Machine Learning and Metaheuristics for Black-Box Optimization of Product Families: A Case-Study Investigating Solution Quality vs. Computational Overhead

David Stenger (), Lena C. Altherr () and Dirk Abel ()
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David Stenger: RWTH Aachen University
Lena C. Altherr: TU Darmstadt
Dirk Abel: RWTH Aachen University

A chapter in Operations Research Proceedings 2018, 2019, pp 379-385 from Springer

Abstract: Abstract 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.

Keywords: Product family optimization; Mixed-integer nonlinear black-box optimization; Engineering optimization; Machine learning (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-18500-8_47

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DOI: 10.1007/978-3-030-18500-8_47

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