基于需求分类和乘客升级购买行为的航空公司收益管理模型

研究了基于乘客分类的航空客运库存控制与动态定价策略.模型中,航空公司以提供折扣票的方式将乘客分为两类,并针对购买折扣票的乘客存在升级购买行为,通过动态的控制折扣票的销售和对机票实施动态定价来最大化自身的期望收益.应用动态规划建立了相应的收益管理模型,讨论了最优定价应满足的关系式,并得到了接受或拒绝乘客购买折扣票的阈值.最后,通过算例分析了升级购买概率对阈值、机票的价格及期望收益的影响.     

竞争环境下基于乘客分类的航空客运机票控制和动态定价综合模型

假定航空公司以提供折扣票的方式将乘客分为两类,并通过动态控制折扣票的销售和对普通票实行动态定价来最大化自身的期望收益.应用动态规划建立了竞争环境下两航空公司的折扣机票控制和普通机票动态定价综合模型,讨论了普通机票价格均衡的存在性,并给出航空公司基于已卖出的机票数量接受或拒绝乘客购买折扣票的阈值.最后,通过算例讨论了不同参数对均衡价格、阈值和期望收益的影响.     

乘客策略行为下考虑需求学习的航空客运动态定价策略研究

研究航空公司在需求学习下的动态定价策略。通过假设乘客到达率不确定以及具有策略等待行为,运用贝叶斯理论和博弈论对航空公司需求学习下的多周期动态定价问题进行建模,探讨了机票最优定价策略的充分条件,并通过分析航空公司收益函数的性质,得到了最优定价随时间和已出售机票数量的变化趋势。最后应用算例分析了需求学习的效果,得出:需求学习能够缓解需求不确定带来的损失,但不能完全消除;乘客策略程度越大,需求学习效果越明显。     

考虑乘客取消订单的网约车平台定价策略

针对乘运市场供需不匹配的情况,考虑乘客在网约车平台预约订单后的取消行为,以平台利润最大化为目标,首先建立乘客选择模型计算乘客取消订单概率,再分别构建市场供过于求和供不应求状态下的利润模型,求解平台最优定价。研究表明:制定适当的违约规则可以有效减少乘客取消订单的概率,提高平台利润;最优定价随着服务质量的提高而增加,在打车非高峰期,平台可以通过提高服务质量来增加平台利润;非高峰期平台最优定价随着出租车费用的增加而减少,而高峰期定价策略受出租车费用影响较小。     

航空直飞与空铁联运模式竞争研究

空铁联运能够缓解机场拥堵问题,合理分配客流量,改善航空和高铁日趋激烈的竞争局面.文章基于航空与空铁联运二者运营者的视角,构建航空直飞与空铁联运两种出行方式竞争模型,在此基础上研究均衡解并分析影响均衡状态客流量的因素.研究表明,当两种出行方式的时长差越小,乘客对机场拥堵越敏感,高铁票价越低,乘客越偏向空铁联运.当乘客出行...     

基于Multi-Logit模型的多机场系统航线补贴研究

随着我国民航业快速发展,机场密度不断提高,在一定地域范围内存在多个机场的现象.然而多机场系统内机场间竞争激烈,其中航线补贴已经成为国内大部分机场激励航空公司增加运力、开拓更多航线的重要手段.对于机场来说,如何科学的补贴很值得研究.从乘客出行选择角度着眼,利用Multinomial Logit模型考察了航线补贴对机场及航空公司的市场份额、机场自身利润及社会总收益的影响,从而给出补贴建议.研究表明:1)多机场系统内,一家航空公司的航线补贴增加时,这家航空公司及所在机场的市场份额增加,其余机场及航空公司市场份额均有所下降.2)多机场系统内大型机场补贴新航班航线时,对市场占有率高的航空公司补贴增加,航空公司市场份额、机场在这条航线上总利润以及社会总收益都随之增加.3)多机场系统内两家邻近中小机场,根据两家机场单位航线补贴方案不同,航空公司市场份额、机场在这条航线上总利润以及社会总收益也相应不同.     

