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Table 7 shows the optimal feasible solution.
Finding the optimal, temporally feasible reconciliation is NP-complete (Tofigh et al., 2011); we leave the problem of obtaining an optimal, feasible solution when all candidate solutions have infeasible timing constraints for future work.
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There has not been a consensus on how to accommodate all these (conflicting) requirements in the search for near optimal feasible solutions.
In this paper the post-optimal analysis is presented, which studies the behavior of optimal (feasible) solutions under small changes in the input data taken into account by this design problem.
Remark 3: Optimal corner feasible solution to (P4) provides optimal solution to (P3).
In fact, the deterministic optimal policy corresponds to the optimal basic feasible solution.
By finding the optimal basic feasible solution of P- 4, we can obtain the deterministic optimal power allocation policy for the original problem P- 1. Again as discussed in the paragraph below Lemma 3, if there are two basic feasible solutions achieving optimal, the linear combination of the two solutions correspond to the probabilistic optimal policy.
The driving force based algorithm and the thermodynamic insights based algorithm help to formulate the problem, generate a superstructure and determine a good (near optimal) initial feasible solution.
Step 6: Find out the optimal basic feasible solution of the transformed transportation problem (P6).
Step 6: Ignoring the allocations in the diagonal cells, the solution obtained is the optimal basic feasible solution for the transshipment problem (P3). .
Ignoring the allocations in the diagonal cells, we obtain the optimal basic feasible solution for the transshipment problem with impaired flow.
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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