Dynamic pricing of product and delivery time in multi-variant production using an actor critic reinforcement learning

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Dynamic pricing of product and delivery time in multi-variant production using an actor critic reinforcement learning. / Stamer, Florian; Lanza, Gisela.
In: CIRP Annals, Vol. 72, No. 1, 01.2023, p. 405-408.

Research output: Journal contributionsJournal articlesResearchpeer-review

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@article{ff0142ae012a4b118378b92782cba1ac,
title = "Dynamic pricing of product and delivery time in multi-variant production using an actor critic reinforcement learning",
abstract = "The profitability of manufacturers in multi-variant production is challenged by the combination of increasing customer requirements and volatile supply chains. A potential solution is dynamic pricing, where customers can select a delivery time and price based on their preferences, and demand can be balanced during peak times. This paper presents a dynamic pricing approach using an actor-critic reinforcement learning agent in combination with a production simulation model and applies it in the automation technology industry.",
keywords = "Adaptive manufacturing, Dynamic pricing, Mass customization, Engineering",
author = "Florian Stamer and Gisela Lanza",
note = "Publisher Copyright: {\textcopyright} 2023 CIRP",
year = "2023",
month = jan,
doi = "10.1016/j.cirp.2023.04.019",
language = "English",
volume = "72",
pages = "405--408",
journal = "CIRP Annals",
issn = "0007-8506",
publisher = "Elsevier Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Dynamic pricing of product and delivery time in multi-variant production using an actor critic reinforcement learning

AU - Stamer, Florian

AU - Lanza, Gisela

N1 - Publisher Copyright: © 2023 CIRP

PY - 2023/1

Y1 - 2023/1

N2 - The profitability of manufacturers in multi-variant production is challenged by the combination of increasing customer requirements and volatile supply chains. A potential solution is dynamic pricing, where customers can select a delivery time and price based on their preferences, and demand can be balanced during peak times. This paper presents a dynamic pricing approach using an actor-critic reinforcement learning agent in combination with a production simulation model and applies it in the automation technology industry.

AB - The profitability of manufacturers in multi-variant production is challenged by the combination of increasing customer requirements and volatile supply chains. A potential solution is dynamic pricing, where customers can select a delivery time and price based on their preferences, and demand can be balanced during peak times. This paper presents a dynamic pricing approach using an actor-critic reinforcement learning agent in combination with a production simulation model and applies it in the automation technology industry.

KW - Adaptive manufacturing

KW - Dynamic pricing

KW - Mass customization

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85161075816&partnerID=8YFLogxK

U2 - 10.1016/j.cirp.2023.04.019

DO - 10.1016/j.cirp.2023.04.019

M3 - Journal articles

AN - SCOPUS:85161075816

VL - 72

SP - 405

EP - 408

JO - CIRP Annals

JF - CIRP Annals

SN - 0007-8506

IS - 1

ER -