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Promoting symmetric weight selection in data envelopment analysis: A penalty function approach

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  • Dimitrov, Stanko
  • Sutton, Warren

Abstract

Traditionally, data envelopment analysis models assume total flexibility in weight selection, though this assumption can lead to several variables being ignored in determining the efficiency score. Existing methods constrain weight selection to a predefined range, thus removing possible feasible solutions. As such, in this paper we propose the symmetric weight assignment technique (SWAT) that does not affect feasibility and rewards decision making units (DMUs) that make a symmetric selection of weights. This allows for a method of weight restrictions that does not require preference constraints on the variables. Moreover, we show that the SWAT method may be used to differentiate among efficient DMUs.

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  • Dimitrov, Stanko & Sutton, Warren, 2010. "Promoting symmetric weight selection in data envelopment analysis: A penalty function approach," European Journal of Operational Research, Elsevier, vol. 200(1), pages 281-288, January.
    • Handle: RePEc:eee:ejores:v:200:y:2010:i:1:p:281-288
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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. 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.
    3. Aneirson Francisco Silva & Fernando Augusto S. Marins & Erica Ximenes Dias, 2020. "Improving the discrimination power with a new multi-criteria data envelopment model," Annals of Operations Research, Springer, vol. 287(1), pages 127-159, April.
    4. Ahn, Heinz & Neumann, Ludmila & Vazquez Novoa, Nadia, 2012. "Measuring the relative balance of DMUs," European Journal of Operational Research, Elsevier, vol. 221(2), pages 417-423.
    5. Dariush Akbarian, 2020. "Avoiding dissimilarity between the weights of the optimal DEA solutions," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 364-375, June.
    6. Qing Wang & Zhaojun Liu & Yang Zhang, 2017. "A Novel Weighting Method for Finding Common Weights in DEA," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-21, October.
    7. Dimitrov Stanko, 2014. "Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach," Stochastics and Quality Control, De Gruyter, vol. 29(2), pages 129-141, December.
    8. Soltanifar, Mehdi & Shahghobadi, Saeid, 2013. "Selecting a benevolent secondary goal model in data envelopment analysis cross-efficiency evaluation by a voting model," Socio-Economic Planning Sciences, Elsevier, vol. 47(1), pages 65-74.
    9. Javier Fiallos & Jonathan Patrick & Wojtek Michalowski & Ken Farion, 2017. "Using data envelopment analysis for assessing the performance of pediatric emergency department physicians," Health Care Management Science, Springer, vol. 20(1), pages 129-140, March.

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