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Table 1 lists MOEAs that use Pareto dominance to choose solutions.
Maybe the tester wants to prioritize this objective and choose solutions with score greater than 98%%.
According to the objectives priority, the decision maker can choose solutions directly from the Pareto front.
Other option is to choose solutions associated to the greatest scores.
It is possible to choose solutions with a score greater than 0.98 and with a reduced number of test cases.
In order to get optimized solutions in job scheduling decision, we use the genetic algorithm to choose solutions which have the minimal finishing time and cost.
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The chosen solutions for each case are denoted by stars.
We propose to expose the problems encountered during the development phase as well as the chosen solutions.
The fitness values of the chosen solutions are presented respectively, for E2O and E3O, in Tables 14 and 15.
Implementing both experimental and simulation outcomes would help well in understanding the applicability of the chosen solutions and techniques industrially.
By modifying the fitness of solutions and choosing solutions in terms of the shuffled sequence of objective axis, the DRSs will be eliminated during the selection.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com