基于Priceline的买方/卖方定价收益管理问题

以著名逆向拍卖网站 Priceline 为背景,研究买方定价和卖方定价下的收益管理问题.假定顾客到达是一任意的更新过程,决策时刻为顾客到达时刻,所以决策是离散时间的.建立了两种定价方式下的马氏决策过程模型,获得了最优策略的表达式.在传统收益管理问题中,通常是卖方定价、连续时间决策、同时需要假定顾客到达是一 Poisson 过程.对于买方定价,文中证明了,卖方是否知道到达顾客的报价信息不影响他的收益;同时,随着剩余物品数的增加,卖方的期望收益递增,而边际收益递减,最优价格(或报价)递减.文中讨论两种定价方式下卖方的期望收益之间的关系.考虑了顾客需求是多重的情形.最后,数值分析表明文中所得的结论是成立的.     

无人仓系统储位分配问题的优化模型与算法

考虑到无人仓系统补货阶段货架上只有部分空余储位的特点,研究了补货商品储位分配问题的优化模型与算法。以同一货架上存放的商品之间关联度之和最大化为目标建立了混合整数规划模型;结合贪婪算法和邻域搜索算法设计了求解模型的两阶段方法。第一阶段利用贪婪算法求初始可行解;第二阶段利用邻域搜索算法对初始可行解进行优化。利用一个具体算例验证了邻域搜索算法的优化效果,结果显示,通过邻域搜索算法对初始可行解的优化,可以使目标函数值至少提升27%。进一步利用多个小规模算例分析了两阶段算法的近似比和求解速度,验证了算法的快速有效性。本文的研究结果不仅解决了货架初始状态非空情况下的储位分配问题,同样适合解决货架初始状态为空的情况,因此更加符合实际场景,可以作为无人仓管理信息系统的核心模型和算法。     

基于收益管理的动态定价研究综述

动态定价作为收益管理的重要手段之一对提高企业收益具有重要作用。本文对收益管理动态定价问题的几个构成要素进行了分析;对国外动态定价基本模型的研究文献进行了简单的评述;重点对动态定价的最新研究热点进行了综述并对其发展前景进行了展望;最后介绍了动态定价在中国的研究现状,旨在推动其在中国的研究与应用。     

考虑空箱调运的集装箱海运收益管理能力分配优化模型

文章基于收益管理的思想对面临不确定需求的海运集装箱能力分配问题进行了定量研究。首先描述了集装箱海运收益管理问题与航空客运收益管理的差异,接着建立了考虑和不考虑空箱调运的海运集装箱能力分配随机规划模型,并应用稳健优化方法对模型进行求解,最后通过数值仿真,说明了模型和求解方法对于海运集装箱运输企业的收益管理问题具有重要的应用价值。     

基于团体客人的酒店收益管理探析

收益管理已经普遍应用于航空，酒店，银行等行业，尤其是酒店业。本文是基于团体客人的酒店收益管理，通过对团体客人特点的分析，并结合酒店的实际情况和散客预订的情况来对房价进行重新定价，以使酒店在合理定价的基础上获得更大的收益。     

收益的定价管理在网络空间租赁中的应用

将收益管理技术引入网络空间租赁领域,通过对网络空间特点的分析,得出影响供应商定价的5种因素:软硬件配置及基础设施建设、服务、租赁空间的大小、租赁时间和技术进步,并通过相关数据说明这些因素对供应商定价产生的影响。将收益管理与网络租赁产品价格设计联系起来,简要阐述了网络产品价格设计的全过程,并针对当前价格设计中存在的问题,提出了几种有效的解决方法。     

基于进化策略的企业混合并购收益动态定价模型

混合并购可能引起目标企业所在行业内其他企业对并购企业进入的阻挠,通过横向并购或者企业问的联合降低并购企业的收益,从而形成了并购企业的进入壁垒.进入壁垒的形成是所有目标企业所在行业内的其他企业反应策略进化的过程,将该过程中反应策略的变化引入到企业混合并购收益定价模型中,从而能够有效地衡量企业混合并购收益,并为并购定价决策奠定了基础.     

易逝性高科技产品收益管理定价策略

高科技产品作为一种典型的易逝性商品,其定价对于零售商的利润有着举足轻重的影响.基于收益管理思想,以获得最大期望利润为目标,考虑缺货时消费者的替代行为,建立了随机需求环境下基于多项logit顾客选择模型和服务水平的易逝性高科技产品收益管理定价策略模型.对建立的模型用单阶段算例进行了模拟分析,并讨论了不同顾客到达率、不同初始库存、产品对于消费者的不同影响度下的最优策略,得出了一系列比较有意义的性质和管理原则.     

基于收益管理的海运集装箱舱位分配与路径选择优化模型

文章应用收益管理的方法对不确定环境下海运集装箱的舱位分配问题进行了定量研究.首先针对海运收益管理的应用特征,建立了考虑空箱调运和路径选择的集装箱多航段能力分配模型,然后考虑需求的不确定性,应用稳健优化方法对此模型进行求解.最后,通过数值仿真得到了优化的舱位分配方案,比较发现稳健优化模型取得了较确定性规划模型更好的收益.显示了模型和方法对于集装箱海运企业的收益管理问题具有应用价值.     

