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Over the Mach range from 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by super-critical airfoil with low drag coefficients.
In this paper, we present a network design model under OD demand uncertainty in order to quantify the effect of the uncertainty by the robust optimization.
It means that all four models of CCR, BCC, CCR-BCC, and BCC-CCR can be appropriately altered by the robust optimization approach.
In other words, the violation of results attained by the robust optimization model is less than that by mean-value based model.
Figure 3 shows the cells configuration in periods 1, 2 and 3 for DCMS model solved by the robust optimization approach respect to 4 scenarios.
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Sensitivity analyses explore the trade-off between optimization and robustness by varying the robust optimization parameter values.
As a result, a robust orbit design approach is proposed by combining the robust optimization model with multi-objective optimization algorithm.
The robust optimization method extended by Mulvey et al. (1995), in fact, develops stochastic programming through replacing traditional expected cost minimization objective by one that explicitly addresses cost variability.
A class of objective functions, such as those found in the robust optimization package developed by Bortfeld and Unkelbach, allows for inputs in the form of probability density functions[38].
The feasibility of the robust optimization method is demonstrated by two examples.
The robust optimization problem is solved by means of the Firefly Algorithm.
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