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On necessarily efficient solutions in interval multiobjective linear programming

Milan Hlad ́ õk

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Abstract

We investigate multiobjective linear programming problems with objective coefficients varying inside given intervals. A feasible solution x ∗ is called necessarily efficient if it is efficient for all realizations of the interval objective function coefficients. Testing nec- essarily efficiency may be computationally expensive. Thus we propose one sufficient and also one necessary condition for necessarily efficiency that can s speed up decision al- gorithms. These conditions do not require the feasible solution x ∗ to be non-degenerate. We demonstrate usage of both conditions on illustrative examples and show how strong they are.

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What this paper is about

We investigate multiobjective linear programming problems with objective coefficients varying inside given intervals. A feasible solution x ∗ is called necessarily efficient if it is efficient for all realizations of the interval objective function coefficients. Testing nec- essarily efficiency may be computationally expensive. Thus we propose one sufficient and also one necessary condition for necessarily efficiency that can s speed up decision al- gorithms. These conditions do not require the feasible solution x ∗ to be non-degenerate. We demonstrate usage of both conditions on illustrative examples and show how strong they are.

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Available abstract

We investigate multiobjective linear programming problems with objective coefficients varying inside given intervals. A feasible solution x ∗ is called necessarily efficient if it is efficient for all realizations of the interval objective function coefficients. Testing nec- essarily efficiency may be computationally expensive. Thus we propose one sufficient and also one necessary condition for necessarily efficiency that can s speed up decision al- gorithms. These conditions do not require the feasible solution x ∗ to be non-degenerate. We demonstrate usage of both conditions on illustrative examples and show how strong they are.

Key concepts: Mathematical optimization, Interval (graph theory), Linear programming, Mathematics, Linear-fractional programming, Degenerate energy levels, Function (biology), Computer science

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