基于收益管理理论的门诊预约存量控制研究

门诊预约机制能够帮助医疗服务系统实现合理的资源组织与顺畅的服务流程,目前是医疗系统管理中的热点问题。论文分析了收益管理的适用性条件,提出其在医院门诊预约调度问题中应用的可行性,然后构建了门诊预约存量控制的Littlewood基础模型,并考虑患者爽约的情况,建立了超订策略下的门诊预约存量控制模型,最终用实例验证了所提出方法的实用性和有效性。     

单向可替代产品价格和库存的联合决策

考虑单周期问题中零售商同时销售两种可单向替代的产品,以期望利润为目标函数建立数学模型.将库存和替代价格共同作为零售商决策变量,证明其目标函数是凹函数,并得到求模型最优解的充要条件及解存在的范围.最后假设产品需求为正态分布,通过数值实验对模型的最优解进行分析,结果表明:实行最优替代价格策略可以有效提高零售商期望利润;允许替代销售不一定提高市场服务水平;被替代产品的销售价格和残值对零售商的最优替代价格决策没有显著影响.     

凸超曲面形变为预定调和平均曲率的曲面 *

使用热流方法将环形域中的凸超曲面形变为其调和平均曲率是一个预定函数的超曲面 .     

基于制造商资金有约束的替代产品的最优生产决策

考虑一个单周期的生产决策模型,在该模型中有一个制造商生产两种可替代的产品.面对随机的市场需求,制造商要在需求到来之前制定出两种产品的生产决策来最大化自己的期望利润.在制造商的资金有、无约束两种情形下,证明了制造商的收益函数的期望是关于两种产品生产数量的凹函数,探讨了资金的约束以及产品的替代给制造商的生产决策所带来的影响,给出了最优生产数量的若干性质.另外,针对需求分布为均匀分布的特殊情形给出了制造商最优生产决策的简单表达形式.     

Ricci张量对称函数的预定问题

本文考虑Ricci张量的对称函数σ2(Ricg)的预定问题.假设(M,g)是闭的Einstein流形,我们得到了只要流形(M,g)不具有σ2(Ric)奇性,则对于变号的函数f∈C∞(M),存在度量g*,使得σ2(Ricg*) = f.然后,作为推论,得到了具有负数量曲率的闭Einstein流形上的预定曲率的结果.     

单纯形上Meyer-Knig and Zeller算子的二阶矩量

该文讨论了单纯形上Meyer- Konig and Zeller算子的矩量问题.首先得到了二阶矩量在广义积分下的显式表示,并由此导出了二阶矩量的二元Appell超几何函数表示,超几何级数表示和完全渐近公式.     

替代产品的动态库存决策与控制

考虑了替代产品的动态库存决策与控制问题,建立了替代产品的多周期动态库存决策与控制模型.得到了目标函数的一些重要性质,给出了系统最优参数的求解算法,利用动态规划方法对系统的库存参数进行了优化求解.     

单纯形上Meyer Konig and Zeller算子的二阶矩量 [DM)]

该文讨论了单纯形上Meyer Konig and Zeller算子的矩量问题. 首先得到了二阶矩量在广义积分下的显式表示，并由此导出了二阶矩量的二元Appell超几何函数表示，超几何级数表示和完全渐近公式.      

含测量误差的部分线性模型的发散参数估计(英文)

本文考虑了部分线性模型中,线性部分协变量含有测量误差,并且线性部分的参数随着样本量的增大而发散的估计问题.我们考虑了用可观测的替代变量来替代不可观察到的真实变量,这种替代变量的期望与真实变量存在线性关系.我们提出了估计方法,并研究了估计量的相合性与渐进正态性.此外,我们研究了发散参数的发散速度.我们通过模拟来说明该估计的实际效果.     

基于收益管理的酒店客房超额预定决策模型

给出了考虑顾客取消和no-show的酒店客房超额预定随机优化决策模型,用半绝对偏差来度量收益风险,并将其转变为线性规划模型进行求解,以得出给定期限内的客房分配策略及可能的超售量.     

