Version 1
: Received: 2 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (11:09:08 CEST)
How to cite:
Modrak, V.; Ranjitharamasamy, S.; Balamurugan, A.; Soltysova, Z. A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective. Preprints2024, 2024050335. https://doi.org/10.20944/preprints202405.0335.v1
Modrak, V.; Ranjitharamasamy, S.; Balamurugan, A.; Soltysova, Z. A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective. Preprints 2024, 2024050335. https://doi.org/10.20944/preprints202405.0335.v1
Modrak, V.; Ranjitharamasamy, S.; Balamurugan, A.; Soltysova, Z. A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective. Preprints2024, 2024050335. https://doi.org/10.20944/preprints202405.0335.v1
APA Style
Modrak, V., Ranjitharamasamy, S., Balamurugan, A., & Soltysova, Z. (2024). A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective. Preprints. https://doi.org/10.20944/preprints202405.0335.v1
Chicago/Turabian Style
Modrak, V., Arunmozhi Balamurugan and Zuzana Soltysova. 2024 "A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective" Preprints. https://doi.org/10.20944/preprints202405.0335.v1
Abstract
In this study, a comprehensive review on production scheduling based on reinforcement learning (RL) techniques using bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of all pertinent publications contained in the Web of Science database. The study focuses on a wide spectrum of publications spanning the years between 1998 and 2023. The findings and recommendations of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.
Keywords
bibliometric analysis; production scheduling; reinforcement learning
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
