Stochastic fractional programming for minimizing variability

S. N. Gupta, Rajnesh Mudaliar, Kuldeep Kumar

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In situations where risk aversion is involved, minimization of the variability of the economic criterion under consideration is indispensable. This paper provides a typical stochastic fractional programming model for minimizing the absolute value of the coefficient of variation of a linear function of decision variables under the assumption that the coefficient parameters of the decision variables have a known multivariate normal probability distribution. The stochastic problem is shown to be equivalent to a deterministic problem whose solution can be obtained by solving a related quadratic programming problem.
Original languageEnglish
Pages (from-to)495-501
Number of pages7
JournalJournal of Information and Optimization Sciences
Volume39
Issue number2
DOIs
Publication statusPublished - 8 Mar 2018

Fingerprint

Fractional Programming
Stochastic Programming
Risk Aversion
Coefficient of variation
Multivariate Normal
Quadratic Programming
Absolute value
Linear Function
Programming Model
Gaussian distribution
Probability Distribution
Economics
Coefficient

Cite this

@article{45418da249654f39aa9c2c18efd4e102,
title = "Stochastic fractional programming for minimizing variability",
abstract = "In situations where risk aversion is involved, minimization of the variability of the economic criterion under consideration is indispensable. This paper provides a typical stochastic fractional programming model for minimizing the absolute value of the coefficient of variation of a linear function of decision variables under the assumption that the coefficient parameters of the decision variables have a known multivariate normal probability distribution. The stochastic problem is shown to be equivalent to a deterministic problem whose solution can be obtained by solving a related quadratic programming problem.",
author = "Gupta, {S. N.} and Rajnesh Mudaliar and Kuldeep Kumar",
year = "2018",
month = "3",
day = "8",
doi = "10.1080/02522667.2017.1378420",
language = "English",
volume = "39",
pages = "495--501",
journal = "Journal of Information and Optimization Sciences",
issn = "0252-2667",
publisher = "Taylor & Francis",
number = "2",

}

Stochastic fractional programming for minimizing variability. / Gupta, S. N.; Mudaliar, Rajnesh; Kumar, Kuldeep.

In: Journal of Information and Optimization Sciences, Vol. 39, No. 2, 08.03.2018, p. 495-501.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Stochastic fractional programming for minimizing variability

AU - Gupta, S. N.

AU - Mudaliar, Rajnesh

AU - Kumar, Kuldeep

PY - 2018/3/8

Y1 - 2018/3/8

N2 - In situations where risk aversion is involved, minimization of the variability of the economic criterion under consideration is indispensable. This paper provides a typical stochastic fractional programming model for minimizing the absolute value of the coefficient of variation of a linear function of decision variables under the assumption that the coefficient parameters of the decision variables have a known multivariate normal probability distribution. The stochastic problem is shown to be equivalent to a deterministic problem whose solution can be obtained by solving a related quadratic programming problem.

AB - In situations where risk aversion is involved, minimization of the variability of the economic criterion under consideration is indispensable. This paper provides a typical stochastic fractional programming model for minimizing the absolute value of the coefficient of variation of a linear function of decision variables under the assumption that the coefficient parameters of the decision variables have a known multivariate normal probability distribution. The stochastic problem is shown to be equivalent to a deterministic problem whose solution can be obtained by solving a related quadratic programming problem.

U2 - 10.1080/02522667.2017.1378420

DO - 10.1080/02522667.2017.1378420

M3 - Article

VL - 39

SP - 495

EP - 501

JO - Journal of Information and Optimization Sciences

JF - Journal of Information and Optimization Sciences

SN - 0252-2667

IS - 2

ER -