Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism

Yilin Liang, Yuping Hu (), Dongjun Luo, Qi Zhu, Qingxuan Chen and Chunmei Wang
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Yilin Liang: School of Informatics, Guangdong University of Finance & Economics, Guangzhou 510320, China
Yuping Hu: School of Informatics, Guangdong University of Finance & Economics, Guangzhou 510320, China
Dongjun Luo: School of Informatics, Guangdong University of Finance & Economics, Guangzhou 510320, China
Qi Zhu: School of Informatics, Guangdong University of Finance & Economics, Guangzhou 510320, China
Qingxuan Chen: School of Informatics, Guangdong University of Finance & Economics, Guangzhou 510320, China
Chunmei Wang: College of Internet Finance & Information Engineering, Guangdong University of Finance, Guangzhou 510521, China

Sustainability, 2023, vol. 15, issue 13, 1-20

Abstract: Despite the emergence of a presale mechanism that reduces manufacturing and ordering risks for retailers, optimizing the real-time pricing strategy in this mechanism and unknown demand environment remains an unsolved issue. Consequently, we propose an automatic real-time pricing system for e-retailers under the inventory backlog impact in the presale mode, using deep reinforcement learning technology based on the Dueling DQN algorithm. This system models the multicycle pricing problem with a finite sales horizon as a Markov decision process (MDP) to cope with the uncertain environment. We train and evaluate the proposed environment and agent in a simulation environment and compare it with two tabular reinforcement learning algorithms (Q-learning and SARSA). The computational results demonstrate that our proposed real-time pricing learning framework for joint inventory impact can effectively maximize retailers’ profits and has universal applicability to a wide range of presale models. Furthermore, according to a series of experiments, we find that retailers should not neglect the impact of the presale or previous prices on consumers’ purchase behavior. If consumers pay more attention to past prices, the retailer must decrease the current price. When the cost of inventory backlog increases, they need to offer deeper discounts in the early selling period. Additionally, introducing blockchain technology can improve the transparency of commodity traceability information, thus increasing consumer demand for purchase.

Keywords: presale; dynamic pricing; deep reinforcement learning; revenue management; blockchain (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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