@inbook{46e5f5d2bfd6439599a7d0d87c33054d,
title = "Learning from Prior Designs for Facility Layout Optimization",
abstract = "The problem of facility layout involves not only optimizing the locations of process components on a factory floor, but in real-world applications there are numerous practical constraints and objectives that can be difficult to formulate comprehensively in an explicit optimization model. As an alternative to explicit modelling, we present an optimization approach that learns structural properties from examples of expert-designed layouts of other similar facilities, and considers similarity to the examples as one objective in a multiobjective facility layout optimization problem. We have tested the approach on small-scale artificial test data, and the initial results indicate that a layout objective can be learned from example layouts, even if the process structure in the examples differs from the target case.",
keywords = "optimisation, facility layout, machine learning",
author = "Hannu Rummukainen and Nurminen, {Jukka K.} and Timo Syrj{\"a}nen and Numminen, {Jukka Pekka}",
note = "Funding Information: This work was supported by Business Finland, through project Engineering Rulez. Publisher Copyright: {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2021",
doi = "10.1007/978-3-030-58930-1_6",
language = "English",
isbn = "978-3-030-58929-5",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "87--101",
editor = "Farouk Yalaoui and Lionel Amodeo and El-Ghazali Talbi",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}