集装箱班轮二维收益管理在线动态定价策略

为了在现实约束条件下最大化班轮公司收益,研究了集装箱海运二维收益管理多航段多箱型在线动态定价模型,提出了其最优在线动态定价策略,并且证明了模型价值函数的单调性及其上界.基于降维的思想提出了更为实际的启发式算法.在算例中分析了单航段单箱型、单航段多箱型和多航段多箱型3种情况下的最优动态定价策略,分析结果表明:在单航段单箱型的情况下,最优价格具有单调性;在单航段多箱型和多航段多箱型的情况下,最优价格不一定具有单调性.     

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

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。     

Pricing,Allocation, and Overbooking in Dynamic Service Network Competition When Demand Is Uncertain

We study the problem of combined pricing, resource allocation, and overbooking by service providers involved in dynamic noncooperative oligopolistic competition on a network that represents the relationships of the providers to one another and to their customers when service demand is uncertain. We propose, analyze, and compute solutions for a model that is more general than other models reported in the revenue management literature to date. In particular, previous models typically consider only three or four of five key revenue management features that we have purposely built into our model: (1) pricing, (2) resource allocation, (3) dynamic competition, (4) an explicit network, and (5) uncertain demand. Illustrative realizations of the abstract problem we study are those of airline revenue management and service provision by companies facing resource constraints. Under fairly general regularity conditions, we prove existence and uniqueness of a pure strategy Nash equilibrium for dynamic oligopolistic service network competition described by our model. We also show, for an appropriate notion of regularity, that competition leads to the underpricing of network services, a finding numerically illustrated by an example of intermediate size. Our proposed algorithm can be implemented using well‐known off‐the‐shelf commercial software.     

Up Then Down: Bid‐Price Trends in Revenue Management

In the classic revenue management (RM) problem of selling a fixed quantity of perishable inventories to price‐sensitive non‐strategic consumers over a finite horizon, the optimal pricing decision at any time depends on two important factors: consumer valuation and bid price. The former is determined exogenously by the demand side, while the latter is determined jointly by the inventory level on the supply side and the consumer valuations in the time remaining within the selling horizon. Because of the importance of bid prices in theory and practice of RM, this study aims to enhance the understanding of the intertemporal behavior of bid prices in dynamic RM environments. We provide a probabilistic characterization of the optimal policies from the perspective of bid‐price processes. We show that an optimal bid‐price process has an upward trend over time before the inventory level falls to one and then has a downward trend. This intertemporal up‐then‐down pattern of bid‐price processes is related to two fundamental static properties of the optimal bid prices: (i) At any given time, a lower inventory level yields a higher optimal bid price, which is referred to as the resource scarcity effect; (ii) Given any inventory level, the optimal bid price decreases with time; that is referred to as the resource perishability effect. The demonstrated upward trend implies that the optimal bid‐price process is mainly driven by the resource scarcity effect, while the downward trend implies that the bid‐price process is mainly driven by the resource perishability effect. We also demonstrate how optimal bid price and consumer valuation, as two competing forces, interact over time to drive the optimal‐price process. The results are also extended to the network RM problems.     

有多个销售渠道的连续时间收益管理问题研究

研究了有多个销售渠道的连续时间收益管理问题.利用最优控制理论,建立了最大期望收益函数所满足的HJB最优方程,证明了最大期望收益是剩余商品数量的递增凹函数、是销售时间的递减凹函数,最优定价策略随剩余商品数量及销售时间递减;得到了不同销售渠道的最优定价之间所满足的一个关系式.最后,利用数值仿真的方法对不同销售渠道差别定价的效果进行了讨论.     

A New Dynamic Programming Decomposition Method for the Network Revenue Management Problem with Customer Choice Behavior

In this paper, we propose a new dynamic programming decomposition method for the network revenue management problem with customer choice behavior. The fundamental idea behind our dynamic programming decomposition method is to allocate the revenue associated with an itinerary among the different flight legs and to solve a single‐leg revenue management problem for each flight leg in the airline network. The novel aspect of our approach is that it chooses the revenue allocations by solving an auxiliary optimization problem that takes the probabilistic nature of the customer choices into consideration. We compare our approach with two standard benchmark methods. The first benchmark method uses a deterministic linear programming formulation. The second benchmark method is a dynamic programming decomposition idea that is similar to our approach, but it chooses the revenue allocations in an ad hoc manner. We establish that our approach provides an upper bound on the optimal total expected revenue, and this upper bound is tighter than the ones obtained by the two benchmark methods. Computational experiments indicate that our approach provides significant improvements over the performances of the benchmark methods.     

Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities

Customer behavior modeling has been gaining increasing attention in the operations management community. In this paper we review current models of customer behavior in the revenue management and auction literatures and suggest several future research directions.     

A Dynamic Disaggregation Approach to Approximate Linear Programs for Network Revenue Management

The linear programming approach to approximate dynamic programming has received considerable attention in the recent network revenue management (RM) literature. A major challenge of the approach lies in solving the resulting approximate linear programs (ALPs), which often have a huge number of constraints and/or variables. Starting from a recently developed compact affine ALP for network RM, we develop a novel dynamic disaggregation algorithm to solve the problem, which combines column and constraint generation and exploits the structure of the underlying problem. We show that the formulation can be further tightened by considering structural properties satisfied by an optimal solution. We prove that the sum of dynamic bid‐prices across resources is concave over time. We also give a counterexample to demonstrate that the dynamic bid‐prices of individual resources are not concave in general. Numerical experiments demonstrate that dynamic disaggregation is often orders of magnitude faster than existing algorithms in the literature for problem instances with and without choice. In addition, adding the concavity constraints can further speed up the algorithm, often by an order of magnitude, for problem instances with choice.