A two-stage deep reinforcement learning-driven dynamic discriminatory pricing model for hotel rooms with fairness constraints

• Xinmin Wang ^[1] ; Yuwei Xie ^[1] ; Ling Jian ^[1] ; Wei Liu ^[1] ; Wenting Lv ^[2]
  1. [1] China University of Petroleum

     China University of Petroleum

     China

     
  2. [2] Crowne Plaza Hotel, China
• Localización: Journal of Theoretical and Applied Electronic Commerce Research, ISSN-e 0718-1876, Vol. 20, Nº. 4, 2025
• Idioma: inglés
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• Resumen

  • Big data-driven discriminatory pricing not only creates opportunities to boost hotel profits but also amplifies consumers’ negative perceptions of price fairness. Developing a dynamic discriminatory pricing model with fairness constraints helps hotel room managers formulate optimal pricing strategies. This paper proposes a dynamic discriminatory pricing model with fairness constraints that unifies four pricing models: fixed pricing, dynamic pricing, discriminatory pricing, and dynamic discriminatory pricing. It further proposes a two-stage deep reinforcement learning algorithm to efficiently solve the model and generate optimal pricing strategies. Finally, a case study is conducted to validate the proposed model and algorithm. The results show that the two-stage deep reinforcement learning algorithm can instantaneously derive optimal pricing schemes that satisfy both group and temporal fairness constraints, following a reasonably time-efficient training process. By adjusting the fairness parameters, our model can be transformed into the four types of pricing models, and the performance of the algorithm is validated for the commonly used dynamic pricing and dynamic discriminatory pricing models. Compared to traditional nonlinear programming solution algorithms, this algorithm generates optimal daily prices based on real-time market changes, making it more practically applicable.