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The yardstick of performances was chosen solely as the NPV attained in the optimization scheme.
In the optimization scheme, the physical model is developed per differential algebraic equations (DAEs).
The physical constraints of the processing equipment, such as maximum heating and cooling rates and maximum and minimum temperatures, are included in the optimization scheme.
These markers can be included in the optimization scheme as well, provided we specify a model for their impact on the response rate.
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The correlation value is then employed to calculate the fitness value fval of a solution in the optimization schemes.
In the optimization schemes, the objective function is to minimize the strain energy for static problems and to maximize the fundamental frequency for free vibration problems.
The optimization problem is solved by means of a Monte Carlo method: in each iteration of the optimization scheme, a Monte Carlo simulation is performed, considering 100 random realizations of the manufacturing error.
In order to apply the optimization scheme, the calculation of the linkage kinematics is firstly performed through a variational approach, then the sampled surface describing the linkage trajectory is approximated by a set of Zernike polynomials, which allows to effectively define and calculate the target function for the optimization itself.
Section 2 introduces the basic idea and the related techniques, and Section 3 presents the proposed residual 2D-DVC and the optimization scheme in detail.
This approach also gives better performance than the optimization scheme outlined in [33].
Table 4 lists the parameter values for each descriptor and dataset after applying the optimization scheme described in Section 3.14.
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