Robust optimization with nonnegative decision variables: A DEA approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10240312" target="_blank" >RIV/61989100:27510/19:10240312 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0360835218304741" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360835218304741</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cie.2018.10.006" target="_blank" >10.1016/j.cie.2018.10.006</a>

Result language

angličtina

Original language name

Robust optimization with nonnegative decision variables: A DEA approach

Original language description

Robust optimization has become the state-of-the-art approach for solving linear optimization problems with uncertain data. Though relatively young, the robust approach has proven to be essential in many real-world applications. Under this approach, robust counterparts to prescribed uncertainty sets are constructed for general solutions to corresponding uncertain linear programming problems. It is remarkable that in most practical problems, the variables represent physical quantities and must be nonnegative. In this paper, we propose alternative robust counterparts with nonnegative decision variables - a reduced robust approach which attempts to minimize model complexity. The new framework is extended to the robust Data Envelopment Analysis (DEA) with the aim of reducing the computational burden. In the DEA methodology, first we deal with the equality in the normalization constraint and then a robust DEA based on the reduced robust counterpart is proposed. The proposed model is examined with numerical data from 250 European banks operating across the globe. The results indicate that the proposed approach (i) reduces almost 50% of the computational burden required to solve DEA problems with nonnegative decision variables; (ii) retains only essential (non-redundant) constraints and decision variables without alerting the optimal value.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA16-17810S" target="_blank" >GA16-17810S: Selective measures in data envelopment analysis: theory and applications</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers and Industrial Engineering

ISSN

0360-8352

e-ISSN

—

Volume of the periodical

127

Issue of the periodical within the volume

nemá

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

313-325

UT code for WoS article

000460708800025

EID of the result in the Scopus database

2-s2.0-85056408035