Neural agent-based production planning and control
- Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combinationNowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.…
Author details: | Marcel PanzerORCiDGND, Benedict BenderORCiDGND, Norbert GronauORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-604777 |
DOI: | https://doi.org/10.25932/publishup-60477 |
ISSN: | 1867-5808 |
Title of parent work (German): | Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe |
Subtitle (English): | an architectural review |
Publication series (Volume number): | Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe (172) |
Publication type: | Postprint |
Language: | English |
Date of first publication: | 2022/11/19 |
Publication year: | 2022 |
Publishing institution: | Universität Potsdam |
Release date: | 2024/04/30 |
Tag: | machine learning; neural networks; production planning and control; systematic literature review; taxonomy |
Number of pages: | 26 |
Source: | Journal of Manufacturing Systems, Volume 65, 2022, Pages 743-766, ISSN 0278-6125, https://doi.org/10.1016/j.jmsy.2022.10.019 |
Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |
External remark: | Bibliographieeintrag der Originalveröffentlichung/Quelle |