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Exact(1)
where X is a bounded feasible solution of an MOILP problem.
Similar(59)
We propose a multi-stage stochastic mixed-integer programming approach to the problem as well as a Lagrangian Heuristic procedure to attain reasonably well bounded feasible solutions.
Theorem For each W ∈ X Open image in new windowas a feasible solution of an MOILP problem with bounded feasible region, the vector g = ( g 1, g 2, ⋯, g s ) T Open image in new windowdominates the vector Y = ( C 1 W, C 2 W, ⋯, C s W ) T ≠ g Open image in new window.
This section extends the proposed method in (Jahanshahloo et al. 2004) to find all efficient solutions of MOILP with bounded feasible region.
Using a straightforward theoretical approach, Sylva and Crema's (2004) algorithm enumerates all efficient solutions of MOILP models with bounded feasible regions.
The strategy to prove Lemma 4 is to show that one feasible solution of (15) gives an upper bound of α k.
So, is a feasible solution of SDD.
Obviously, is a feasible solution of.
Since is a feasible solution of,.
For any feasible solution of SDD, (3.18).
For any feasible solution of SDP and any feasible solution of SDD, (3.11).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com