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Reinforcement learning based optimal decision making towards product lifecycle sustainability
Yang Liu (Författare), Miying Yang (Författare), Zhengang Guo (Författare)
URI http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-182923, URN urn:nbn:se:liu:diva-182923, DOI 10.1080/0951192X.2022.2025623
Linköpings universitet Institutionen för ekonomisk och industriell utveckling (Utgivare), Linköpings universitet Tekniska fakulteten (Utgivare)
utgivning
Taylor & Francis Ltd, 2022är del av
International journal of computer integrated manufacturing (Print) · ISSN 0951-192XBidrag, Onlineresurs
ämne
Engineering and Technology, Mechanical Engineering, Production Engineering, Human Work Science and Ergonomics, Teknik och teknologier, Maskinteknik, Produktionsteknik, arbetsvetenskap och ergonomi, Artificial intelligence; reinforcement learning; decision-making; sustainability; lifecycleSammanfattning
Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact.
Detaljer
Medverkan och funktion
Yang Liu (Författare), Miying Yang (Författare), Zhengang Guo (Författare)Medverkan och funktion
Linköpings universitet Institutionen för ekonomisk och industriell utveckling (Utgivare), Linköpings universitet Tekniska fakulteten (Utgivare)Identifikator
URI http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-182923, URN urn:nbn:se:liu:diva-182923, DOI 10.1080/0951192X.2022.2025623har titel
Reinforcement learning based optimal decision making towards product lifecycle sustainabilityär del av
International journal of computer integrated manufacturing (Print) · ISSN 0951-192Xutgivning
Taylor & Francis Ltd, 2022tillhörande media
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-182923http://dx.doi.org/10.1080/0951192X.2022.2025623http://liu.diva-portal.org/smash/get/diva2:1638633/FULLTEXT01Relaterad beskrivning eller innehåll
Värdpublikationanmärkning
- <p>Funding Agencies|VinnovaVinnova [2017-01649]</p>
- Epub ahead of print
tillhörande media
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-182923http://dx.doi.org/10.1080/0951192X.2022.2025623http://liu.diva-portal.org/smash/get/diva2:1638633/FULLTEXT01Villkor för användning och åtkomst
gratisingår i bibliografi
kontrollnummer
v9348lm0s615v5gqResursens ID / Permalänk: https://libris.kb.se/v9348lm0s615v5gq#it
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