research-article

Dynamic pricing policies for interdependent perishable products or services using reinforcement learning

Authors:

Fernando S. OliveiraAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 42, Issue 1

Pages 426 - 436

https://doi.org/10.1016/j.eswa.2014.07.007

Published: 01 January 2015 Publication History

Abstract

Dynamic prices maximize the expected revenue of interdependent products.Reinforcement learning optimizes the pricing of interdependent products.Interdependent pricing enhances learning. Many businesses offer multiple products or services that are interdependent, in which the demand for one is often affected by the prices of others. This article considers a revenue management problem of multiple interdependent products, in which dynamically adjusted over a finite sales horizon to maximize expected revenue, given an initial inventory for each product. The main contribution of this article is to use reinforcement learning to model the optimal pricing of perishable interdependent products when demand is stochastic and its functional form unknown. We show that reinforcement learning can be used to price interdependent products. Moreover, we analyze the performance of the Q-learning with eligibility traces algorithm under different conditions. We illustrate our analysis with the pricing of services.

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Qiao WHuang MGao ZWang X(2024)Distributed dynamic pricing of multiple perishable products using multi-agent reinforcement learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121252237:PAOnline publication date: 27-Feb-2024
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Mohamadi NNiaki STaher MShavandi A(2024)An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain managementEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107403127:PBOnline publication date: 1-Jan-2024
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Zhu XJian LChen XZhao Q(2024)Reinforcement learning for Multi-Flight Dynamic PricingComputers and Industrial Engineering10.1016/j.cie.2024.110302193:COnline publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.110302
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 42, Issue 1

January 2015

698 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2015

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Cited By

Qiao WHuang MGao ZWang X(2024)Distributed dynamic pricing of multiple perishable products using multi-agent reinforcement learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121252237:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121252
Mohamadi NNiaki STaher MShavandi A(2024)An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain managementEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107403127:PBOnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107403
Zhu XJian LChen XZhao Q(2024)Reinforcement learning for Multi-Flight Dynamic PricingComputers and Industrial Engineering10.1016/j.cie.2024.110302193:COnline publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.110302
Yavuz TKaya O(2024)Deep reinforcement learning algorithms for dynamic pricing and inventory management of perishable productsApplied Soft Computing10.1016/j.asoc.2024.111864163:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111864
Xu SZhang GYuan X(2024)An Enterprise Multi-agent Model with Game Q-Learning Based on a Single Decision FactorComputational Economics10.1007/s10614-023-10524-x64:4(2523-2562)Online publication date: 1-Oct-2024
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Abdalrahman AZhuang W(2022)Dynamic Pricing for Differentiated PEV Charging Services Using Deep Reinforcement LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.302583223:2(1415-1427)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1109/TITS.2020.3025832
Ahmadi EMosadegh HMaihami RGhalehkhondabi ISun MSüer G(2022)Intelligent inventory management approaches for perishable pharmaceutical products in a healthcare supply chainComputers and Operations Research10.1016/j.cor.2022.105968147:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.cor.2022.105968
Wang RGan XLi QYan X(2021)Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement LearningComplexity10.1155/2021/66431312021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6643131
Seo DChang KCheong TBaek J(2021)A reinforcement learning approach to distribution-free capacity allocation for sea cargo revenue managementInformation Sciences: an International Journal10.1016/j.ins.2021.04.092571:C(623-648)Online publication date: 1-Sep-2021
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Elreedy DAtiya AShaheen S(2021)Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation frameworkSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06047-y25:17(11711-11733)Online publication date: 1-Sep-2021
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