Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Economics of Digital Transformation: Approaching Non-stable and Uncertain Digitalized Production Systems Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Reinforcement Learning Approach for Dynamic Pricing

Authors : Maksim Balashov, Anton Kiselev, Alena Kuryleva

Published in: The Economics of Digital Transformation

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the introduction of digital technologies, it becomes easier for customers to compare prices and choose the product that is most profitable for them, this leads to instability of demand, which means that there is a need for market players to review pricing policies in favor of one that could take into account the characteristics of producer’s resources and current demand status.
Dynamic pricing seems to be an adequate solution to the problem, as it is adaptive to customer expectations. In addition, with the digitalization of the economy, unique opportunities arise for using this apparatus.
The purpose of this study is to evaluate the possibility of applying the concept of dynamic pricing to traditional retail.
The goal of solving the dynamic pricing problem in the framework of this study is to maximize profits from the sale of a specific associated product at an automatic gas station.
To solve this problem, the authors propose using machine learning approaches that adapt to the external environment, one of which is reinforcement learning (RL). At the same time, an approach is proposed to restore the demand surface for subsequent training of the agent.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Oil Shocks and Stock Market Performance: Evidence from the Eurozone and the USA
next chapter Convergent Evolution of IT Security Paradigm: From Access Control to Cyber-Defense
Literature
Aviv, Y., & Pazgal, A. (2002). Pricing of short life-cycle products through active learning. Under revision for Management Science.
Carroll, W. J., & Grimes, R. C. (1995). Evolutionary change in product management: Experiences in the car rental industry. Interfaces, t., 25(5), 84–104.CrossRef
Carvalho, A. X., Puterman, M. L. (2003). Dynamic pricing and learning over short time horizons. Sander School of Business, UBC, Working Paper.
Chen, L., Mislove, A., & Wilson, C. (2016). An empirical analysis of algorithmic pricing on amazon marketplace. In Proceedings of the 25th international conference on world wide web (pp. 1339–1349). International World Wide Web Conferences Steering Committee.
Chinthalapati, V. L. R., Yadati, N., & Karumanchi, R. (2006). Learning dynamic prices in multi-seller electronic retail markets with price-sensitive customers, stochastic demands, and inventory replenishments. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(1), 92–106.CrossRef
Ciancimino, A., et al. (1999). A mathematical programming approach for the solution of the railway yield management problem. Transportation Science., 33(2), 168–181.CrossRef
Deksnyte, I., Lydeka, Z. (2012). Dynamic pricing and its forming factors. International Journal of Business and Social Science, 3, no. 23.
Dimicco, J. M., Maes, P., & Greenwald, A. (2003). Learning curve: A simulation-based approach to dynamic pricing. Electronic Commerce Research, 3(3–4), 245–276.CrossRef
Gupta, M., Ravikumar, K., & Kumar, M. (2002). Adaptive strategies for price markdown in a multi-unit descending price auction: A comparative study. IEEE International Conference on Systems, Man and Cybernetics, 1, 373–378.CrossRef
Hayes, D. K., & Miller, A. A. (2011). Revenue Management for the Hospitality Industry. Hoboken, NJ: Wiley.
Hester, T. et al. (2018). Deep q-learning from demonstrations. Thirty-second AAAI conference on artificial intelligence.
Jintian, W., & Lei, Z. (2009). Application of reinforcement learning in dynamic pricing algorithms. In IEEE international conference on automation and logistics (pp. 419–423).
Kephart, J. O., Hanson, J. E., & Greenwald, A. R. (2000). Dynamic pricing by software agents. Computer Networks, 32(6), 731–752.CrossRef
Lawrence, R. D. (2003). A machine-learning approach to optimal bid pricing. Computational modeling and problem solving in the networked world, Springer, Boston, MA, pp. 97–118.
Littlewood, K. (1972). Forecasting and control of passenger bookings. Airline Group International Federation of Operational Research Societies Proceedings, t. 12 (pp. 95–117).
Liu, G., & Wang, H. (2013). An online sequential feed-forward network model for demand curve prediction. Journal of information & computational science, 10(10), 3063–3069.CrossRef
Liu, J., et al. (2018). Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning. International conference on learning representations. New Orleans, Louisiana, United States.
Lu, R., Hong, S. H., & Zhang, X. (2018). A dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach. Applied Energy, 220, 220–230.CrossRef
Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529.CrossRef
Mullen, P. B. et al. (2006). Particle swarm optimization in dynamic pricing. IEEE International Conference on Evolutionary Computation, pp. 1232–1239.
Nikolenko, S., Arhangelskaya, E., & Kadurin, A. (2020). Deep learning. Dive into the world of neural networks. Sankt-Petersburg: Publishing house Piter (in Russian). [Николенко С., Архангельская Е., Кадурин А. Глубокое обучение. Погружение в мир нейронных сетей. Санкт-Петербург, Издательство Питер, 2020 г. 480 с.].
Raju, C. V. L., Narahari, Y., & Ravikumar, K. (2006). Learning dynamic prices in electronic retail markets with customer segmentation. Annals of Operations Research, 143(1), 59–75.CrossRef
Ramezani, S., Bosman, P. A. N., & La Poutré, H. (2011). Adaptive strategies for dynamic pricing agents. Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Volume 02. IEEE Computer Society, pp. 323–328.
Rothstein, M. (1971). An airline overbooking model. Transportation Science, 5(2), 180–192.CrossRef
Spedicato, G. A., Dutang, C., & Petrini, L. (2018). Machine learning methods to perform pricing optimization. A comparison with standard GLMs. Casualty actuarial society, 12(1), 69–89.
Subrahmanyan, S., & Shoemaker, R. (1996). Developing optimal pricing and inventory policies for retailers who face uncertain demand. Journal of Retailing, 72(1), 7–30.CrossRef
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Weatherford, L. R., & Bodily, S. E. (1992). A taxonomy and research overview of perishable-asset revenue management: Yield management, overbooking, and pricing. Operations Research, 40(5), 831–844.CrossRef
Xia, L., Monroe, K. B., & Cox, J. L. (2004). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1–15.CrossRef
Metadata
Title
Reinforcement Learning Approach for Dynamic Pricing
Authors
Maksim Balashov
Anton Kiselev
Alena Kuryleva
Copyright Year
2021
Publisher
Springer International Publishing
Book
The Economics of Digital Transformation

Print ISBN: 978-3-030-59958-4

Electronic ISBN: 978-3-030-59959-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-59959-1_8