Application of dynamic pricing for variant production using reinforcement learning

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Application of dynamic pricing for variant production using reinforcement learning. / Stamer, Florian; Henzi, Matthias; Lanza, Gisela.
in: CIRP Journal of Manufacturing Science and Technology, Jahrgang 60, 09.2025, S. 248-259.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{9e158e7b2a1f4f5a83e4e13356305e06,
title = "Application of dynamic pricing for variant production using reinforcement learning",
abstract = "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.",
keywords = "Capacity levelling, Dynamic pricing, Reinforcement learning, Variant production, Volatility, Engineering",
author = "Florian Stamer and Matthias Henzi and Gisela Lanza",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors",
year = "2025",
month = sep,
doi = "10.1016/j.cirpj.2025.05.004",
language = "English",
volume = "60",
pages = "248--259",
journal = "CIRP Journal of Manufacturing Science and Technology",
issn = "1755-5817",
publisher = "Elsevier B.V.",

}

RIS

TY - JOUR

T1 - Application of dynamic pricing for variant production using reinforcement learning

AU - Stamer, Florian

AU - Henzi, Matthias

AU - Lanza, Gisela

N1 - Publisher Copyright: © 2025 The Authors

PY - 2025/9

Y1 - 2025/9

N2 - 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.

AB - 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.

KW - Capacity levelling

KW - Dynamic pricing

KW - Reinforcement learning

KW - Variant production

KW - Volatility

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=105005958707&partnerID=8YFLogxK

U2 - 10.1016/j.cirpj.2025.05.004

DO - 10.1016/j.cirpj.2025.05.004

M3 - Journal articles

AN - SCOPUS:105005958707

VL - 60

SP - 248

EP - 259

JO - CIRP Journal of Manufacturing Science and Technology

JF - CIRP Journal of Manufacturing Science and Technology

SN - 1755-5817

ER -

DOI