基于马尔可夫过程的门诊预约策略研究

在现实的门诊预约决策问题中,已经预约的患者在接受医疗服务之前,有可能取消先前的预约,也可能在就诊当天爽约,这些均会对医院的收益造成负面影响,如何在考虑患者存在取消预约和爽约的情形下,设计合理有效的门诊预约策略来保证医院的收益,是一个值得研究的问题.文章针对具有两类预约患者(提前预约患者和当天预约患者)的门诊预约决策问题,在考虑提前预约患者可能存在取消预约和爽约行为的情形下,提出了一种基于马尔可夫过程的门诊预约策略.文中首先以医院的期望收益最大作为决策目标,建立对提前预约患者进行超额预订的马尔可夫过程模型;基于所建立模型的特征,证明了对于任意的提前预约时段,存在不同爽约概率下提前预约患者的最佳预约数量,且此最佳预约数量随着爽约概率的增加而增加;进一步地,给出了该门诊预约问题的最优预约策略以及确定该策略的精确算法;最后通过数值实验说明了文章所提出的预约策略的适用性和有效性.     

考虑存在取消预约情形的门诊预约能力分配策略

在现实的门诊预约问题中,已经预约的患者在接收医疗服务之前,有可能取消先前的预约,这会对医院的收益造成负面影响,如何在考虑患者存在取消预约的情形下,设计合理有效的能力分配策略来保证医院的收益,是一个值得研究的问题.本文针对具有提前预约和当天预约的门诊预约能力分配问题,在考虑提前预约患者可能存在取消预约行为的情形下,提出了一种提前预约患者和当天预约患者的最优能力分配策略。文中首先以医院的期望收益最大作为决策目标,建立了存在取消预约患者的医疗预约问题的马尔科夫过程模型,并给出了该模型的相关性质;基于所建立模型的特征,证明了对于任意的提前预约时段,存在提前预约患者的最佳数量,进而给出了提前预约患者和当天预约患者的最优能力分配策略以及确定该策略的精确算法;最后,通过数值试验说明了本文所提出的能力分配策略的适用性和有效性。     

基于收益管理的医疗门诊服务优化方法研究

将航空业的收益管理思想用于医院门诊预约过程.在充分考虑了患者对门诊诊疗能力未来实际需求的不确定性情况下,针对患者爽约(no-show)的情况,采用可调整的鲁棒优化模型,克服了传统优化方法对假设和估计严格性的依赖.所利用的鲁棒优化模型在一定程度上弥补了医院门诊预约所采用传统的随机优化和需求分布估计方法的不足,为医院门诊预约预售策略的制定提供了可以借鉴的思路.同时利用收益管理帮助医疗机构更好满足来自不同患者的诊疗需求,优化医疗门诊服务的质量,提高医院的实际收益.     

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。     

直营连锁企业生产分配供应单周期模型研究

直营连锁企业一直面临着一个重要的决策问题是生产多少产品按什么分配方案供应给它的连锁店进行销售,使得连锁企业的损失最小和利润最大.文章首先建立了连锁企业在给定总生产量和基于期望损失下的单周期最优分配模型,导出了生产分配供应的最优策略公式,提出了求解最优生产总量和最优分配供应策略的近似计算方法,以及在销售周期内打折定价策略.最后通过对某食品直营公司的销售数据进行计算,所提近似算法可以获得连锁公司对应的最优分配供应策略,数值结果表明采用最优分配供应策略可以减少直营连锁企业的期望损失风险,增加期望收益.文章给出了连锁企业最优生产供应策略的模型和近似求解算法,对于连锁企业的生产和分配供应具有重要的理论指导意义.     

随机服务时间下异质患者门诊预约调度优化

考虑随机服务时间与行为特征互不相同的异质患者,建立随机混合整数规划模型对门诊预约调度问题展开研究。首先在给定服务顺序的假设下求解了两个患者的预约调度问题;在此基础上,设计启发式算法对多个患者预约方案和服务顺序同时进行优化。数值结果表明:当患者服务时间为独立同分布的随机变量时,患者预约时间间隔呈现先增加后减少的圆顶形状;当患者服务时间服从不同分布时,通过与样本平均近似方法对比,验证了启发式算法的计算效率和有效性。     

基于分类服务机制的可预约排队系统设计和服务定价研究

采取合理有效的服务机制和服务定价是确保服务型企业持续有效运行的重要方式,服务提供商设计合理的服务策略可以使得服务收益达到最优。本文以服务可预约的排队系统为研究背景,对服务提供商分类服务机制选择和服务定价策略进行研究。通过考虑由两种服务策略下顾客之间的平均等待时间的对比和顾客等待厌恶心理参数形成的全新顾客服务效用模型,给出服务提供商在不同服务机制下的最优服务策略以及顾客市场规模对其服务策略选择的影响。研究表明,当预约顾客的市场规模相对较小时,服务提供商可以采取分类服务机制,即同时服务预约顾客和未预约顾客以增加服务收益,反之亦然。此外,预约顾客等待厌恶程度能够显著增大服务提供商的服务定价。     

