| Issue |
RAIRO-Oper. Res.
Volume 60, Number 3, May-June 2026
|
|
|---|---|---|
| Page(s) | 1307 - 1323 | |
| DOI | https://doi.org/10.1051/ro/2026009 | |
| Published online | 20 May 2026 | |
On the dual formulation of Russell measure model
Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
30
November
2024
Accepted:
23
January
2026
Abstract
The Russell Measure Model (RM), a nonlinear data envelopment analysis (DEA) model for evaluating decision-making units, allows for independent and disproportionate inputs and outputs, which makes it superior and more accurate than the radial models. The model is formulated as a second-order cone programming (SOCP) problem, and its dual is derived using SOCP duality. Previous studies have noted the complexity and limited interpretability of this dual formulation and have proposed an alternative using semidefinite programming (SDP) problem. This paper demonstrates the equivalence of these dual formulations through variable transformations. In addition, a new SOCP formulation of the dual RM model is introduced, which is in the usual form of multiplier models without any variable transformations. It is shown that this new formulation is equivalent to the SDP model. Moreover, using the conic model, a new approach is proposed to identify the unique maximal reference set and projection by solving one model, thereby improving upon the existing two-stage approach. Two examples demonstrate the advantages of the proposed models.
Mathematics Subject Classification: 90C22 / 90C25
Key words: Russell measure / second-order cone programming / semidefinite programming / strong complementary slackness conditions / maximal reference set
© The authors. Published by EDP Sciences, ROADEF, SMAI 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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