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.     

海运集装箱收益管理舱位动态控制策略研究

文章基于收益管理的思想对不确定环境下集装箱班轮的动态舱位分配问题进行了定量研究.考虑了班轮运输业中的团体预订行为,建立了离散时间、离散状态的随机动态规划模型,并分别对考虑团体需求和不考虑团体需求模型的性质进行了分析,提出了在不考虑团体需求情况下的阈值控制策略并指出了在考虑团体需求情况下该策略不成立的情况,最后,用数值仿真对以上性质和策略进行了验证.     

考虑长期运力合同的班轮收益管理运输路径优化模型

基于收益管理的方法,文章对随机需求环境下班轮运力分配和路径优化问题进行了定量研究。首先针对海运收益管理的特征,建立了考虑长期运力合同、空箱调运的轮运力分配和路径选择随机规划模型,然后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。     

考虑船舶速度偏差的集装箱班轮运输货运收益鲁棒优化

由于受到天气、海况及航路环境等不确定因素的影响,船舶实际航速常常与计划航速产生偏差,导致班轮运输的航次加油和货物装运策略也产生变化,进而对船舶燃油成本和运费收入产生影响。因此,考虑速度偏差的集装箱班轮运输收益优化研究具有重要的现实意义。为了保证速度偏差不确定性下的各航段船舶航行用油安全,以船舶到达下一挂靠港的船期时间窗为约束,推导得出速度偏差最大情况下的船舶航段最大燃油消耗量显性函数,结合各加油港燃油价格及折扣差异,以及集装箱货物各O-D流量及运费率差异,以班轮运输航次收益鲁棒优化为目标,构建了速度偏差下的集装箱班轮运输收益混合整数非线性规划模型,设计分段线性割线逼近算法进行模型求解。以中国远洋海运集团有限公司的MEX航线数据为实际算例进行了分析验证,结果显示：考虑速度偏差的班轮运输加油和货物装运全局鲁棒优化策略能够有效地提高班轮运输航次货运收益;随着船舶燃油消耗系数和各航段速度偏差极值的增加,班轮运输航次燃油消耗量也随之增加,而航次运费收入和货运收益都将随之减少。研究结论可为船公司制定速度偏差下的集装箱班轮运输决策提供有益的参考。     

基于收益管理的邮轮客舱分配与定价模型

基于收益管理的思想对邮轮客舱分配与定价问题进行了研究。结合邮轮运营中的个性化特点,例如消费者团体人员构成多样、较长的预售周期以及救生位和儿童看护人员的数量限制等。在不失一般性的前提下,建立了整数规划模型用以确定在预售周期内的不同预售阶段中各种客舱类型的待售数量及其价格,以达到使邮轮公司收益最大化的目的。实验分析表明,该模型在实际应用中是有效的且呈现出显著的年增长趋势,可明显提高邮轮公司的收益。此外,设计了一种基于韦伯分布的EM算法用以解决模型中涉及到的需求量的无约束估计问题。数值算例研究表明,该算法收敛速度快且无约束估计过程可靠有效。     

高速铁路客运专线的收益管理模型

本文在分析铁路客运收益管理的研究进展的基础上,提出了一个适合铁路客运专线的收益管理优化模型。模型以列车运营总收益最大化为目标,优化列车的席位控制和发车间隔,将席位分配与运营能力优化统一在一个模型中。利用随机生成数据进行的模型试验表明,模型可以在较短的时间内求解较大规模的收益管理优化问题。     

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.     

鲁棒优化理论视角下餐饮收益管理的策略分析

基于酒店餐饮价值的易逝性以及顾客需求的波动性,如何实现餐饮销售收益最大化一直是困扰酒店餐饮行业的一大难题。本文在鲁棒优化原理的指导下提出了关于餐饮收益管理的对策。     

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。     

集装箱码头泊位计划干扰恢复多目标模型

研究集装箱码头中干扰事件发生后泊位计划的调整问题,目的是降低干扰事件对集装箱码头作业系统的干扰.基于干扰管理方法,建立泊位计划干扰恢复多目标、多阶段模型,该模型考虑码头不同客户的特点以及多方利益的平衡,从码头作业成本、船舶延误以及计划偏离度三个方面度量系统扰动.为求解模型,提出了基于字典续的求解方法,并利用算例对模型与算法的有效性进行了验证.结果表明:该模型与算法可以有效解决泊位计划调整问题,模型能够考虑各方的利益以及码头各类客户的特点,因此得到的泊位调整方案更科学,同时,模型各目标的重要顺序可根据情况进行调整,实用性与可操作性更高.     

