基于马尔可夫决策过程的医疗检查预约优化模型

医技部门的医疗检查如电子计算机断层扫描（CT）、核磁共振成像设备（MRI）、X射线（X－rays）常常有如下三种病人类型：门诊病人、住院病人和急诊病人。针对不同病人类型的需求特点，运用马尔可夫决策过程原理和动态规划方法，建立了医疗检查设备的预约优化模型，并证明了模型的最优性质，得出了不同病人类型的最优预约策略。数值算例的结果表明：本文的预约策略不仅易于实施，而且，通过该模型获得的最大收益比按传统先来先预约的模式所获得的收益要大。     

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。     

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.     

基于满意度的预约门诊排队策略研究

为了推进预约挂号服务在医院有效的应用,本文结合实际情形,提出了病人满意度度量的新指标——加权病人等待时间,建立了以最大化病人满意度为目标的排队模型,并分析了医院目前常用的两种预约排队策略:不同优先级预约排队策略与时间段优先型预约排队策略。通过两种预约策略的比较,得到后者优于前者;通过预约与非预约策略的比较,得到预约策略优于非预约策略。在此基础上,对两种预约策略进行优化分析,求解出两种预约策略分别对应的最佳预约与非预约病人比例。最后,通过数值分析说明了应用预约策略对改善病人等待满意度的合理性及有效性,并对应用预约策略达到更好的满意度提出了可行建议。     

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。     

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.     

基于仿真优化算法的医院关键资源能力分派研究

图像检查设备是医院的瓶颈资源。医院管理者通常追求瓶颈资源的高利用率,导致患者需长时间等待才能进行检查。长时间的等待加剧了患者的焦虑情绪,也会给患者造成病情加重等隐患。考虑患者具有不同的目标等待时间,本文针对医疗关键资源能力分派问题,提出了双层嵌套的阈值策略。非紧急患者预约时,双层嵌套阈值考虑为将来到达的紧急患者预留一定量的能力。如果患者在目标等待时间内预约不成功,则离开医院,并对医院造成患者流失惩罚。目标函数是使患者总流失惩罚最小。本文用目标函数对嵌套阈值的偏导数作为最速梯度法下降方向,基于样本路径对该偏导数进行估计,并通过不断迭代得到最优阈值。数值实验中,与医院应用的传统阈值策略比较,结果显示,本文所提嵌套阈值策略能够有效降低因超过目标等待时间而流失的患者给医院带来的损失。     

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.     

网络环境下的医院门诊挂号预约系统改善研究

本文对改善挂号预约系统的模型进行了分析探索。根据病人的自报病情信息及医疗经验信息,通过量化的方法,对挂号预约阶段病人对疾病分类和症状程度划分进行了研究。研究结果有助于改善预约挂号系统。新的方法有助于解决挂号难的问题、有助于医疗服务质量的提高、并且有助于医疗资源的优化利用。     

基于LSTM-GA混合模型的患者预约排队策略优化

在医疗运作管理领域,合理的资源分配能够帮助更多的患者尽早就医,降低患者病情恶化和死亡的风险。本文设计了预约排队策略对患者占有资源的顺序进行分配,建立了基于长短时记忆(Long Short Term-Memory, LSTM)神经网络和遗传算法(Genetic Algorithm, GA)的混合模型以优化排队策略。首先利用大数据和深度学习分析患者到达和医院服务情况,建立LSTM神经网络学习数据特征并预测未来数据,相比于排队论常用的随机分布方法取得了更好的效果.其次设计了基于排队系统仿真的排队策略优化算法,利用改进GA得到最优排队策略。实证研究表明,文本的方法可以明显降低患者的等待时间,最高可达59%。最后对排队策略进行敏感性分析,结果表明排队策略有效作用于仿真的各个时段。     

Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system

Military medical planners must develop dispatching policies that dictate how aerial medical evacuation (MEDEVAC) units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on a request’s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic policies. Two computational experiments are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system performance measure. Several excursions are examined to identify how the 9-line MEDEVAC request arrival rate and the MEDEVAC flight speeds impact the optimal dispatching policy. Results indicate that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the linear programming algorithms executed by CPLEX 12.6 with regard to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems.     

