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 -