Approach for Dynamic Flight Pricing Based on Strategy Learning
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摘要: 机票动态定价旨在构建机票售价策略以最大化航班座位收益。现有机票定价算法都建立在提前预测各票价等级的需求量基础之上，会因票价等级需求量的预测偏差而降低模型性能。为此，提出基于策略学习的机票动态定价算法，其核心是不再预测各票价等级的需求量，而是将机票动态定价问题建模为离线强化学习问题。通过设计定价策略评估和策略更新的方式，从历史购票数据上学习具有最大期望收益的机票动态定价策略。同时设计了与现行定价策略和需求量预测方法的对比方法及评价指标。在两趟航班的多组定价结果表明：相比于现行机票销售策略，策略学习算法在座位收益上的提升率分别为30.94%和39.96%，且比基于需求量预测方法提升了6.04%和3.36%。Abstract: The core of the dynamic flight pricing is to yield a pricing strategy with maximum seat revenue. The state-of-the-art flight pricing approaches are built on forecasting the fare demand. They suffer low profit due to the inaccurate prediction. To tackle the above issue, an approach for dynamic flight pricing based on strategy learning is proposed. That approach resorts to reinforcement learning to output pricing strategy with the highest expected return. That strategy is learned by iteratively policy evaluation and policy improvement. The rate of profit improvement on the two flights is empirically 30.94% and 39.96% over the existing pricing strategy, while that rate is 6.04% and 3.36% over the demand forecasting algorithm.
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Key words:
- Revenue management /
- Dynamic flight pricing /
- Reinforcement learning /
- Strategy learning
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表 1 机票动态定价策略学习算法
输入 学习速率$\eta $，折扣因子$\gamma $，最大迭代次数${\rm{episodes}}$，航班总座位数$N$ 航班第1天到$T - 1$天的历史销售序列${\rm{\{ }}s_{\rm{0}}^{{\rm{(}}n)},a_{\rm{0}}^{(n)},r_{\rm{0}}^{(n)}, ··· ,s_v^{(n)},a_v^{(n)},r_v^{(n)}{\rm{\} }}_{n = {\rm{1}}}^{T - {\rm{1}}}$ 初始化 对于任何状态$s$和$\alpha ,$$q(s,\alpha ) = 0,k = 0,n = 1$ Repeat： Repeat (对于第1天到$T - 1$天的每趟离港航班)： Repeat (对于此趟航班历史销售序列的每一步$(s_t^{(n)},a_t^{(n)},r_t^{(n)},s_{t + {\rm{1}}}^{(n)})$)： 策略评估：据式(3)更新动作值函数$q(s_t^{(n)},a_t^{(n)})$ 策略更新：按式(4)调整策略$\pi (s_t^{(n)}) = \arg {\rm{ma}}{{\rm{x}}_\alpha }q(s_t^{(n)},a)$ Until 航班没有剩余座位或售票时间截止 $n \leftarrow n + 1$ Until $n > T - 1$ $k \leftarrow k + 1$ Until $k > {\rm{episodes}}$ 输出 第$T$天的机票动态定价策略$\pi (s) = \arg {\max _\alpha }q(s,\alpha )$ 
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表 2 旅客订票记录示例
身份证号 航空公司 航班号 出发机场 到达机场 出发日期 订单编号 票价等级 52893787 CA 1501 PEK SHA 20100308 2273651247 0.5213 55503718 CA 1501 PEK SHA 20100308 2745812364 0.8212
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表 3 实验数据集的统计信息
航班 售票记录
总数销售
序列数状态、动作等
四元组数原始票价等级
(精确到万分位)预处理后的票价
等级(精确到千分位)预处理后的票价
等级(精确到百分位)票价
等级数各等级
平均票数票价
等级数各等级
平均票数票价
等级数各等级
平均票数CA1501 130118 718 102809 5737 22.68 1087 119.70 150 867.45 JR1505 22691 611 17102 2359 9.62 745 30.46 90 254.96
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表 4 票价等级精确度影响分析
票价等级
精度训练集中票价
等级总数定价策略中出现
票价等级总数收益平均提升率
${\rm{ALR@T}}$(%)0.0001 4590 128 13.21 0.0100 120 16 16.38
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