New common set of weights method in black-box and two-stage data envelopment analysis

Hamid Kiaei () and Reza Kazemi Matin ()
Additional contact information
Hamid Kiaei: University of Mazandaran
Reza Kazemi Matin: Islamic Azad University

Annals of Operations Research, 2022, vol. 309, issue 1, No 6, 143-162

Abstract: Abstract Data envelopment analysis (DEA) strives to evaluate the production units under their best conditions. DEA flexibility in selecting the appropriate input/output weights always results in unreal and zero weights. Treating decision-making units (DMUs) as black-box regardless of their internal structures misleads the DEA performance evaluation. While considering units as a network process, it is more likely to identify more inefficiency sources. This paper suggests using a new common set of weights (CSWs) approach to evaluate the units in both black-box and two-stage structures based on a unified criterion. Indeed, our contribution to this line of research is as follows: Firstly, we improve the model proposed by Kao and Hung (J Oper res Soc 56(10): 1196–1203, 2005) to calculate the CSWs in a linear-based optimization model. Secondly, a new CSWs method is suggested in the two-stage network DEA (NDEA) as multiple objectives fractional programming (MOFP) problem. Thirdly, the MOFP problem is converted into a single objective linear programming problem in the two-stage network case. Finally, an enlightening application is presented.

Keywords: DEA; Two-stage; Common set of weights; Efficiency (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-021-04304-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:309:y:2022:i:1:d:10.1007_s10479-021-04304-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-021-04304-9

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().