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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Veröffentlicht in:	European journal of operational research Jg. 143; H. 1; S. 19 - 31
Hauptverfasser:	Sinha, Surabhi, Sinha, S.B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 16.11.2002 Elsevier
Schriftenreihe:	European Journal of Operational Research
Schlagworte:	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-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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