Refine
Year of publication
Institute
- Fachbereich Elektrotechnik und Informationstechnik (38) (remove)
Has Fulltext
- no (38)
Language
- English (38) (remove)
Document Type
- Part of a Book (38) (remove)
Keywords
- Engineering optimization (2)
- robotic process automation (2)
- Advanced driver assistance systems (ADAS/AD) (1)
- Autonomous mobile robots (1)
- BEV (1)
- Change culture (1)
- Charging stations (1)
- Controller Parameter (1)
- E-carsharing (1)
- E-mobility (1)
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.