Deep reinforcement learning in production planning and control
- Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on theirIncreasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.…
Author details: | Marcel PanzerORCiD, Benedict BenderORCiDGND, Norbert GronauORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-605722 |
DOI: | https://doi.org/10.25932/publishup-60572 |
ISSN: | 2701-6277 |
ISSN: | 1867-5808 |
Title of parent work (German): | Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe |
Subtitle (English): | A systematic literature review |
Publication series (Volume number): | Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe (198) |
Publication type: | Postprint |
Language: | English |
Year of first publication: | 2021 |
Publication year: | 2021 |
Publishing institution: | Universität Potsdam |
Release date: | 2024/05/02 |
Tag: | deep reinforcement learning; machine learning; production control; production planning; systematic literature review |
Number of pages: | 13 |
Source: | Proceedings of the Conference on Production Systems and Logistics : CPSL 2021. Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2021, S. 535-545. DOI: https://doi.org/10.15488/11238 |
Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre |
DDC classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
6 Technik, Medizin, angewandte Wissenschaften / 60 Technik / 600 Technik, Technologie | |
6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |
License (German): | Creative Commons - Namensnennung, 3.0 Deutschland |
External remark: | Bibliographieeintrag der Originalveröffentlichung/Quelle |