Outperformed by a Computer? - Comparing Human Decisions to Reinforcement Learning Agents, Assigning Lot Sizes in a Learning Factory: 11th Conference on Learning Factories, CLF2021
Research output: Working paper › Working papers
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Rochester: Elsevier Inc., 2021. (SSRN).
Research output: Working paper › Working papers
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RIS
TY - UNPB
T1 - Outperformed by a Computer? - Comparing Human Decisions to Reinforcement Learning Agents, Assigning Lot Sizes in a Learning Factory
T2 - 11th Conference on Learning Factories, CLF2021
AU - Voß, Thomas
AU - Rokoss, Alexander
AU - Maier, Janine Tatjana
AU - Schmidt, Matthias
AU - Heger, Jens
N1 - 11th Conference on Learning Factories; July 1st - July 2nd, 2021. Online Event. Posted: 4 Jun 2021
PY - 2021/6
Y1 - 2021/6
N2 - Given the fact that some products in the production system require larger cycle times and a setup process during production, the application of different lot sizes might be beneficial regarding economic and logistic key performance indicators. Maximizing the profit considering the according work in process, inventory, set up and penalty costs is a complex challenge. In this contribution, we present the complete workflow from collecting data, deriving a simulation model, as well as training and deploying a machine learning model for assigning lot sizing in a dynamic production system. Furthermore, we compare the results of human participants and the reinforcement learning agent in a real-world learning factory workshop. The comparison of the results shows, that the reinforcement learning-agent is able to achieve the same level of results as the human experts.
AB - Given the fact that some products in the production system require larger cycle times and a setup process during production, the application of different lot sizes might be beneficial regarding economic and logistic key performance indicators. Maximizing the profit considering the according work in process, inventory, set up and penalty costs is a complex challenge. In this contribution, we present the complete workflow from collecting data, deriving a simulation model, as well as training and deploying a machine learning model for assigning lot sizing in a dynamic production system. Furthermore, we compare the results of human participants and the reinforcement learning agent in a real-world learning factory workshop. The comparison of the results shows, that the reinforcement learning-agent is able to achieve the same level of results as the human experts.
KW - Engineering
KW - reinforcement learning
KW - Artifical intelligence
KW - Lot sizing
KW - Learning Factory
UR - https://www.mendeley.com/catalogue/2d0008f5-c3eb-30da-8131-2d708cf3cad0/
U2 - 10.2139/ssrn.3859196
DO - 10.2139/ssrn.3859196
M3 - Working papers
T3 - SSRN
BT - Outperformed by a Computer? - Comparing Human Decisions to Reinforcement Learning Agents, Assigning Lot Sizes in a Learning Factory
PB - Elsevier Inc.
CY - Rochester
ER -