Chen, Cook, & Zhu, ; Despotis, Koronakos, & Sotiros, ; Guo, Abbasi Shureshjani, Foroughi, & Zhu, ; Kao & Hwang, ; Kao & Hwang, ; Rho & An, ; Tone & Tsutsui, ) based on these two approaches. However, these models still demonstrate deficiencies in evaluating overall efficiency, divisional efficiency, and projection (benchmark DMU; Chen, Cook, Kao, & Zhu, ).…”

Section: Introductionmentioning

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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Composition versus decomposition in two-stage network DEA: a reverse approach

Cited by 109 publications

References 16 publications

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

Benchmarking with network dea in a fuzzy environment

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

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