基于粒子群算法的航班舱位控制研究

首先介绍了收益管理提出的背景思想,在对航班收益管理基本概念作简要阐述的情况下,给出了期望边际座位收入理论.提出了在北京到上海的单航程航段下,航空公司会提供出发时间不同但航程相同的连续两个航班.旅客在没能购买到期望的航班舱位时,会以一定的概率选择购买下一航班的机票或者取消订票,改乘其他交通运输工具.旅客对于某一航班的座位需求主要由固有需求和上一航班需求的转移两部分构成.基于这样的角度建立了两航班机票预售模型,不考虑动态订座,no-show和超售等情况,并利用算法对模型进行了求解和分析.最后得出结论当航班票价等级数量较少时,为高票价等级安排较多数量座位可提高航班收入.而当航班票价等级数量比较多时,为低票价等级多安排一些座位可为航班提高收入.     

基于损失厌恶的Littlewood容量控制准则

在传统的航空收益管理单航段两种票价等级的容量控制准则—Littlewood准则容量控制模型基础上考虑有限理性决策中的损失厌恶特性和参照依赖属性,以盈亏均衡点为基点,引入具有损失厌恶特征的效用函数,在Kahneman和Tversky预期理论(Prospect Theory,PT)的框架下,给出单航段两种票价等级的不同损失厌恶程度下高等级座位保护水平的计算公式.理论分析和数值算例均表明,随着损失厌恶程度的增加,在其他条件不变的前提下,高等级座位的保护水平将不断降低.     

考虑决策目标的FIC模型选择

收益管理优化是提高零售商经济收益的有效途径之一.定价是收益管理的引擎和核心技术,对于提高零售商的收益具有重要作用.考虑到在收益管理的实际应用中,预测和优化问题的复杂性,通常采用先对产品需求进行预测,然后对收益进行优化的步骤.在对产品需求进行预测时,通常会面临多个候选模型,即面临模型的不确定性,这时一般会采用模型选择方法确定最终的模型.但传统的模型选择准则包括赤池信息准则(Akaike information criterion,AIC),贝叶斯信息准则(Bayesian information criterion,BIC)等通常只考虑了模型选择对预测精度的影响,而不考虑该预测模型会如何影响接下来的优化决策目标.本文首次在商品的收益管理优化中提出最小化聚焦信息准则(focused information criterion,FIC)这种模型选择准则,运用FIC模型选择准则选择产品需求预测模型,考虑了优化模型的结构,以最小化决策误差,而不是预测误差为目标,来选择预测模型.数值模拟结果表明,在大部分情况下,相比于AIC和BIC两种模型选择准则,考虑决策目标的FIC模型选择准则表现最佳.同时,...     

公路客运监管的进化博弈分析

运用进化博弈理论研究公路客运监管问题,建立了公路客运监管问题的博弈模型,分析了公路客运车主和公路客运管理者之间的行为选择,得到了博弈方的复制动态方程,研究了博弈模型的进化稳定策略。探讨了影响进化稳定策略的因素。研究结果表明公路客运车主和公路客运管理者在有限理性基础上得到的进化稳定策略与博弈双方的收益、系统所处的初始状态有关,并根据所提出的博弈模型,提出了合理性建议。     

弹性需求下城市公交网络服务的优化

对具有弹性需求的城市公交网络系统进行了票价结构与发车频率组合的优化。考虑到公交定价和发车频率会影响乘客需求以及乘客对路径的选择行为,将这一问题描述为一个双层规划问题,上层是寻求社会福利最大的优化问题;下层考虑了乘客的出行选择行为,为弹性需求下乘客在城市公交网络上流量分布的随机用户平衡分配模型。鉴于双层规划问题的非凸性,运用模拟退火算法对模型进行求解,并给出一个仿真算例说明提出的模型和算法的合理性。     

铁路客运网络列车开行方案优化模型的列生成算法

列车开行方案的设计是铁路旅客运输组织规划中的一个重要环节。本文首先给出了一个综合考虑铁路旅客运输的经济效益和公共服务性的优化模型,以铁路旅客运输的公共效益最大化为目标,对整个铁路客运网络上不同始发-终到和不同停站方式的列车开行方案进行优化。然后提出了一个求解此模型的启发式列生成算法,该算法与标准列生成算法相比,可以减少迭代次数并缩短收敛时间。最后给出一组利用随机生成的网络和需求进行求解的算例,验证本算法可以在较短时间内求解较大规模的铁路网络列车开行方案优化问题,并能有效缩小问题规模。     

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.     

