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Chance Constrained Multi-Level Linear Programming Problem

by Surapati Pramanik, Durga Banerjee, B. C. Giri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 18
Year of Publication: 2015
Authors: Surapati Pramanik, Durga Banerjee, B. C. Giri
10.5120/21324-4275

Surapati Pramanik, Durga Banerjee, B. C. Giri . Chance Constrained Multi-Level Linear Programming Problem. International Journal of Computer Applications. 120, 18 ( June 2015), 1-6. DOI=10.5120/21324-4275

@article{ 10.5120/21324-4275,
author = { Surapati Pramanik, Durga Banerjee, B. C. Giri },
title = { Chance Constrained Multi-Level Linear Programming Problem },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 18 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number18/21324-4275/ },
doi = { 10.5120/21324-4275 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:31.732113+05:30
%A Surapati Pramanik
%A Durga Banerjee
%A B. C. Giri
%T Chance Constrained Multi-Level Linear Programming Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 18
%P 1-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the paper, we present chance constrained multi-level linear programming problem. The right hand parameters and the coefficients of the constraints are considered as the random variables of known distribution function and the chance constraints are transformed into equivalent deterministic constraints. Membership function for each level objective function is constructed subject to the equivalent deterministic constraints. In the multi-level decision making situation, lower level decision makers may not be satisfied with the decision of higher level decision maker. To avoid this problem, each level decision maker provides relaxation in his/ her decision. Three FGP models are adopted to get the membership goals. Euclidean distance function is used to select the best FGP model offering the most satisfactory solution. Two numerical examples are solved to demonstrate the proposed approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Multi-level programming Fuzzy goal programming Chance constrained programming.

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