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DEA models for non-homogeneous DMUs with different input configurationsAuthor-Name: Li, WangHong

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  • Liang, Liang
  • Cook, Wade D.
  • Zhu, Joe

Abstract

The data envelopment analysis (DEA) methodology is a benchmarking tool where it is generally assumed that decision making units (DMUs) constitute a homogeneous set; specifically, it is assumed that all DMUs have a common (input, output) bundle. In earlier work by the authors the issue of non-homogeneity on the output side was investigated. There we examined a set of steel fabrication plants where not all plants produced the same set of products/outputs. In the current research we investigate non-homogeneity on the input side. Such can occur in manufacturing plants, for example, when the output bundle can be produced using different mixes of machines, robots and laborers. Thus, we can have an input configuration existing in a DMU that is different from the configuration in another DMU. As a practical application of this phenomenon, we examine the measurement of efficiencies of a set of provinces in China. There, all provinces have the same common set of outputs in the form of GDP, supported population, and an undesirable output, nitrogen dioxide. On the input side, however, this commonality is missing. While all provinces have water, capital investment and natural resources, the latter of these (natural resources) takes several different forms, namely coal, natural gas and petroleum. However, not all provinces have the same mix of these resources, nor are there clear exchange rates among these very different, albeit substitutable inputs. This means that that one cannot directly apply the conventional DEA methodology. This then raises the question as to how to fairly evaluate efficiency when the configuration or mix of inputs can differ from one DMU to another. To address this, we view the generation of outputs for a province as a set of processes created by the different configurations of natural resources available. We develop a DEA type of methodology to evaluate these processes. This evaluation provides important insights into not only the overall performance of each province, but as well provides measures of the efficiency of the various configurations of the three natural resources.

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  • Liang, Liang & Cook, Wade D. & Zhu, Joe, 2016. "DEA models for non-homogeneous DMUs with different input configurationsAuthor-Name: Li, WangHong," European Journal of Operational Research, Elsevier, vol. 254(3), pages 946-956.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:3:p:946-956
    DOI: 10.1016/j.ejor.2016.04.063
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    Cited by:

    1. Xiang Ji & Jie Wu & Qingyuan Zhu & Jiasen Sun, 2019. "Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: an empirical study," Annals of Operations Research, Springer, vol. 278(1), pages 281-335, July.
    2. Wu, Jie & Li, Mingjun & Zhu, Qingyuan & Zhou, Zhixiang & Liang, Liang, 2019. "Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs," Energy Economics, Elsevier, vol. 78(C), pages 468-480.
    3. Delis, Manthos D. & Iosifidi, Maria & Tasiou, Menelaos, 2021. "Efficiency of government policy during the COVID-19 pandemic," MPRA Paper 107046, University Library of Munich, Germany.
    4. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    5. Josef Jablonský, 2019. "Data Envelopment Analysis Models in Non-Homogeneous Environment," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 67(6), pages 1535-1540.
    6. Manthos D. Delis & Maria Iosifidi & Menelaos Tasiou, 2023. "Efficiency of government policy during the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 328(2), pages 1287-1312, September.
    7. Zhu, Weiwei & Yu, Yu & Sun, Panpan, 2018. "Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability," European Journal of Operational Research, Elsevier, vol. 269(1), pages 99-110.
    8. Saeedi, Hamid & Behdani, Behzad & Wiegmans, Bart & Zuidwijk, Rob, 2019. "Assessing the technical efficiency of intermodal freight transport chains using a modified network DEA approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 66-86.
    9. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "A parallel DEA-based method for evaluating parallel independent subunits with heterogeneous outputs," Journal of Informetrics, Elsevier, vol. 14(3).
    10. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.

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