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DEA Models with Production Trade-offs and Weight Restrictions

In: Data Envelopment Analysis

Author

Listed:
  • Victor V. Podinovski

    (University of Warwick)

Abstract

There is a large literature on the use of weight restrictions in multiplier DEA models. In this chapter we provide an alternative view of this subject from the perspective of dual envelopment DEA models in which weight restrictions can be interpreted as production trade-offs. The notion of production trade-offs allows us to state assumptions that certain simultaneous changes to the inputs and outputs are technologically possible in the production process. The incorporation of production trade-offs in the envelopment DEA model, or the corresponding weight restrictions in the multiplier model, leads to a meaningful expansion of the model of production technology. The efficiency measures in DEA models with production trade-offs retain their traditional meaning as the ultimate and technologically realistic improvement factors. This overcomes one of the known drawbacks of weight restrictions assessed using other methods. In this chapter we discuss the assessment of production trade-offs, provide the corresponding theoretical developments and suggest computational methods suitable for the solution of the resulting DEA models.

Suggested Citation

  • Victor V. Podinovski, 2015. "DEA Models with Production Trade-offs and Weight Restrictions," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 5, pages 105-144, Springer.
  • Handle: RePEc:spr:isochp:978-1-4899-7553-9_5
    DOI: 10.1007/978-1-4899-7553-9_5
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    Citations

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    Cited by:

    1. Papaioannou, Grammatoula & Podinovski, Victor V., 2023. "Multicomponent production technologies with restricted allocations of shared inputs and outputs," European Journal of Operational Research, Elsevier, vol. 308(1), pages 274-289.
    2. Ströhl, Florian & Borsch, Erik & Souren, Rainer, 2018. "Integration von Gewichtsrestriktionen in das DEA-Modell nach Charnes, Cooper und Rhodes: Exemplarische Optionen und Auswirkungen," Ilmenauer Schriften zur Betriebswirtschaftslehre, Technische Universität Ilmenau, Institut für Betriebswirtschaftslehre, volume 3, number 32018.
    3. Podinovski, Victor V. & Bouzdine-Chameeva, Tatiana, 2016. "On single-stage DEA models with weight restrictions," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1044-1050.
    4. Podinovski, Victor V. & Bouzdine-Chameeva, Tatiana, 2019. "Cone extensions of polyhedral production technologies," European Journal of Operational Research, Elsevier, vol. 276(2), pages 736-743.
    5. Robin C. Sickles & Wonho Song & Valentin Zelenyuk, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," CEPA Working Papers Series WP082018, School of Economics, University of Queensland, Australia.
    6. Bogetoft, Peter & Wittrup, Jesper, 2021. "Benefit-of-the-doubt approach to workload indicators: Simplifying the use of case weights in court evaluations," Omega, Elsevier, vol. 103(C).
    7. A. Guerrini & G. Romano & L. Carosi & F. Mancuso, 2017. "Cost Savings in Wastewater Treatment Processes: the Role of Environmental and Operational Drivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(8), pages 2465-2478, June.
    8. Førsund, Finn & Krivonozhko, Vladimir W & Lychev, Andrey V., 2016. "Smoothing the frontier in the DEA models," Memorandum 11/2016, Oslo University, Department of Economics.
    9. Podinovski, Victor V., 2016. "Optimal weights in DEA models with weight restrictions," European Journal of Operational Research, Elsevier, vol. 254(3), pages 916-924.
    10. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    11. Podinovski, Victor V., 2017. "Returns to scale in convex production technologies," European Journal of Operational Research, Elsevier, vol. 258(3), pages 970-982.
    12. Martin Bod’a & Martin Dlouhý & Emília Zimková, 2018. "Unobservable or omitted production variables in data envelopment analysis through unit-specific production trade-offs," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 813-846, December.
    13. Alireza Amirteimoori & Biresh K. Sahoo & Saber Mehdizadeh, 2023. "Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.

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