Application of dynamic pricing for variant production using reinforcement learning

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In the context of variant production, the increasing volatility and customer requirements challenge the profitability of manufacturers. A promising approach to mitigate these challenges could be a dynamic pricing. An intelligent design of a continuous delivery-time-price function allows customers to choose based on their preferences and demand may be shifted to level any peaks. This way, profit, service level, and capacity usage could be improved. This work develops a dynamic pricing model based on reinforcement learning applied to a use case of the automation industry. The results show that the dynamic pricing model performs better than current methods in practice.

Original languageEnglish
JournalCIRP Journal of Manufacturing Science and Technology
Volume60
Pages (from-to)248-259
Number of pages12
ISSN1755-5817
DOIs
Publication statusPublished - 09.2025

Bibliographical note

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© 2025 The Authors

    Research areas

  • Capacity levelling, Dynamic pricing, Reinforcement learning, Variant production, Volatility
  • Engineering