Integrated data envelopment analysis: Global vs. local optimum

Lim, Sungmook; Zhu, Joe

doi:10.1016/j.ejor.2013.02.023

Cited by 42 publications

(37 citation statements)

References 8 publications

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“…Differently form Chen et al (2009a), these authors do not linearize their model but claim the existence and the uniqueness of optimal weights μ js , υ ls , ν if , ω f and ω s . Unfortunately, Lim and Zhu (2013) show that such a conclusion is a false statement. They also show how the two-stage DEA model proposed in Chiou and Lan (2007) can be transformed into a parametric linear program.…”

Section: Variable Returns To Scale and Additive Measures Of Efficiencymentioning

confidence: 86%

Network, Shared Flow and Multi-level DEA Models: A Critical Review

Castelli

Pesenti

2014

International Series in Operations Research &Amp; Management Science

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In the last two decades, complex and detailed DEA models that consider the internal structure of DMUs have been proposed by several authors. This chapter describes the mathematical formulations, along with their main variants, extensions and applications, of three large and popular model families: network (with special emphasis on multi-stage), shared flow (also known as multi-component or multi-activity), and multi-level models. Each family is a different generalization of the same elementary internal structure. This review extends and updates the classification presented in Castelli et al. (Ann Oper Res 173(1):207–235, 2010)

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Section: Variable Returns To Scale and Additive Measures Of Efficiencymentioning

confidence: 86%

Network, Shared Flow and Multi-level DEA Models: A Critical Review

Castelli

Pesenti

2014

International Series in Operations Research &Amp; Management Science

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“…Notwithstanding, their developments have serious flaws. Despite they claim any optimal solutions determined by their DEA model are global, but Lima and Zhu (2013) indicated that such a conclusion is a false statement due to their misuse of Hessian matrix in examining concavity of objective function, and their DEA model is actually a non-convex optimisation problem. As a result, their DEA model is unusable.…”

Section: Literature Reviewmentioning

confidence: 88%

Concurrent estimation of efficiency, effectiveness and returns to scale

Khodakarami

Shabani

Saen

2014

International Journal of Systems Science

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In recent years, data envelopment analysis (DEA) has been widely used to assess both efficiency and effectiveness. Accurate measurement of overall performance is a product of concurrent consideration of these measures. There are a couple of well-known methods to assess both efficiency and effectiveness. However, some issues can be found in previous methods. The issues include non-linearity problem, paradoxical improvement solutions, efficiency and effectiveness evaluation in two independent environments: dividing an operating unit into two autonomous departments for performance evaluation and problems associated with determining economies of scale. To overcome these issues, this paper aims to develop a series of linear DEA methods to estimate efficiency, effectiveness, and returns to scale of decision-making units (DMUs), simultaneously. This paper considers the departments of a DMU as a united entity to recommend consistent improvements. We first present a model under constant returns to scale (CRS) assumption, and examine its relationship with one of existing network DEA model. We then extend model under variable returns to scale (VRS) condition, and again its relationship with one of existing network DEA models is discussed. Next, we introduce a new integrated two-stage additive model. Finally, an in-depth analysis of returns to scale is provided. A case study demonstrates applicability of the proposed models.

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“…Through multiple tests, the results from this model were highly robust with different initial variable values, and the calculation also showed good convergence and high accuracy. Therefore, we have a good reason to believe that the calculation result from this model is the global optimal solution rather than the local optimal solution of our optimization problem [34][35][36].…”

Section: Dea Model With Dual Target Variablementioning

confidence: 99%

Total Factor Efficiency Study on China’s Industrial Coal Input and Wastewater Control with Dual Target Variables

Yang

2018

Sustainability

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After decades of economic development in China, its energy consumption still relies heavily on coal, and the dominant segment of our economy-the industrial sectors with large coal input-have caused massive wastewater pollution. To provide a decision-making basis for the sustainable development of Chinese industry and economy, this paper selected 39 industrial sectors in China and studied their Total Factor Efficiency (TFE) using Dual Target Variables of coal input and wastewater control during 2003-2014. The results showed that 82.05% of the total industries within our study scope had an efficiency level below 0.5. Over the entire study period of 2003-2014, only the national monopoly industries and high and new tech industries were able to maintain the optimal efficiency level. In comparison with the existing Data Envelope Analysis (DEA) Model and studies on TFE using multi-target variables methods, this paper has probably made two improvements. First, we improved the original DEA model to calculate the TFE of two target variables (coal input and wastewater control) at the same time. Second, we developed an algorithm to calculate the TFE of industrial coal input and wastewater control from 2003 to 2014, based on the official input-output statistics of China.

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Integrated data envelopment analysis: Global vs. local optimum

Cited by 42 publications

References 8 publications

Network, Shared Flow and Multi-level DEA Models: A Critical Review

Network, Shared Flow and Multi-level DEA Models: A Critical Review

Concurrent estimation of efficiency, effectiveness and returns to scale

Total Factor Efficiency Study on China’s Industrial Coal Input and Wastewater Control with Dual Target Variables

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