Towards automatic facility layout design using reinforcement learning

Ikeda, Hikaru; Nakagawa, Hiroyuki; Tsuchija, Tatsuhiro

doi:10.15439/2022F25

Artykuł - szczegóły

Tytuł artykułu

Towards automatic facility layout design using reinforcement learning

Autorzy

Ikeda Hikaru , Nakagawa Hiroyuki , Tsuchija Tatsuhiro

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2022F25

Warianty tytułu

Konferencja

17th Conference on Computer Science and Intelligence Systems

Języki publikacji

Abstrakty

The accuracy and perfection of layout designing significantly depend on the designer's ability. Quick and near-optimal designs are very difficult to create. In this study, we proposed an automatic design mechanism that can more easily design layouts for various unit groups and sites using reinforcement learning. Accordingly, we devised a mechanism to deploy units to be able to fill the largest rectangular space in the current site. We aim to successfully deploy given units within a given site by filling a part of the site. We apply the mechanism to the three sets of units in benchmark problems. The performance was evaluated by changing the learning parameters and iteration count. Consequently, it was possible to produce a layout that successfully deployed units within a given one-floor site.

Słowa kluczowe

reinforcement learning machine learning layout design facility layout problem

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2022

Tom

Vol. 32

Strony

11--20

Opis fizyczny

Bibliogr. 56 poz., rys., wykr.

Twórcy

autor

Ikeda Hikaru

h-ikeda@ist.osaka-u.ac.jp

Graduate School of Information Science and Technology Osaka University, Japan

autor

Nakagawa Hiroyuki

nakagawa@ist.osaka-u.ac.jp

Graduate School of Information Science and Technology Osaka University, Japan

autor

Tsuchija Tatsuhiro

t-tutiya}@ist.osaka-u.ac.jp

Graduate School of Information Science and Technology Osaka University, Japan

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-f7fa1226-b921-46d5-a8fc-f7e08bdb86d3