Most Favorable Russell Measures of Efficiency: Properties and Measurement

Chiang Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Conventional radial efficiency measurement models in data envelopment analysis are unable to produce appropriate efficiency scores for production units lying outside the cone generated by the convex hull of the extreme efficient production units. In addition, in the case of production technologies with variable returns to scale, the efficiency scores measured from the input and output sides are usually different. To solve these problems, the Russell measure of efficiency, which takes both the inputs and outputs into account, has been proposed. However, the conventional Russell efficiency is measured under the least favorable conditions, rather than the general custom of measuring under the most favorable ones. This paper develops a model to measure Russell efficiency under the most favorable conditions in two forms, the average and the product. They can be transformed into a second-order cone program and a mixed integer linear program, respectively, so that the solution can be obtained efficiently. A case of Taiwanese commercial banks demonstrates that they are more reliable and representative than the radial measures. Since the most favorable measures are higher than the least favorable measures, and the targets for making improvements are the easiest to reach, they are more acceptable to the production units to be evaluated.

Original languageEnglish
Title of host publicationMathematical Optimization Theory and Operations Research - 19th International Conference, MOTOR 2020, Proceedings
EditorsAlexander Kononov, Michael Khachay, Valery A. Kalyagin, Panos Pardalos
Number of pages16
ISBN (Print)9783030499877
Publication statusPublished - 2020
Event19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020 - Novosibirsk, Russian Federation
Duration: 2020 Jul 62020 Jul 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12095 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020
Country/TerritoryRussian Federation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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