Smart scheduling for next generation manufacturing systems: a systematic literature review
Author:
Funder
IITI DRISHTI CPS Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02484-2.pdf
Reference110 articles.
1. Azab, E., Nafea, M., Shihata, L. A., & Mashaly, M. (2021). A machine-learning-assisted simulation approach for incorporating predictive maintenance in dynamic flow-shop scheduling. Applied Sciences (Switzerland), 11(24). https://doi.org/10.3390/app112411725
2. Bagheri Rad, N., & Behnamian, J. (2023). Real-time multi-factory scheduling in industry 4.0 with virtual alliances. Engineering Applications of Artificial Intelligence, 125. https://doi.org/10.1016/j.engappai.2023.106636
3. Barenji, A. V., Barenji, R. V., Roudi, D., & Hashemipour, M. (2017). A dynamic multi-agent-based scheduling approach for SMEs. International Journal of Advanced Manufacturing Technology, 89(9–12), 3123–3137. https://doi.org/10.1007/s00170-016-9299-4
4. Bencheikh, G., Letouzey, A., & Desforges, X. (2022). An approach for joint scheduling of production and predictive maintenance activities. Journal of Manufacturing Systems, 64, 546–560. https://doi.org/10.1016/j.jmsy.2022.08.005
5. Bouazza, W., Sallez, Y., & Trentesaux, D. (2021). Dynamic scheduling of manufacturing systems: A product-driven approach using hyper-heuristics. International Journal of Computer Integrated Manufacturing, 34(6), 641–665. https://doi.org/10.1080/0951192X.2021.1925969
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