多方式弹性需求下公交定价双层规划模型

为描述多方式城市交通网络下公交定价与出行选择行为的相互作用与影响,将出行方式选择与路径选择涵盖于同一网络,建立了上层模型分别以企业利润最大化、乘客出行成本最小化和社会福利最大化为目标函数,下层模型为多方式弹性需求随机用户配流模型的公交定价双层规划模型。运用改进遗传算法对模型整体进行求解,下层模型采用综合对角化算法和MSA算法的组合求解算法。最后,设计了一个算例以说明模型应用。结果表明:运用双层规划模型所确定的公交票价较传统静态票价可使政府、企业及出行者三方都获得更高收益,且上层模型以社会福利最大化为目标函数能代表社会群体中多数人利益,优化效果最为理想。     

两阶段可修串联系统的变点控制图与维修策略整合研究北大核心CSCD

针对两阶段串联可修系统,考虑系统的输出为多个质量特性且不同阶段均可能出现异常的情形,采用变点控制图监控系统,揭示系统的状态并据此进行相应的维护策略.首先,给出监控多阶段系统的变点控制图.其次,考虑两阶段过程均可能出现异常因素的情形,剖析过程演变可能的场景,进一步假设异常因素的发生服从一般分布,给出每个场景发生的概率;同时,分析维修行为发生的概率.再次,根据更新报酬理论构建变点控制图与维修策略整合的期望收益模型;采用具体实例来比较分析所提出的收益模型与单独的维修策略的收益模型,其结果表明构建的模型的明显优势;最后,运用分式析因设计对模型输入参数进行了敏感性分析.     

航空公司超额订票的收益分析

研究了航空公司超额预订机票的收益问题 .通过建立多等级票价模型 ,分别对一个航班和两个航班超额订票的收益进行研究 ,并讨论收益对预订票数、未到乘客数等参量的敏感性 .     

Airline network revenue management by multistage stochastic programming

A multistage stochastic programming approach to airline network revenue management is presented. The objective is to determine seat protection levels for all itineraries, fare classes, points of sale of the airline network and all dcps of the booking horizon such that the expected revenue is maximized. While the passenger demand and cancelation rate processes are the stochastic inputs of the model, the stochastic protection level process represents its output and allows to control the booking process. The stochastic passenger demand and cancelation rate processes are approximated by a finite number of tree structured scenarios. The scenario tree is generated from historical data using a stability-based recursive scenario reduction scheme. Numerical results for a small hub-and-spoke network are reported. This research is supported by the DFG Research Center Matheon “Mathematics for key technologies” in Berlin.     

Revenue impacts of fare input and demand forecast accuracy in airline yield management

Most airline yield management seat allocation models require inputs of the expected demand by fare class, the variance of this demand, and a revenue value associated with the bookings expected in each class. In this paper, we examine the impacts of errors in the demand forecasts and fare estimates on the revenue performance of some commonly used seat allocation heuristic decision rules. Through simulation analysis of scenarios in which the fare or demand inputs used by the models differ from the ‘actual’ values simulated in the flight booking process, we examine the effects of unexpected variability in the actual fare values, misestimation of the mean fare values of the different booking classes, and forecasting errors in the expected demand for each class. Our findings confirm previous studies that found the accuracy of the demand forecasts to be of greatest importance, but we also uncover some instances where misestimation of the mean demands and/or mean fare values used as inputs to the decision models can actually be beneficial. At the same time, we conclude that the variability of actual fare values around the mean fare values used as inputs does not have a significant impact, given the mathematical characteristics of existing EMSR seat allocation methods.     

Dynamic simultaneous fare proration for large-scale network revenue management

Network revenue management is concerned with managing demand for products that require inventory from one or several resources by controlling product availability and/or prices in order to maximize expected revenues subject to the available resource capacities. One can tackle this problem by decomposing it into resource-level subproblems that can be solved efficiently, for example by dynamic programming. We propose a new dynamic fare proration method specifically having large-scale applications in mind. It decomposes the network problem by fare proration and solves the resource-level dynamic programs simultaneously using simple, endogenously obtained dynamic marginal capacity value estimates to update fare prorations over time. An extensive numerical simulation study demonstrates that the method results in tightened upper bounds on the optimal expected revenue, and that the obtained policies are very effective with regard to achieved revenues and required runtime.     

