Learning from Prior Designs for Facility Layout Optimization

Hannu Rummukainen (Corresponding author), Jukka K. Nurminen, Timo Syrjänen, Jukka Pekka Numminen

    Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

    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.
    Original languageEnglish
    Title of host publicationStudies in Computational Intelligence
    EditorsFarouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi
    PublisherSpringer
    Chapter6
    Pages87-101
    Number of pages15
    ISBN (Electronic)978-3-030-58930-1
    ISBN (Print)978-3-030-58929-5
    DOIs
    Publication statusPublished - 2021
    MoE publication typeA3 Part of a book or another research book

    Publication series

    SeriesStudies in Computational Intelligence
    Volume906
    ISSN1860-949X

    Keywords

    • optimisation
    • facility layout
    • machine learning

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