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.
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Notes
- 1.
In all cases, an anisotropic squared exponential kernel and a Gaussian likelihood function with a noise level fixed to 10−10 are used. Hyper parameters θ of the model are chosen by maximization of the log likelihood.
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Stenger, D., Altherr, L.C., Abel, D. (2019). Machine Learning and Metaheuristics for Black-Box Optimization of Product Families: A Case-Study Investigating Solution Quality vs. Computational Overhead. In: Fortz, B., Labbé, M. (eds) Operations Research Proceedings 2018. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-18500-8_47
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DOI: https://doi.org/10.1007/978-3-030-18500-8_47
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