A framework for solving network revenue management problems with customer choice behavior

Revenue management is the process of understanding, anticipating and influencing consumer behavior in order to maximize revenue. Network revenue management models attempt to maximize revenue when customers buy bundles of multiple resources. The dependence among the resources in such cases is created by customer demand. Network revenue management can be formulated as a stochastic dynamic programming problem whose exact solution is computationally intractable. Solutions are based on approximations of various types. Customer choice behavior modeling has been gaining increasing attention in the revenue management. A framework for solving network revenue management problems with customer choice behavior is proposed. The modeling and solving framework is composed from three inter-related network structures: basic network model, Petri net, and neural net.     

Network revenue management with inventory-sensitive bid prices and customer choice

We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.     

基于收入管理的CRM策略优化研究

在以客户关系为竞争导向的服务经济时代,有效的收入管理必须深入的结合客户关系管理策略.利用赋值马尔科夫过程方案决策的基本理论构建起了基于收入管理的CRM策略优化方案决策的随机模型,并对该模型方法进行了实证应用.结果表明通过应用该模型方法进行CRM策略的优化选择后,企业在加强客户关系承诺的同时能有效的提升其收入管理水平.讨论了研究结论对企业管理实践的意义.     

Mathematical programming models for revenue management under customer choice

We study a network airline revenue management problem with discrete customer choice behavior. We discuss a choice model based on the concept of preference orders, in which customers can be grouped according to a list of options in decreasing order of preference. If a customer’s preferred option is not available, the customer moves to the next choice on the list with some probability. If that option is not available, the customer moves to the third choice on the list with some probability, and so forth until either the customer has no other choice but to leave or his/her request is accepted. Using this choice model as an input, we propose some mathematical programs to determine seat allocations. We also propose a post-optimization heuristic to refine the allocation suggested by the optimization model. Simulation results are presented to illustrate the effectiveness of our method, including comparisons with other models.     

Revenue management model for on-demand IT services

This paper presents a model for applying revenue management to on-demand IT services. The multinomial logit model is used to describe customer choice over multiple classes with different service-level agreements (SLAs). A nonlinear programming model is provided to determine the optimal price or service level for each class. Through a numerical analysis, we examine the impacts of system capacity and customer waiting incentives on the service provider’s profit and pricing strategies.     

Improved bid prices for choice-based network revenue management

One of the latest developments in network revenue management (RM) is the incorporation of customer purchase behavior via discrete choice models. Many authors presented control policies for the booking process that are expressed in terms of which combination of products to offer at a given point in time and given resource inventories. However, in many implemented RM systems—most notably in the hotel industry—bid price control is being used, and this entails the problem that the recommended combination of products as identified by these policies might not be representable through bid price control. If demand were independent from available product alternatives, an optimal choice of bid prices is to use the marginal value of capacity for each resource in the network. But under dependent demand, this is not necessarily the case. In fact, it seems that these bid prices are typically not restrictive enough and result in buy-down effects.We propose (1) a simple and fast heuristic that iteratively improves on an initial guess for the bid price vector; this first guess could be, for example, dynamic estimates of the marginal value of capacity. Moreover, (2) we demonstrate that using these dynamic marginal capacity values directly as bid prices can lead to significant revenue loss as compared to using our heuristic to improve them. Finally, (3) we investigate numerically how much revenue performance is lost due to the confinement to product combinations that can be represented by a bid price.The heuristic is not restricted to a particular choice model and can be combined with any method that provides us with estimates of the marginal values of capacity. In our numerical experiments, we test the heuristic on some popular networks examples taken from peer literature. We use a multinomial logit choice model which allows customers from different segments to have products in common that they consider to purchase. In most problem instances, our heuristic policy results in significant revenue gains over some currently available alternatives at low computational cost.     

基于Logit离散选择模型的品类优化问题综述

品类优化问题(Assortment Optimization Problem)是收益管理的经典问题.它研究零售商在满足运营约束的前提下,应如何从给定产品集合中选择一个子集提供给消费者,以最大化预期收益.该问题的核心在于如何准确地刻画消费者在面对细分产品时的选择行为、建立相应的优化模型并设计高效率的求解算法.基于Logit离散选择模型的品类优化问题:首先,介绍了基于Multinomial Logit模型的品类优化问题.然后介绍了两个更复杂的变种:第一个是基于两层以及多层Nested Logit模型的品类优化问题,这类问题可合理刻画细分产品之间的"替代效应";第二个是基于Mixtures of Multinomial Logits模型的品类优化问题,这类问题可充分考虑消费者群体的异质性.随后,介绍了数据驱动的品类优化问题的相关进展.最后,指出该问题未来可能的若干研究方向.