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After sorting all input sequences based on their prospective time-to-solution as well as the optimized solution of scheduling all tasks, we start to run them from the longest one in a sorted order.
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Results from this study indicate that there is a significant reduction in the TSP penalty value of the optimized solution, as a result of incorporating SDF criterion during the optimization process.
The optimized solution is obtained as a result of simulation.
The optimized solution is denoted as p⋆.
The log likelihood − L θ; X) corresponding to the optimized solution θ* (denoted as ℒ r *) is used for guiding the choice of parameter r: for the same dataset, larger r means weaker penalization of the model freedom and leads to better data fitting (larger likelihood ℒ r *).
The optimized solution for the response parameter as provided by the software is given in Table 7.
Soft set, as efficient uncertain information processing mathematical methodology, is used to help us for finding the optimized solution under the uncertain environment.
As a result, the execution speed will be too slow to achieve the optimized solution.
A sensitivity analysis of the optimized solution follows.
The optimized solution is validated with experimental results.
The optimized solution also avoids overextension of the proposed line.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com