Decisional performance efficiencies of cross-efficiency DEA decision-making units using benevolent criterion

Parag C. Pendharkar ()
Additional contact information
Parag C. Pendharkar: Pennsylvania State University at Harrisburg

Operational Research, 2025, vol. 25, issue 1, No 18, 29 pages

Abstract: Abstract This paper uses the maximum decisional efficiency (MDE) principle to rank decisional performance efficiencies of cross-efficiency (CE) data envelopment analysis (DEA) decision-making units (DMUs) that use benevolent criterion. Under the benevolent criterion, each DMU independently selects a set of common weights to maximize the average peer efficiency scores. The performance of each DMU in selecting the set of common weights is called the decisional performance efficiency (DPE) of the DMU. The DPEs of all DMUs are assumed to be distributed as a monotone increasing probability density function (pdf) of unknown parameters. A maximum-likelihood (ML) approach using a genetic algorithm is used to learn the parameters of the pdf. Once the underlying parameters of the pdf are known, DPEs can be computed and the DMUs can be ranked based on their DPEs. Additionally, the Markov Chain Monte Carlo technique can be applied to establish confidence interval bounds on the ML parameter describing the MDE pdf. Three examples using datasets from the literature are provided.

Keywords: Data envelopment analysis; Genetic algorithms; Cross efficiency; Markov chain Monte Carlo; Decisional performance efficiency (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12351-024-00890-1 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:operea:v:25:y:2025:i:1:d:10.1007_s12351-024-00890-1

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-024-00890-1

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

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