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In recent years, the International Air Transport Association (IATA) has introduced the New Distribution Capability (NDC), which has enabled airlines to implement continuous dynamic pricing. However, there is currently a lack of research on continuous dynamic pricing for multiple products. To fill this gap, this paper utilizes the Soft Actor-Critic (SAC) algorithm from reinforcement learning to study the continuous dynamic pricing problem of multi-class airline tickets. Additionally, the paper proposes a reward shaping method called Balanced Inventory Soft Actor-Critic (BISAC) to prevent certain ticket classes from being sold too quickly due to pricing errors during the sales process, outpacing other classes. Experimental results indicate that the performance of the BISAC algorithm significantly outperforms the original SAC algorithm and existing algorithms used for dynamic pricing of multiple products.

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Reinforcement Learning for Airline Multi-product Continuous Dynamic Pricing

  • 会议论文

  • 发表日期：

    2025年

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  • Zhicheng Yao,
  • Wenguo Yang

摘要

In recent years, the International Air Transport Association (IATA) has introduced the New Distribution Capability (NDC), which has enabled airlines to implement continuous dynamic pricing. However, there is currently a lack of research on continuous dynamic pricing for multiple products. To fill this gap, this paper utilizes the Soft Actor-Critic (SAC) algorithm from reinforcement learning to study the continuous dynamic pricing problem of multi-class airline tickets. Additionally, the paper proposes a reward shaping method called Balanced Inventory Soft Actor-Critic (BISAC) to prevent certain ticket classes from being sold too quickly due to pricing errors during the sales process, outpacing other classes. Experimental results indicate that the performance of the BISAC algorithm significantly outperforms the original SAC algorithm and existing algorithms used for dynamic pricing of multiple products.