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Interactive reinforcement learning-based factory layout planning

  • Due to the complexity and the number of factors involved in factory layout planning, computers were identified as an efficient tool to support the process. However, so far no method for computer-aided layout planning has gained wide acceptance in practical application. One reason for this is that in present approaches either the user or the computer designs the layout, neglecting either the qualitative or the quantitative goals. To bridge this gap, this article introduces a concept for human-computer-integration based on evaluative feedback and inverse reinforcement learning. A key element of the concept is the interactive planning process in which user and computer alternately design and improve the layout until a satisfactory layout is found. The user evaluates the layouts according to qualitative criteria, adjusts them intuitively and specifies objectives and restrictions in an explorative way. The computer on the other hand – in form of a reinforcement algorithm – generates possible layouts and incorporates the user’s feedback into its policy. This synergy is expected to generate better results than an expert or an algorithm alone could. Furthermore, in the context of learning factories, it encourages critical thinking and allows students to develop a deeper understanding of the factors that contribute to efficient manufacturing processes. Both an architecture for the implementation is proposed and the requirements for the user interface are specified.

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Metadaten
Author:Jan Schneidewind, Stefan GalkaGND
URL / DOI:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4469300
Parent Title (English):13th Conference on Learning Factories (CLF 2023), 9-11 May 2023, Reutlingen, Germany
Publisher:SSRN
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2023
Release Date:2023/06/05
Tag:facility layout problem; factory planning; inverse reinforcement learning; machine learning; optimization
Pagenumber:6
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
Kostenträger (Forschungsprojekt, Labor, Einrichtung etc.):BMBF Projekt StaR (H2Giga), Innovationszentrum für Produktion und Logistik - IZPL
research focus:Produktion und Systeme
OpenAccess Publikationsweg:Bronze Open Access - frei zugänglich aber ohne Lizenzhinweis
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG