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.     

Effective cancellation policy to reduce the negative impact of patient no-show

The problem of patient no-show in outpatient delivery systems has been a long recognized issue. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Many clinics have cancellation policies of asking their patients to cancel 24 or 48?h in advance. However, there is no logical or mathematical basis for such a policy. The objective is to develop an effective cancellation policy that accounts for current no-show rates, the clinic's flow, and its fill rates to minimize the cost of patient wait time, physician idle time, and overtime. A simulation approach is presented to determine the hours required for patients to call in advance for cancelling appointments. The findings indicate that when fill rates are low and no-show probabilities are high, the time required for patients to cancel appointments needs to increase in order to achieve the goal of being cost-effective.     

A dual-response forwarding approach for containerizing air cargoes under uncertainty,based on stochastic mixed 0-1 programming

This paper proposes a dual-response forwarding approach for renting air containers and simultaneously determining how cargoes are distributed into the containers under uncertain information. Containers have to be booked in advance to obtain a discount rental rate from airlines, as urgent requirement or cancellation of containers on the day of shipping will incur a heavy penalty. We firstly formulate a mixed 0-1 integer model to determine the booking types and quantities of containers for the deterministic problem under accurate information. We then formulate a stochastic mixed 0-1 model to structure a dual-response forwarding system for the uncertain problem where accurate information is not available when booking. The first-stage response is to determine the booking types and quantities of containers. The second-stage response is to prepare for different scenarios that might occur on the day of shipping, including the types and quantities of containers required or returned for each scenario, and also the corresponding cargo loading plan. Computational results show that the stochastic model can provide a cost-efficient, flexible and responsive cargo forwarding system.     

A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling

This paper considers the outpatient no-show problem faced by a rural free clinic located in the south-eastern United States. Using data mining and simulation techniques, we develop sequencing schemes for patients, in order to optimize a combination of performance measures used at the clinic. We utilize association rule mining (ARM) to build a model for predicting patient no-shows; and then use a set covering optimization method to derive three manageable sets of rules for patient sequencing. Simulation is used to determine the optimal number of patients and to evaluate the models. The ARM technique presented here results in significant improvements over models that do not employ rules, supporting the conjecture that, when dealing with noisy data such as in an outpatient clinic, extracting partial patterns, as is done by ARM, can be of significant value for simulation modelling.     

上海未来短期客运量的预测分析——基于最小二乘法改进的GM(1,1)模型

客运量受诸多因素影响因此数据波动性大,并且具有小样本性及贫信息等特点.采用基于最小二乘法改进的GM(1,1)模型,对上海市的客运量进行短期预测.首先介绍了普通GM(1,1)模型的建立方法;然后通过最小二乘法的原理弱化波动较大的数据,减少随机性,加强规律性建立改进的新GM(1,1)模型;其次结合2005-201.4年数据建立预测模型;最后使用2014年数据对模型可靠性进行验证.结果表明该预测方法精度高误差小,改进的模型预测结果更加接近实际值.该模型为其他相关预测提供了理论依据,也便于上海市对未来交通运输的宏观调控.     

全渠道服务商在线上自助服务技术背景下的定价策略研究

在线上自助服务技术兴起的背景下,研究餐饮业服务商整合全渠道的定价策略。分析归纳了三种提供服务的渠道(线下取号排队、线上取号排队、线上预定);利用排队论以及依据消费者效用对服务系统进行理论建模,并结合数值分析,为服务商如何应对不同的消费者与市场环境指明了方向。研究发现:(1)给出了三种服务渠道的最优定价模型表示,并结合市场情况分析不同的定价策略,如在服务高峰期可以采取歧视定价策略;(2)指明了线上取号与线上预定两种渠道不宜同时提供,并给出了最优的线上预定的预留容量比例,对商家设计渠道具有指导意义;(3)发现了不管线下消费者比例如何,服务商营收曲线必定经过同一点,对服务商采取措施引流具有启发意义。本文对服务商全渠道理论建模具有启发意义。     

A Markov model for single-leg air cargo revenue management under a bid-price policy

In this paper, we consider the capacity allocation problem in single-leg air cargo revenue management. We assume that each cargo booking request is endowed with a random weight, volume and profit rate and propose a Markovian model for the booking request/acceptance/rejection process. The decision on whether to accept the booking request or to reserve the capacity for future bookings follows a bid-price control policy. In particular, a cargo will be accepted only when the revenue from accepting it exceeds the opportunity cost, which is calculated based on bid prices. Optimal solutions are derived by maximizing a reward function of a Markov chain. Numerical comparisons between the proposed approach and two existing static single-leg air cargo capacity allocation policies are presented.     

50 years of data mining and OR: upcoming trends and challenges

Data mining involves extracting interesting patterns from data and can be found at the heart of operational research (OR), as its aim is to create and enhance decision support systems. Even in the early days, some data mining approaches relied on traditional OR methods such as linear programming and forecasting, and modern data mining methods are based on a wide variety of OR methods including linear and quadratic optimization, genetic algorithms and concepts based on artificial ant colonies. The use of data mining has rapidly become widespread, with applications in domains ranging from credit risk, marketing, and fraud detection to counter-terrorism. In all of these, data mining is increasingly playing a key role in decision making. Nonetheless, many challenges still need to be tackled, ranging from data quality issues to the problem of how to include domain experts' knowledge, or how to monitor model performance. In this paper, we outline a series of upcoming trends and challenges for data mining and its role within OR.     

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.     

Time series forecasting with neural network ensembles: an application for exchange rate prediction

This paper investigates the use of neural network combining methods to improve time series forecasting performance of the traditional single keep-the-best (KTB) model. The ensemble methods are applied to the difficult problem of exchange rate forecasting. Two general approaches to combining neural networks are proposed and examined in predicting the exchange rate between the British pound and US dollar. Specifically, we propose to use systematic and serial partitioning methods to build neural network ensembles for time series forecasting. It is found that the basic ensemble approach created with non-varying network architectures trained using different initial random weights is not effective in improving the accuracy of prediction while ensemble models consisting of different neural network structures can consistently outperform predictions of the single ‘best’ network. Results also show that neural ensembles based on different partitions of the data are more effective than those developed with the full training data in out-of-sample forecasting. Moreover, reducing correlation among forecasts made by the ensemble members by utilizing data partitioning techniques is the key to success for the neural ensemble models. Although our ensemble methods show considerable advantages over the traditional KTB approach, they do not have significant improvement compared to the widely used random walk model in exchange rate forecasting.     

大数据背景下的商品需求预测与分仓规划

商品需求预测对于电商企业意义重大,对阿里电商平台的交易数据进行挖掘以获取有效特征,利用特征建立模型对未来两周这些商品的需求进行动态预测,并基于预测结果和成本最小的原则提出分仓规划建议.预测模型选择随机森林做回归,然后在残差分析的基础上建立报童模型求解分仓的库存规划.对特征数量众多的电商交易数据挖掘所建立的模型有助于电商企业进行有效的商品需求预测并据此制定成本更低的分仓规划.     

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。     

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.     

A new wavelet-based denoising algorithm for high-frequency financial data mining

Denoising analysis imposes new challenge for mining high-frequency financial data due to its irregularities and roughness. Inefficient decomposition of the systematic pattern (the trend) and noises of high-frequency data will lead to erroneous conclusion as the irregularities and roughness of the data make the application of traditional methods difficult. In this paper, we propose the local linear scaling approximation (in short, LLSA) algorithm, a new nonlinear filtering algorithm based on the linear maximal overlap discrete wavelet transform (MODWT) to decompose the systematic pattern and noises. We show several unique properties of this brand-new algorithm, that are, the local linearity, computational complexity, and consistency. We conduct a simulation study to confirm these properties we have analytically shown and compare the performance of LLSA with MODWT. We then apply our new algorithm with the real high-frequency data from German equity market to investigate its implementation in forecasting. We show the superior performance of LLSA and conclude that it can be applied with flexible settings and suitable for high-frequency data mining.     

A cost-effective urgent care policy to improve patient access in a dynamic scheduled clinic setting

Patient no-show in outpatient clinics has been a long recognized issue, which negatively impacts clinic operational efficiency in terms of costs and patient access to care. One way to reduce these negative impacts is to allow urgent walk-ins during a clinic day. Some clinics allow random walk-ins and some purposely leave open time slots to accommodate them. The objective of this paper is to develop a cost-effective urgent care policy that is added on top of a full schedule and takes into account scheduled patients’ no-show rates to improve patient access to care in a dynamic clinic environment. The findings indicate that the proposed approach outperforms the current random and urgent slot approaches. This paper demonstrates a dynamic approach for accommodating urgent patients into a patient scheduling system, based on the prediction of an individual patient's no-show probability and the maximum number of urgent patients allowed.     

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.     

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.     

Outpatient appointment scheduling given individual day-dependent no-show predictions

This paper examines the combined use of predictive analytics, optimization, and overbooking to schedule outpatient appointments in the presence of no-shows. We tackle the problem of optimally overbooking appointments given no-show predictions that depend on the individual appointment characteristics and on the appointment day. The goal is maximizing the number of patients seen while minimizing waiting time and overtime. Our analysis leads to the definition of a near-optimal and simple heuristic which consists of giving same-day appointments to likely shows and future-day appointments to likely no-shows. We validate our findings by performing extensive simulation tests based on an empirical data set of nearly fifty thousand appointments from a real outpatient clinic. The results suggest that our heuristic can lead to a substantial increase in performance and that it should be preferred to open access under most parameter configurations. Our paper will be of great interest to practitioners who want to improve their clinic performance by using individual no-show predictions to guide appointment scheduling.     

Hierarchical forecasting based on AR-GARCH model in a coherent structure

This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent relationships between individual components. In this experiment, we simulated 30 coherent components based on AR(2)-GARCH(1, 1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method.     

可加号的门诊预约能力分配问题

预约服务可以有效优化医院门诊就诊流程,针对我国患者预约意识不强和预约患者爽约率高的特点,本文研究患者需求量较高时可以增加号源的条件下,考虑加号和拒绝患者成本,以门诊收益期望最大为目标,匹配预约患者和现场挂号患者需求量的能力分配问题。证明了门诊收益期望函数的单峰性,给出了最优解满足的条件。通过大量数值实验分析不同参数对门诊能力分配方案的影响,结果表明两类患者需求量对能力分配方案有较大影响,可加号情况下能力分配方案对患者爽约更敏感。