A weighted metric method to optimize multi-response robust problems

a weighted metric method to optimize multi-response robust problems

in a robust parameter design (rpd) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. response sur-face methodology (rsm) is one of the effective methods that can be employed for this purpose. since quality of products or processes is usually evaluated through several quality ...

Cognitive Weighted Method Hiding Factor Complexity Metric

Method hiding plays a key role by serving the foundation for several guidelines in object-oriented software design. It brings about plethora of benefits such as easy comprehension, secured accessibility, modifiability without side-effects, modular testing, higher reliability etc. But, it has to be handled carefully since very low method hiding may result in insufficiently abstracted implementat...

Coordinating Between Medical Professions’ Tasks to Optimize Sub-Saharan Health Systems: A Response to Recent Commentaries

Multi-Objective Biogeography-Based Method to Optimize Virtual Machine Consolidation

Robust solutions to uncertain weighted least squares problems ∗

Robust optimization is a rapidly developing methodology for handling optimization problems affected by non-stochastic uncertain-but-bounded data perturbations. In this paper, we consider the weighted least squares problems where the coefficient matrices and vector belong to different uncertain bounded sets. We introduce the robust counterparts of these problems and reformulate them as the tract...

Robust Parameter Design Using the Weighted Metric Method - The Case of 'the Smaller the Better'

In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this pa...