航空公司微博评论的意见信息抽取研究——以国航、南航和东航为例

如今越来越多的乘客选择乘坐舒适快捷的飞机出行,中国航空运输需求因此逐年增长,航空公司在获得更多盈利空间的同时也面临激烈的竞争.对航空公司的用户评论进行意见信息抽取,不仅可用于航空公司改进服务质量和用户体验,还可为用户选择满意的航空公司提供参考.文章首次以新浪微博平台上航空公司的用户评论为基础数据,利用条件随机场进行意见信息抽取.在有关研究中,专家学者大多凭借以往知识的了解对特征对象和特征词进行人工标注,鲜少分析用户在本评论语料中的关注点.因此,文章创新性地在人工标注前首先利用TF-IDF算法进行关键词提取,找到本评论语料中用户的关注点,最后以超过93%的F平均值证明模型的有效性,为后续的研究提供了新方向.     

乘客到达时间不确定的机场动态拼车策略与算法研究

网约车拼车服务作为共享经济领域重要应用,已成为国内外研究热点。针对机场在线拼车平台运营中乘客等待时间过长和车辆行驶成本较高的突出问题,本文提出前瞻式动态拼车匹配策略。该策略将未来随机到达乘客信息纳入当前已到达乘客的拼车匹配决策中,建立了乘客匹配与车辆路径联合优化两阶段随机规划模型。为了在动态环境中实时产生高质量的匹配与路径规划方案,首先基于贝叶斯估计压缩乘客随机到达情景空间,建立了问题的确定性近似最优模型。为了快速求解模型,提出基于订单目的地和乘客期望到达时间相似度的匹配规则,并以此开发改进的差分进化算法。最后,基于某拼车平台真实订单数据,通过对比测试验证了前瞻式匹配策略和改进差分进化算法的有效性与计算效率。     

考虑质量成本的居家养老服务平台定价策略研究

人口老龄化背景下,为满足老年人日益增长的高质量养老服务需求,共享养老互联网平台开始涌现.为了探讨从服务质量角度研究质量成本投入对居家养老服务平台利润的影响,建立了以平台利润最大化为决策目标的考虑质量成本居家养老服务定价模型,构建长者和养老服务提供方效用函数,分别讨论了居家养老服务平台双方质量成本对最优定价、最优消费者剩余、最优生产者剩余以及平台最大利润的影响,并运用Matlab软件进行模拟分析,给出平台基于质量成本的最优定价策略.研究发现:居家养老服务平台增加双方质量成本投入会使得平台向长者和养老服务提供方收取较高的价格,同时带来双方体验感增加,平台网络外部性增强,有利于居家养老服务平台利润提升,最终促进共享养老产业健康发展.     

考虑桥载设备使用的机场和航空公司运行成本研究

分析了桥载设备在机场和航空公司推广所面临的问题和原因,从航空公司运行的角度讨论桥载设备的使用能否有效地节约航空公司运行成本,分析了不同机型使用桥载设备的成本优化率;然后从机场运行的角度构建了桥载设备静态投资回收周期计算模型,重点讨论了桥载设备套数和使用时间对其静态投资回收周期的影响.实践表明,使用桥载设备代替APU可实现机场、航空公司、旅客三赢:机场可收取桥载设备使用费增加收入,能在较短的时间内回收桥载设备的投资成本;航空公司可有效地节省燃油和APU维修等运行费用;使用桥载设备后可减少碳氮气体排放和噪声污染,改善旅客的航空体验.研究结果能够增强机场和航空公司使用桥载设备的信心和决心,能有效地推动民航节能减排政策在机场的实施.     

IBM journey management library: an arena system for airport simulations

Airline passenger terminal congestion caused by increasing passenger traffic results in unsatisfactory levels of customer service. We discuss a simulation modelling tool to help airlines and airports to use advanced technology to improve service to passengers. The tool consists of custom designed, reusable modules that represent the most common airline and airport system data, logic and processes. A model of an actual airline operation based on this approach is described.     

基于数字仿真的收益管理整合决策

对航空公司收益管理进行机票定价和座位存量分配的整合研究。应用计算机仿真算法动态构造民航收益管理系统中的需求预测模型,并根据航班收益最大化原则,确定价格与座位存量分配,根据需求变化实时调整价格和座位存量。仿真运算结果显示,该算法可以使航空公司不同航班收益比固定价格提高2%以上。     

Revenue management for low-cost providers

Low-cost providers have emerged as important players in many service industries, the most predominant being low-cost, or the so-called discount airlines. This paper presents models and results leading toward understanding the revenue management outlook for a discount pricing firm. A framework and model is formulated specifically for the airline industry, but is generalizable to low-cost providers in similar revenue management settings. We formulate an optimal pricing control model for a firm that must underprice to capture a segment of exogenous demand. Two specific model formulations are considered: a continuous deterministic version, and a discrete stochastic version. Structural results are derived for the deterministic case, providing insight into the general form of optimal underpricing policies. The stochastic results support the structural insight from the deterministic solution, and illuminate the effect of randomness on the underpricing policies.     

