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Lately there has been an increasing concern about uranium toxicity in some districts of Punjab State located in the North Western part of India after the publication of a report (Blaurock-Busch et al. 2010) which showed that the concentration of uranium in hair and urine of children suffering from physical deformities, neurological and mental disorder from Malwa region (Fig. 1) of Punjab State was manifold higher than the reference ranges. A train which connects the affected region with the nearby city of Bikaner which has a Cancer Hospital has been nicknamed as Cancer Express due to the frenzy generated on account of uranium related toxicity.
In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.