准备多少货源平均收益最大

设某商品每年的需求量ξ(吨)是随机变量,其概率分布为已知.假设每销售一吨可获利润s元,但若年内销售不出,那么每吨将耗费保管费t元.对商店而言,若进货较多,那么若全卖出的话将获利较多,但卖不完的话将白白损失保管费,但若进货较少,保管费的损失则较小,但很可能白白丢失许多获利的机会,因此究竟备多少货最为合适呢?这就是本文所要讨论的问题.因为年需求量ξ是随机变量,从而年收益也是随机变量,为比较方案的好坏,我们考虑平均收益,即考虑年收益的数学期望.本文所讨论的问题是应组织多少货源才能使平均收益(收益的数学期望)为最大.本文分别就年…     

基于全支付拍卖模型的众包激励策略优化分析

考虑协作式众包与众包竞赛在发包方期望收益上存在差异,本文对两种众包模式下的激励策略优化问题进行了研究。基于全支付拍卖理论建立了协作式众包模式中全部接包方贡献总收益和众包竞赛模式中接包方贡献最高质量收益的数学模型。接下来,针对两种模式下接包方收益的区别,分析了各自的最优激励策略。最后,采用比较静态均衡法分析了接包方数量和能力在两种众包模式中对激励策略的不同影响,并采用了数值算例验证了相关结论。结果显示:在不设置奖励限制时,协作式众包模式中存在一个最优的奖励分配个数使得接包方贡献总收益取得最大值;而众包竞赛模式中存在一个最优的奖励分配个数使得接包方贡献最高质量收益取得最大值。因此,若接包方参与数量及能力水平提高时,发包方应增加奖励分配个数,以促进接包方贡献总收益与接包方贡献最高质量收益的最大化。     

考虑急诊随机需求的CT室预先排程研究

在考虑急诊随机插队情形下,以服务系统期望总收益最大化为目标建立马尔科夫决策模型描述CT室预先排程问题.精细划分预约病人的费用信息,精确刻画预约病人的直接等待时间.运用收益管理理论,通过边际分析技巧获得各等级病人的预约限制数量,保证接受预约请求的增量效益大于0.数值算例结果显示,得到的预约策略在医院服务病人的收入、病人的直接等待成本、预约请求被推迟的惩罚成本、医疗资源加班成本和空闲成本之间达到较好的平衡.由模型的结构性质知,得到的策略在实践中容易操作.     

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.     

Matching daily healthcare provider capacity to demand in advanced access scheduling systems

Advanced access scheduling, introduced in the early 1990s, is reported to significantly improve the performance of outpatient clinics. The successful implementation of advanced access scheduling requires the match of daily healthcare provider capacity with patient demand. In this paper, for the first time a closed-form approach is presented to determine the optimal percentage of open-access appointments to match daily provider capacity to demand. This paper introduces the conditions for the optimal percentage of open-access appointments and the procedure to find the optimal percentage. Furthermore, the sensitivity of the optimal percentage of open-access appointments to provider capacity, no-show rates, and demand distribution is investigated. Our results demonstrate that the optimal percentage of open-access appointments mainly depends on the ratio of the average demand for open-access appointments to provider capacity and the ratio of the show-up rates for prescheduled and open-access appointments.     

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.     

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.     

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 stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic

This research focuses on the stochastic assignment system motivated by outpatient clinics, especially the physical therapy in rehabilitation service. The aim of this research is to develop a stochastic overbooking model to enhance the service quality as well as to increase the utilization of multiple resources, like therapy equipment in a physical therapy room, with the consideration of patients’ call-in sequence. The schedule for a single-service period includes a fixed number of blocks of equal length. When patients call, they are assigned to an appointment time for that block, and an existing appointment is not allowed to be changed. In each visit, a patient might require more than one resource and a probability of no-show. Two estimation methods were proposed for the expected waiting and overtime cost with multiple resources: Convolution Estimation Method and Joint Cumulative Estimation Method for the upper and lower bound value; respectively. A numerical example based on a physical therapy room was used to show that this stochastic model was able to schedule patients for better profitability compared with traditional appointment systems based on four prioritization rules. The workload in each appointment slot was more balanced albeit more patients were assigned to the first slot to fill up the empty room.     

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.     

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.