Optimal Dynamic Upgrading in Revenue Management

We consider a revenue management problem involving a two compartment aircraft flying a single leg, with no cancellations or over‐booking. We apply the practice of transforming a choice revenue management model into an independent demand model. Within this assumed independent model, there are two sets of demands, business and economy, each with multiple fare class products. A business passenger can only be accepted into business. An economy passenger can be accepted into economy or upgraded into business. We define a two‐dimensional dynamic program (DP) and show that the value function is sub‐modular and concave in seat availability in the two compartments. Thus the bid prices are non‐decreasing with respect to these state variables. We use this result to propose an exact algorithm to solve the DP. Our numerical investigation suggests that in contrast to standard backward induction, our method could be included in production revenue management systems. Further, when the economy compartment is capacity constrained, we observe a substantial monetary benefit from optimal dynamic upgrading compared to the static upgrading procedures currently used in practice.     

Revenue Management for Intermodal Transportation: The Role of Dynamic Forecasting

We study a joint capacity leasing and demand acceptance problem in intermodal transportation. The model features multiple sources of evolving supply and demand, and endogenizes the interplay of three levers—forecasting, leasing, and demand acceptance. We characterize the optimal policy, and show how dynamic forecasting coordinates leasing and acceptance. We find (i) the value of dynamic forecasting depends critically on scarcity, stochasticity, and volatility; (ii) traditional mean‐value equivalence approach performs poorly in volatile intermodal context; (iii) mean‐value‐based forecast may outperform stationary distribution‐based forecast. Our work enriches revenue management models and applications. It advances our understanding on when and how to use dynamic forecasting in intermodal revenue management.     

一种基于闭排队网络的集装箱码头设备配置优化模型

将集装箱码头龙门吊装卸工艺抽象为闭排队网络模型,采用估算均值法计算了顾客到达和服务时间分布为一般情况的闭排队网络系统性能指标.仿真结果表明,闭排队网络模型计算结果可以为集装箱码头设备配置提供一定的决策支持.     

Strategic Capacity Management When Customers Have Boundedly Rational Expectations

In retailing industries, such as apparel, sporting goods, customer electronics, and appliances, many firms deploy sophisticated modeling and optimization software to conduct dynamic pricing in response to uncertain and fluctuating market conditions. However, the possibility of markdown pricing creates an incentive for customers to strategize over the timing of their purchases. How should a retailing firm optimally account for customer behavior when making its pricing and stocking/capacity decisions? For example, is it optimal for a firm to create rationing risk by deliberately under stocking products? In this study, we develop a stylized modeling framework to answer these questions. In our model, customers strategize over the timing of their purchases. However, customers have boundedly rational expectations in the sense of anecdotal reasoning about the firm's fill rate, i.e., they have to rely on anecdotes, past experiences, or word‐of‐mouth to infer the firm's fill rate. In our modeling framework, we distinguish two settings: (i) capacity commitment, where the firm commits to its capacity level in the long run, or (ii) the firm dynamically changes it in each season. For both settings, within the simplest form of anecdotal reasoning, we prove that strategic capacity rationing is not optimal independent of customer risk preferences. Then, using a general form of anecdotal reasoning, we provide sufficient conditions for capacity rationing to be optimal for both settings, respectively. We show that the result of strategic capacity rationing being suboptimal is fairly robust to different valuation distributions and utility functions, heterogeneous sample size, and price optimization.     

On the Use of Buy Up as a Model of Customer Choice in Revenue Management

We consider settings in which a revenue manager controls bookings over a sequence of flights. The revenue manager uses a buy‐up model to select booking limits and updates estimates of the model parameters as data are accumulated. The buy‐up model we consider is based upon a simple model of customer choice, wherein each low‐fare customer who is not able to purchase a low‐fare ticket will, with a fixed probability, “buy up” to the high fare, independent of everything else. We analyze the evolution of the parameter estimates (e.g., the buy‐up probability) and chosen booking limits in situations where the buy‐up model is misspecified, that is, in situations where there is no setting of its parameters for which its objective function gives an accurate representation of expected revenue as a function of the booking limit. The analysis is motivated by the common situation in which a revenue manager does not know precisely how customers behave but nevertheless uses a parametric model to make decisions. Under some assumptions, we prove that the booking limits and parameter estimates converge and we compare the actual expected revenue at the limiting values with that associated with the booking limits that would be chosen if the revenue manager knew the actual behavior of customers. The analysis shows that the buy‐up model often works reasonably well even when it is misspecified, and also reveals the importance of understanding how parameter estimates of misspecified models vary as functions of decisions.