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中国管理科学 ›› 2019, Vol. 27 ›› Issue (8): 172-180.doi: 10.16381/j.cnki.issn1003-207x.2019.08.017

• 论文 • 上一篇    下一篇

考虑旅客跨区间流转的机票多预定区间折扣优化模型研究

熊浩1, 鄢慧丽2   

  1. 1. 海南大学管理学院, 海南 海口 570228;
    2. 海南大学旅游学院, 海南 海口 570228
  • 收稿日期:2017-09-06 修回日期:2018-01-25 出版日期:2019-08-20 发布日期:2019-08-27
  • 通讯作者: 鄢慧丽(1980-),女(汉族),湖北襄阳人,海南大学旅游学院,博士,副教授,研究方向:旅游供应链管理及收益管理,E-mail:yhl_yanhuili@126.com. E-mail:yhl_yanhuili@126.com
  • 基金资助:
    海南省哲学社会科学规划课题资助项目(HNSK(YB)19-06,HNSK(YB)19-11);国家自然科学基金资助项目(71761009,71461006,71461007);海南省自然科学基金创新研究团队项目(2019CXT402)

An Intertemporal Pricing Optimization Model of the Airline Ticket based on Revenue Management

XIONG Hao1, YAN Hui-li2   

  1. 1. Management School, Hainan University, Haikou 570228, China;
    2. School of Tourism, Hainan University, Haikou 570228, China
  • Received:2017-09-06 Revised:2018-01-25 Online:2019-08-20 Published:2019-08-27

摘要: 多预定区间差异化折扣逐渐成为机票收益管理的重要分支。本文提出了一种新的收益管理模型:基于顾客跨区间流转的收益管理模型,并给出了二分法迭代求解方法。假设各个预订时间区间的潜在需求可以通过大数据手段进行预测,首先结合旅客的价格敏感和潜在需求跨时间段流转的特性分析了各区间的需求函数,然后结合需求函数构建了多预定区间折扣优化模型。由于该模型属于动态的收益管理模型,因此构建了一种动态求解方法——二分迭代法。最后,依据航空公司的实际情况设计了两个仿真实验。实验计算结果不仅验证了新模型和算法的有效性,而且得出一些比较有用的结论:(1)票价与提前购票时间不存在单调的线性关系;(2)预订区间远离离港日折扣逐渐变大,靠近离港日的折扣会逐渐减少,但是包含离港日的预订区间的折扣又会变大;(3)流转率越高则折扣越少;(4)价格敏感系数越高折扣越高;(5)流转率通过改变价格敏感系数而影响折扣的大小。本文给出的折扣优化决策模型符合旅游产品多预定区间折扣决策的实践,可以为机票、酒店、景区等多种旅游产品的票价决策提供有益参考。

关键词: 机票定价, 折扣优化, 多预定区间, 收益管理

Abstract: Multiple booking interval difference discount gradually became an important branch of revenue management of airline ticket. In this paper, a new revenue management model are proposed and a dynamic iterative method to solve it. Firstly, the new revenue management model considering the price sensitivity and the demand transform among the booking interval. It is assumed that potential demand emerging in each specific booking interval can be predicted by big data method. Then the realistic demand can be formed as a function of the potential demand and price sensitivity and price discount. And the price sensitive is affected by the potential demand emerged in the interval and the demand transformed to it from the last interval. Secondly, a two-partition dynamic iterative method is presented to solve the model. In each iteration of the solving method, except the earliest one of the left booking intervals, all the others are treated as a temporary whole interval. Then a two-interval model is solved to determinate the price discount of the earliest booking interval. And then, drop that booking internal to the solved set and start another iteration until the last two intervals. Finally, two experimental examples are constructed. From the empirical results, this new pricing model conforms to the aviation ticket pricing practices. And some useful results are also proposed:(1) there is no monotonic linear relationship between the fare and the advance purchase time. (2) When the booking interval is closer to the flying date the optimal discount should be decreased. However, when the booking interval tend to reach the flying date the price discount should be increased again. (3) Price sensitivity is positively related to the price discount. (4) The transformation rate has a negative relationship with the discount. (5) The demand transformation can change the price sensitivity of the booking interval, which lead to impact the price discount. So, some beneficial reference is provided for airlines and passengers.

Key words: airline pricing, discount optimization, multiple booking interval, revenue management

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