Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead
- 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.
Author: | David Stenger, Lena Altherr, Dirk Abel |
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DOI: | https://doi.org/10.1007/978-3-030-18500-8_47 |
ISBN: | 978-3-030-18499-5 (Print) |
ISBN: | 978-3-030-18500-8 (Online) |
Parent Title (English): | Operations Research Proceedings 2018 |
Publisher: | Springer |
Place of publication: | Cham |
Document Type: | Part of a Book |
Language: | English |
Year of Completion: | 2019 |
Tag: | Engineering optimization; Machine learning; Mixed-integer nonlinear black-box optimization; Product family optimization |
First Page: | 379 |
Last Page: | 385 |
Link: | https://doi.org/10.1007/978-3-030-18500-8_47 |
Zugriffsart: | campus |
Institutes: | FH Aachen / Fachbereich Elektrotechnik und Informationstechnik |
collections: | Verlag / Springer |