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

Biresh Sahoo (), Hilda Saleh (), Morteza Shafiee, Kaoru Tone () and Joe Zhu
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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

Asia-Pacific Journal of Operational Research (APJOR), 2021, vol. 38, issue 06, 1-27

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.

Keywords: Data envelopment analysis; network DEA; dynamic network DEA; composition approach; static and dynamic efficiencies (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0217595921500044

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