Einsatz von bestärkendem Lernen in der Reihenfolgeplanung mit dem Ziel der platzeffizienten Produktion
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Simulation in Produktion und Logistik 2025. ed. / Sebastian Rank; Mathias Kühn; Thorsten Schmidt. Dresden: Dresden University of Technology, 2025. 40 (ASIM-Mitteilung; No. 194), (Tagungsband ASIM-Fachtagung Simulation in Produktion und Logistik; Vol. 21).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Bibtex
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RIS
TY - CHAP
T1 - Einsatz von bestärkendem Lernen in der Reihenfolgeplanung mit dem Ziel der platzeffizienten Produktion
AU - Müller, Kristin
AU - Heger, Jens
N1 - Conference code: 21
PY - 2025
Y1 - 2025
N2 - Priority rules are often used in production planning and control for sequence planning of production orders to optimise production efficiency based onkey figures such as order throughput time, machine utilisation or production output. Compared to priority rules, reinforcement learning agents are adaptive and can adjust to dynamic production conditions. This offers enormous potential for optimising production control. This study shows the successful implementation of a reinforcement learning agent that is trained with the Proximal Policy Optimisation algorithm. The agent is used to adjust the sequence of transport orders with the aim of achieving space-efficient production with short lead times. The results of the simulation study show an improvement in lead time and space efficiency compared to conventional priority rules.
AB - Priority rules are often used in production planning and control for sequence planning of production orders to optimise production efficiency based onkey figures such as order throughput time, machine utilisation or production output. Compared to priority rules, reinforcement learning agents are adaptive and can adjust to dynamic production conditions. This offers enormous potential for optimising production control. This study shows the successful implementation of a reinforcement learning agent that is trained with the Proximal Policy Optimisation algorithm. The agent is used to adjust the sequence of transport orders with the aim of achieving space-efficient production with short lead times. The results of the simulation study show an improvement in lead time and space efficiency compared to conventional priority rules.
KW - Ingenieurwissenschaften
UR - https://d-nb.info/1378976436/34
U2 - 10.25368/2025.273
DO - 10.25368/2025.273
M3 - Aufsätze in Konferenzbänden
SN - 978-3-86780-806-4
T3 - ASIM-Mitteilung
BT - Simulation in Produktion und Logistik 2025
A2 - Rank, Sebastian
A2 - Kühn, Mathias
A2 - Schmidt, Thorsten
PB - Dresden University of Technology
CY - Dresden
T2 - 21. ASIM-Fachtagung Simulation in Produktion und Logistik
Y2 - 24 September 2025 through 25 September 2025
ER -