Analysis of seat allocation and overbooking decisions with hybrid information

We investigate a single-leg airline revenue management problem where an airline has limited demand information and uncensored no-show information. To use such hybrid information for simultaneous overbooking and booking control decisions, we combine expected overbooking cost with revenue. Then we take a robust optimization approach with a regret-based criterion. While the criterion is defined on a myriad of possible demand scenarios, we show that only a small number of them are necessary to compute the objective. We also prove that nested booking control policies are optimal among all deterministic ones. We further develop an effective computational method to find the optimal policy and compare our policy to others proposed in the literature.     

Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities

Clinical overbooking is intended to reduce the negative impact of patient no-shows on clinic operations and performance. In this paper, we study the clinical scheduling problem with overbooking for heterogeneous patients, i.e. patients who have different no-show probabilities. We consider the objective of maximizing expected profit, which includes revenue from patients and costs associated with patient waiting times and physician overtime. We show that the objective function with homogeneous patients, i.e. patients with the same no-show probability, is multimodular. We also show that this property does not hold when patients are heterogeneous. We identify properties of an optimal schedule with heterogeneous patients and propose a local search algorithm to find local optimal schedules. Then, we extend our results to sequential scheduling and propose two sequential scheduling procedures. Finally, we perform a set of numerical experiments and provide managerial insights for health care practitioners.     

“酒店+OTA”双渠道供应链超订策略研究

为解决市场需求不确定环境下,酒店和在线旅行网站(Online Travel Agency,OTA)合作时的能力超订量与在线房间预留量的决策问题,建立了基于佣金合作模式的数学模型,给出了实现酒店整体期望收益最大化的在线房间预留量与能力超订量。借助数值分析,进一步研究了佣金率与需求不确定性对最优决策的影响。结果表明,当佣金率与门店需求不确定性较小时,酒店采取双渠道策略并且实施超订;当佣金率与门店需求不确定性很大时,酒店采取门店单渠道策略但不实施超订。另外,在线房间预留量随着佣金率、门店需求不确定性的增大而减小。     

Maximum utility product pricing models and algorithms based on reservation price

We consider a revenue management model for pricing a product line with several customer segments under the assumption that customers’ product choices are determined entirely by their reservation prices. We highlight key mathematical properties of the maximum utility model and formulate it as a mixed-integer programming problem, design heuristics and valid cuts. We further present extensions of the models to deal with various practical issues arising in applications. Our computational experiments with real data from the tourism sector as well as with the randomly generated data show the effectiveness of our approach.     

A pricing model for clearing end-of-season retail inventory

The problem of setting prices for clearing retail inventories of fashion goods is a difficult task that is further exacerbated by the fact that markdowns enacted near the end of the selling season have a smaller impact on demand. In this article, we present discrete-time models for setting clearance prices in such an environment. When demand is deterministic, we compute optimal prices and show that decreasing reservation prices lead to declining optimal prices. When demand is stochastic and arbitrarily correlated across planning periods, we obtain bounds on the optimal expected revenue and on optimal prices. We also develop a heuristic procedure for finding near-optimal prices and test its accuracy through numerical experiments. These experiments reveal new insights for practitioners. For example, the penalty for choosing clearance price once and keeping it unchanged for the remainder of the selling season is found to be small when either the mean reservation prices do not change appreciably over time or when they drop sharply after the first period.     

