Research Article
Chance Constrained Multi-Level Linear Programming Problem
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 120 - Issue 18 |
| Published: June 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 },
year = { 2015 },
volume = { 120 },
number = { 18 },
pages = { 1-6 },
doi = { 10.5120/21324-4275 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Surapati Pramanik
%A Durga Banerjee
%A B. C. Giri
%T Chance Constrained Multi-Level Linear Programming Problem%T
%J International Journal of Computer Applications
%V 120
%N 18
%P 1-6
%R 10.5120/21324-4275
%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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