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Optimal scale sizes in input–output allocative data envelopment analysis models

  • S.I.: Business Analytics and Operations Research
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Abstract

In production theory, industrial units do business in such a way that they use minimum amount of resources to produce maximum amount of products. So, inefficient units decrease their inputs level and increase their outputs level to meet the efficient frontier. By changing inputs and outputs, achieving an optimal scale size (OSS) in industrial units is one of the most important attempts and has attracted considerable attention among researchers. In this paper, an optimal scale size in input–output allocative DEA model is defined to each production firm in which the costs of inputs and the revenues of outputs are considered. We first rearrange the average-revenue efficiency measure that combines scale and output allocative efficiencies. Next, we simultaneously consider both of inputs and outputs in a new average-cost/revenue efficiency measure (ACRE). It has been shown that the proposed ACRE measure is the ratio of the profitability efficiency to ray average productivity. A numerical heuristic procedure is proposed to calculate a relatively good approximation of the new OSS in a convex and continuous technology set. To illustrate the real applicability of the proposed approach, we use a real case on 39 electricity distribution companies.

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References

  • Amirteimoori, A., & Emrouznejad, A. (2011). Flexible measures in production process: A DEA-based approach. RAIRO Operations Research, 45, 63–74.

    Article  Google Scholar 

  • An, Q., Yang, M., Chu, J., Jie, Wu, & Zhu, Q. (2017). Efficiency evaluation of an interactive system by data envelopment analysis approach. Computers & Industrial Engineering, 103, 17–25.

    Article  Google Scholar 

  • Assani, S., Jiang, J., Cao, R., & Yang, F. (2018). Most productive scale size decomposition for multi-stage systems in data envelopment analysis. Computers & Industrial Engineering, 120, 279–287.

    Article  Google Scholar 

  • Banker, R. D. (1984). Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research, 17, 35–44.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Models for the estimation of technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

    Article  Google Scholar 

  • Baumol, W. J. (1977). On the proper cost tests for natural monopoly in a multiproduct industry. American Economic Review, 67, 809–822.

    Google Scholar 

  • Cesaroni, G., & Giovannola, D. (2015). Average–cost efficiency and optimal scale sizes in non-parametric analysis. European Journal of Operational Research, 242, 121–133.

    Article  Google Scholar 

  • Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functions. Naval Research Logistics Quarterly, 9, 181–186.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Handbook of data envelopment analysis. Norwell, MA: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8.

    Article  Google Scholar 

  • Erbetta, F., & Rappuoli, L. (2008). Optimal scale in the Italian gas distribution industry using data envelopment analysis. Omega, 36(2), 325–336.

    Article  Google Scholar 

  • Lee, C. Y. (2016). Most productive scale size versus demand fulfillment: A solution to the capacity dilemma. European Journal of Operational Research, 248(3), 954–962.

    Article  Google Scholar 

  • Ouellette, P., Quesnel, J. P., & Vigeant, S. (2012). Measuring returns to scale in DEA models when the firm is regulated. European Journal of Operational Research, 220(2), 571–576.

    Article  Google Scholar 

  • Podinovski, V. V. (2017). Returns to scale in convex production technologies. European Journal of Operational Research, 258, 970–982.

    Article  Google Scholar 

  • Sahoo, B. K., Khoveyni, M., Eslami, R., & Chaudhury, P. (2016). Returns to scale and most productive scale size in DEA with negative data. European Journal of Operational Research, 255(2), 545–558.

    Article  Google Scholar 

  • Zhu, Q., Wu, J., & Song, M. (2018). Efficiency evaluation based on data envelopment analysis in the big data context. Computers & Operations Research, 98, 291–300.

    Article  Google Scholar 

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Correspondence to Alireza Amirteimoori.

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Haghighatpisheh, H., Kordrostami, S., Amirteimoori, A. et al. Optimal scale sizes in input–output allocative data envelopment analysis models. Ann Oper Res 315, 1455–1476 (2022). https://doi.org/10.1007/s10479-019-03499-2

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