Passenger grouping under constant threat probability in an airport security system

In a typical security system at a US airport, there are a series of inspections done on passengers as well as on baggage to check whether any item of threat is entering into the system. Considering the large number of passengers and items using our airports, one hundred percent check on all the items is not practical. This paper investigates the benefit of classifying passengers into different groups, with the idea that the number of checks and the degree of inspection may vary for different groups. The threat probability is assumed to be known and identical for all passengers. We develop a model to determine the number of groups, the fractions of passengers and the assignment of check stations for each group. The constraint is that the false clear probability is within Federal of Aviation Administration (FAA) specifications, and the objective is that the number of false alarms (a surrogate measure of passenger inconvenience) in the system is minimized. The model studies the effect of the system parameters on the number of items checked at various check stations and on the false alarm rate. The major conclusion of this paper is that passenger grouping is beneficial even when the threat probability is assumed constant across all passengers. A further conclusion is that the optimal grouping and check station assignments can depend on the overall threat probability.     

Network capacity management under competition

We consider capacity management games between airlines who transport passengers over a joint airline network. Passengers are likely to purchase alternative tickets of the same class from competing airlines if they do not get tickets from their preferred airlines. We propose a Nash and a generalized Nash game model to address the competitive network revenue management problem. These two models are based on well-known deterministic linear programming and probabilistic nonlinear programming approximations for the non-competitive network capacity management problem. We prove the existence of a Nash equilibrium for both games and investigate the uniqueness of a Nash equilibrium for the Nash game. We provide some further uniqueness and comparative statics analysis when the network is reduced to a single-leg flight structure with two products. The comparative statics analysis reveals some useful insights on how Nash equilibrium booking limits change monotonically in the prices of products. Our numerical results indicate that airlines can generate higher and more stable revenues from a booking scheme that is based on the combination of the partitioned booking-limit policy and the generalized Nash game model. The results also show that this booking scheme is robust irrespective of which booking scheme the competitor takes.     

Virtual queuing at airport security lanes

Airports continuously seek opportunities to reduce the security costs without negatively affecting passenger satisfaction. In this paper, we investigate the possibilities of implementing virtual queuing at airport security lanes, by offering some passengers a time window during which they can arrive to enter a priority queue. This process could result in a smoother distribution of arriving passengers, such that the required security personnel (costs) can be decreased. While this concept has received attention in a number of settings, such as theme parks, virtual queuing at airports bears an additional level of complexity related to the flight schedules, i.e., passengers can only be transferred forward in time to a limited extent, which we denote by the transfer time limit. We conducted a major simulation study in collaboration with a large international airport in Western Europe to determine the potential impact of virtual queuing and find that nearly one million Euro can be saved on security personnel cost without negatively impacting the passenger waiting time.     

A continuous-time dynamic pricing model knowing the competitor’s pricing strategy

In this paper we consider a dynamic pricing model for a firm knowing that a competitor adopts a static pricing strategy. We establish a continuous time model to analyze the effect of dynamic pricing on the improvement in expected revenue in the duopoly. We assume that customers arrive to purchase tickets in accordance with a geometric Brownian motion. We derive an explicit closed-form expression for an optimal pricing policy to maximize the expected revenue. It is shown that when the competitor adopts a static pricing policy, dynamic pricing is not always effective in terms of maximizing expected revenue compared to a fixed pricing strategy. Moreover, we show that the size of the reduction in the expected revenue depends on the competitor’s pricing strategy. Numerical results are presented to illustrate the dynamic pricing policy.     

基于熵值Topsis和博弈动力学的空港联盟决策研究

针对空港在面临联盟选择时的决策问题进行了研究,认为众空港在博弈中的收益为不等值,从区域发展、生产要素、需求状况、支撑产业和环境影响五方面建立空港联盟收益的指标体系,将空港网络抽象成小世界网络,结合熵值Topsis和小世界网络博弈动力学建立空港联盟决策算法,并且运用2010年我国25大空港的实证数据进行研究。结论显示空港联盟只能解决空港发展的一时问题,提升自身能力才是重中之重,研究结论具有实际应用意义。