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Depending on the temporal discretization of the contact constraints, this may require an iterative solution process involving the evaluation of the constraints at predictor configurations.
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We propose for the stacking sequence optimization an outer approximation method which iteratively solves a set of mixed integer linear problems associated to the evaluation of the constraint margin function.
In each iteration, only one evaluation of the implicit constraints is needed to see whether the current design point is feasible.
The first approach is based on a well-known two-level method where the first level involves the optimization for the design parameters whereas the evaluation of the probabilistic constraints is carried out in a sub-optimization level.
We show that mobile data holds great potential for improving the quality, quantity and timing of selected information required for disaster management, but that testing and evaluation of the benefits, constraints and limitations of mobile data use in a wider range of mobile-user and disaster contexts is needed to fully understand its utility, validity, and limitations.
Because the evaluation of the probabilistic constraints in a sub-optimization level causes non-convergence behavior for some problems as indicated in the literature, an alternative formulation based on one-level is developed considering the optimality conditions of the β-computation by which the probabilistic constraint appears in the first level reliability-based design optimization formulation.
Evaluation of the selective constraints showed that the mean dN:dS values of the Micropia sequences were 14-58 times higher than those of Gpdh (Table 3 and [Additional file 10]).
So no additional evaluation of the probabilistic constraint is required in the deterministic optimization in SORA.
They include the accurate evaluation of the rotational constraint provided by the intact section of DCB, possible transverse shear deformation in the delaminated section, and effects of uniform and nonuniform temperature on the response.
Diehl et al. [ 35] presented another SQP algorithm which does not require the evaluation of the exact constraint Jacobian matrix.
Evaluation of this constraint occurs at the beginning of each 5-year management period.
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