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Centralized resource allocation DEA models based on revenue efficiency under limited information

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  • Lei Fang

    (Business School, Nankai University, Tainjin City, People’s Republic of China)

Abstract

In this paper, we extend the centralized DEA models by Lozano et al (2011) to allocate resources based on revenue efficiency across a set of DMUs under a centralized decision-making environment. The aim is to allocate resources so as to maximize the total output revenue produced by all the DMUs under limited information. To uncover the sources of total revenue increase from the centralized resource allocation model, we further decompose the aggregate revenue efficiency into three components: the aggregate output-oriented technical efficiency, the aggregate output allocative efficiency and the aggregate revenue re-allocative efficiency. Finally, two empirical data sets are presented to show that our proposed approach is not only an efficient tool to allocate the resources among the DMUs based on the revenue efficiency but additionally provides the central DM with guidance on how to identify the weak areas where more effort should be devoted to improve the total outputs.

Suggested Citation

  • Lei Fang, 2016. "Centralized resource allocation DEA models based on revenue efficiency under limited information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(7), pages 945-952, July.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:7:p:945-952
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    Citations

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    Cited by:

    1. Menghan Chen & Sheng Ang & Lijing Jiang & Feng Yang, 2020. "Centralized resource allocation based on cross-evaluation considering organizational objective and individual preferences," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(2), pages 529-565, June.
    2. Zhu, Qingyuan & Li, Xingchen & Li, Feng & Wu, Jie & Zhou, Dequn, 2020. "Energy and environmental efficiency of China's transportation sectors under the constraints of energy consumption and environmental pollutions," Energy Economics, Elsevier, vol. 89(C).
    3. Xiong, Xi & Yang, Guo-liang & Zhou, De-qun & Wang, Zi-long, 2022. "How to allocate multi-period research resources? Centralized resource allocation for public universities in China using a parallel DEA-based approach," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    4. Bo Hsiao & LihChyun Shu & Ming-Miin Yu & Li-Kang Shen & Ding-Jiun Wang, 2017. "Performance evaluation of the Taiwan railway administration," Annals of Operations Research, Springer, vol. 259(1), pages 119-156, December.
    5. Lin, Winston T. & Chen, Yueh H. & Hung, TingShu, 2019. "A partial adjustment valuation approach with stochastic and dynamic speeds of partial adjustment to measuring and evaluating the business value of information technology," European Journal of Operational Research, Elsevier, vol. 272(2), pages 766-779.
    6. Feng Li & Qingyuan Zhu & Liang Liang, 2019. "A new data envelopment analysis based approach for fixed cost allocation," Annals of Operations Research, Springer, vol. 274(1), pages 347-372, March.
    7. Arocena, Pablo & Cabasés, Fermín & Pascual, Pedro, 2022. "A centralized directional distance model for efficient and horizontally equitable grants allocation to local governments," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    8. Liesiö, Juuso & Andelmin, Juho & Salo, Ahti, 2020. "Efficient allocation of resources to a portfolio of decision making units," European Journal of Operational Research, Elsevier, vol. 286(2), pages 619-636.
    9. Sheng Dai & Natalia Kuosmanen & Timo Kuosmanen & Juuso Liesio, 2023. "Optimal resource allocation: Convex quantile regression approach," Papers 2311.06590, arXiv.org.

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