Umfassende Service-Einschränkungen ab 18. März
ISO-690 (author-date, English)

LEE, Donggun, KANG, Yong-Shin und NOH, Sang Do, 2026. Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems. International Journal of Production Research. 1 Januar 2026. Vol. 64, no. 1, p. 106-124. DOI 10.1080/00207543.2025.2543491.

Elsevier - Harvard (with titles)

Lee, D., Kang, Y.-S., Noh, S.D., 2026. Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems. International Journal of Production Research 64, 106-124. https://doi.org/10.1080/00207543.2025.2543491

American Psychological Association 7th edition

Lee, D., Kang, Y.-S., & Noh, S. D. (2026). Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems. International Journal of Production Research, 64(1), 106-124. https://doi.org/10.1080/00207543.2025.2543491

Springer - Basic (author-date)

Lee D, Kang Y-S, Noh SD (2026) Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems.. International Journal of Production Research 64:106-124. https://doi.org/10.1080/00207543.2025.2543491

Juristische Zitierweise (Stüber) (Deutsch)

Lee, Donggun/ Kang, Yong-Shin/ Noh, Sang Do, Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems., International Journal of Production Research 2026, 106-124.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.