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An Alternative Approach to Dealing with the Composition Approach for Series Network Production Processes

Author

Listed:
  • Biresh K. Sahoo

    (Xavier Institute of Management, Xavier University Bhubaneswar, Bhubaneswar 751013, India)

  • Hilda Saleh

    (Department of Mathematics, Faculty of Science, Central Tehran Branch, Islamic Azad University, Tehran, Iran)

  • Morteza Shafiee

    (Department of Industrial Management, Economic and Management Faculty, Shiraz Branch, Islamic Azad University, Shiraz, Iran)

  • Kaoru Tone

    (National Graduate Institute for Policy Studies, 7-22-1 Roppongi, Minato-ku, Tokyo 106-8677, Japan)

  • Joe Zhu

    (College of Auditing and Evaluation, Nanjing Audit University, Nanjing, Jiangsu Province 211815, P. R. China6Foisie Business School, Worcester Polytechnic Institute Worcester, MA 01609, USA)

Abstract

To deal with efficiency assessment in a two-stage production process, Despotis et al. (2016b) proposed an innovative composition approach to calculate the two-stage efficiencies, whose minimum is defined as overall efficiency of the network production process. The underlying proposed programming method in this approach is nonlinear and requires the application of a very time-consuming bi-section procedure to obtain the stage efficiencies. In this paper, we have, therefore, proposed a simple linear network DEA model to carry out the same stage efficiency assessment, which is computationally efficient, and is also readily applicable to any multi-stage production process. Additionally, unlike the Despotis et al.’s (2016b) method, our proposed alternative is extended to deal with dynamic efficiency assessment. Finally, we have considered both synthetic and real-life data to demonstrate the ready applicability of our proposed models for both static and dynamic efficiency assessments.

Suggested Citation

  • Biresh K. Sahoo & Hilda Saleh & Morteza Shafiee & Kaoru Tone & Joe Zhu, 2021. "An Alternative Approach to Dealing with the Composition Approach for Series Network Production Processes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(06), pages 1-27, December.
    • Handle: RePEc:wsi:apjorx:v:38:y:2021:i:06:n:s0217595921500044
    DOI: 10.1142/S0217595921500044
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    Cited by:

    1. Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.

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