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Generalized symmetric weight assignment technique: Incorporating managerial preferences in data envelopment analysis using a penalty function

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  • Dimitrov, Stanko
  • Sutton, Warren

Abstract

In this paper, we generalize the Symmetric Weight Assignment Technique to incorporate all managerial preferences in Data Envelopment Analysis (DEA). This is a method that promotes managerial preferences, while not changing the feasibility region of the nominal DEA model, unlike standard techniques such as cone ratio and assurance region that potentially yield infeasible problems. We discuss how this generalization is motivated by a real word problem where the decision maker's preference is not precisely known. We also discuss how we are using our generalization in solving a workforce allocation problem for the United States Navy.

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  • Dimitrov, Stanko & Sutton, Warren, 2013. "Generalized symmetric weight assignment technique: Incorporating managerial preferences in data envelopment analysis using a penalty function," Omega, Elsevier, vol. 41(1), pages 48-54.
    • Handle: RePEc:eee:jomega:v:41:y:2013:i:1:p:48-54
    DOI: 10.1016/j.omega.2011.07.012
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    1. Hosein Arman & Abdollah Hadi‐Vencheh, 2021. "Restricting the relative weights in data envelopment analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4127-4136, July.
    2. Halme, Merja & Korhonen, Pekka & Eskelinen, Juha, 2014. "Non-convex value efficiency analysis and its application to bank branch sales evaluation," Omega, Elsevier, vol. 48(C), pages 10-18.
    3. Sutton, Warren & Dimitrov, Stanko, 2013. "The U.S. Navy explores detailing cost reduction via Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 227(1), pages 166-173.
    4. Shabani, Amir & Visani, Franco & Barbieri, Paolo & Dullaert, Wout & Vigo, Daniele, 2019. "Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights," Omega, Elsevier, vol. 87(C), pages 57-70.

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    Keywords

    DEA; Multicriteria; Application;
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