TY  - CHAP
A1  - Stenger, David
A1  - Altherr, Lena
A1  - Abel, Dirk
T1  - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead
T2  - Operations Research Proceedings 2018
N2  - 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.
KW  - Product family optimization
KW  - Mixed-integer nonlinear black-box optimization
KW  - Engineering optimization
KW  - Machine learning
Y1  - 2019
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/9739
SN  - 978-3-030-18499-5  (Print)
SN  - 978-3-030-18500-8 (Online)
SP  - 379
EP  - 385
PB  - Springer
CY  - Cham
ER  -