KKT transformation approach for multi-objective multi-level linear programming problems

The earlier Karush–Kuhn–Tucker (KKT) transformation method has been applied to multi-level decentralized programming problems (ML(D)PPs) when the decision variable set was divided into subsets where each decision maker (DM) of the system controlled only a particular subset but had no control over an...

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Bibliographic Details
Published in:	European journal of operational research Vol. 143; no. 1; pp. 19 - 31
Main Authors:	Sinha, Surabhi, Sinha, S.B.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 16.11.2002 Elsevier
Series:	European Journal of Operational Research
Subjects:	Karush–Kuhn–Tucker's optimality conditions Lagrange function Local optimal solution Multi-level linear programming Multiple objective programming Local optimal solution Multi-level linear programming Lagrange function Multiple objective programming Karush–Kuhn–Tucker's optimality conditions
ISSN:	0377-2217, 1872-6860
Online Access:	Get full text
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Summary:	The earlier Karush–Kuhn–Tucker (KKT) transformation method has been applied to multi-level decentralized programming problems (ML(D)PPs) when the decision variable set was divided into subsets where each decision maker (DM) of the system controlled only a particular subset but had no control over any decision variables of some other subset. In this paper we give the mathematical formulation and corresponding development of ML(D)PPs by KKT transformation when DMs have absolute control over certain decision variables but some variables may be shared and hence controlled by two or more DMs.
ISSN:	0377-2217 1872-6860
DOI:	10.1016/S0377-2217(01)00323-X