Bundle pricing of inventories with stochastic demand

We consider a retailer selling a fixed inventory of two perishable products over a finite horizon. Assuming Poisson arrivals and a bivariate reservation price distribution, we determine the optimal product and bundle prices that maximize the expected revenue. Our results indicate that the performances of mixed bundling, pure bundling and unbundled sales strategies heavily depend on the parameters of the demand process and the initial inventory levels. Bundling appears to be most effective with negatively correlated reservation prices and high starting inventory levels. When the starting inventory levels are equal and in excess of average demand, most of the benefits of bundling can be achieved through pure bundling. However, the mixed bundling strategy dominates the other two when the starting inventory levels are not equal. We also observe that an incorrect modeling of the reservation prices may lead to significant losses. The model is extended to allow for price changes during the selling horizon. It is shown that offering price bundles mid-season may be more effective than changing individual product prices.     

A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound

This article proposes a continuous-time model to combine seat control and overbooking policies for single-leg flights. We assume that demand is time-and-fare dependent and follows a Poisson process. No-show passengers receive refunds which depend on their fares. Overbooking penalty is a uniformly convex function of oversale. To maximize the expected revenue, airline managers seek optimal seat allocation among competing passengers. In the meantime, they need to determine an optimal aggregate overbooking upper bound, which balances the no-show refunds and oversale penalties. Our basic model shows (i) although the nested-fare structure does not hold for the face value of fares, its essence is preserved in the sense of net fares; i.e., the face value less the no-show refund; (ii) the optimal control policy is based on a set of pre-calculated time thresholds, which is easy to implement; and (iii) there exists an optimal overbooking upper bound, below which the value function strictly increases in the upper bound, and above which the value function stays constant. We further extend the basic model to consider fare-dependent no-show rates. Numerical examples are presented.     

Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service

We consider a problem where different classes of customers can book different types of services in advance and the service company has to respond immediately to the booking request confirming or rejecting it. Due to the possibility of cancellations before the day of service, or no-shows at the day of service, overbooking the given capacity is a viable decision. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as the cost of overtime. For the calculation of the latter, information of the underlying appointment schedule is required. Throughout the paper we will relate the problem to capacity allocation in radiology services. Drawing upon ideas from revenue management, overbooking, and appointment scheduling we model the problem as a Markov decision process in discrete time which due to proper aggregation can be optimally solved with an iterative stochastic dynamic programming approach. In an experimental study we successfully apply the approach to a real world problem with data from the radiology department of a hospital. Furthermore, we compare the optimal policy to four heuristic policies, of whom one is currently in use. We can show that the optimal policy significantly improves the currently used policy and that a nested booking limit type policy closely approximates the optimal policy and is thus recommended for use in practice.     

搜寻成本下可替代产品的定价策略与库存决策

为探寻存在搜寻成本情况下消费者购买可替代产品时的定价与库存问题,从消费者效用出发,对厂商收益构建了基于马尔可夫决策过程的优化模型。在消费者方面,分析了其购买与继续搜寻的条件,并分别在搜寻成本不变和搜寻成本边际递减的情况下研究了消费者保留效用的变化情况以及购买相应产品的概率。此外,与很多相关文献不同的是,由于搜寻成本的存在,该情形下的消费者并不一定会在完成购买之前搜寻完所有的产品。在厂商方面,根据实际情况构建不同搜寻顺序下的收益模型并求解出最优定价策略与库存策略,并将定价模型与库存策略扩展到了动态的环境,为厂商制定价格及库存方案提供相应的决策支持。     

Online stochastic reservation systems

This paper considers online stochastic reservation problems, where requests come online and must be dynamically allocated to limited resources in order to maximize profit. Multi-knapsack problems with or without overbooking are examples of such online stochastic reservations. The paper studies how to adapt the online stochastic framework and the consensus and regret algorithms proposed earlier to online stochastic reservation systems. On the theoretical side, it presents a constant sub-optimality approximation of multi-knapsack problems, leading to a regret algorithm that evaluates each scenario with a single mathematical programming optimization followed by a small number of dynamic programs for one-dimensional knapsacks. It also proposes several integer programming models for handling cancellations and proves their equivalence. On the experimental side, the paper demonstrates the effectiveness of the regret algorithm on multi-knapsack problems (with and without overbooking) based on the benchmarks proposed earlier.