Admission policies for the customized stochastic lot scheduling problem with strict due-dates

This papers considers admission control and scheduling of customer orders in a production system that produces different items on a single machine. Customer orders drive the production and belong to product families, and have family dependent due-date, size, and reward. When production changes from one family to another a setup time is incurred. Moreover, if an order cannot be accepted, it is considered lost upon arrival. The problem is to find a policy that accepts/rejects and schedules orders such that long run profit is maximized. This problem finds its motivation in batch industries in which suppliers have to realize high machine utilization while delivery times should be short and reliable and the production environment is subject to long setup times.We model the joint admission control/scheduling problem as a Markov decision process (MDP) to gain insight into the optimal control of the production system and use the MDP to benchmark the performance of a simple heuristic acceptance/scheduling policy. Numerical results show that the heuristic performs very well compared with the optimal policy for a wide range of parameter settings, including product family asymmetries in arrival rate, order size, and order reward.     

SPAR: stochastic programming with adversarial recourse

We consider a general adversarial stochastic optimization model. Our model involves the design of a system that an adversary may subsequently attempt to destroy or degrade. We introduce SPAR, which utilizes mixed-integer programming for the design decision and a Markov decision process (MDP) for the modeling of our adversarial phase.     

Improved utilization for joint HCCA–EDCA access in IEEE 802.11e WLANs

This paper presents a model for improving utilization in IEEE 802.11e wireless LAN via a Markov decision process (MDP) approach. A Markov chain tracking the utilized transmission window for two separate access mechanisms is devised. Subsequently, the action space and the rewards of the MDP are judiciously selected with the aim of improving overall utilization without explicit blocking. The proposed MDP model for 802.11e reveals that proportional allocation of access opportunities improve overall utilization compared to completely randomized access. Simulation results go on to show that a policy that limits HCCA access as a function of channel load improves utilization by an average 8 %. The optimization framework proposed in this paper is promising as a practical decision support tool for resource planning in 802.11e.     

Optimal control of polling models for transportation applications

We formulate and analyze a dynamic scheduling problem for a class of transportation systems in a Markov Decision Process (MDP) framework. A transportation system is represented by a polling model consisting of a number of stations and a server with switch-over costs and constraints on its movement (the model we have analyzed is intended to emulate key features of an elevator system). Customers request service in order to be transported by the server from various arrival stations to a common destination station. The objective is to minimize a cost criterion that incorporates waiting costs at the arrival stations. Two versions of the basic problem are considered and structural properties of the optimal policy in each case are derived. It is shown that optimal scheduling policies are characterized by switching functions dependent on state information consisting of queue lengths formed at the arrival stations.     

基于运行时间预测的枢纽内多线路协调调度研究

针对传统调度模型预见性不强的弱点,提出一个基于支持向量机(SVM)的公交车辆到达枢纽时间的预测模型,基于该模型构建以所有乘客节约时间最大为目标的调度模型,动态协调公交车辆从枢纽的发车时间,并基于遗传算法对该模型进行求解。最后,我们以大连市沙河口火车站枢纽为实例,对该模型和算法的可行性进行了检验,结果显示,本文提出的调度方法优于传统调度策略。     

A decomposition algorithm for limiting average Markov decision problems

We consider a Markov decision process (MDP) under average reward criterion. We investigate the decomposition of such MDP into smaller MDPs by using the strongly connected classes in the associated graph. Then, by introducing the associated levels, we construct an aggregation-disaggregation algorithm for the computation of an optimal strategy for the original MDP.     

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.     

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。     

An interactive,computer-aided ship scheduling system

This paper is concerned with a fleet scheduling and inventory resupply problem faced by an international chemical operation. The firm uses a fleet of small ocean-going tankers to deliver bulk fluid to warehouses all over the world. The scheduling problem centers around decisions on routes, arrival/departure times, and inventory replenishment quantities. An interactive computer system was developed and implemented at the firm, and was successfully used to address daily scheduling issues as well as longer range planning problems. The purpose of this paper is to first present how the underlying decision problem was analyzed using both a network flow model and a mixed integer programming model, and then to describe the components of the decision support system developed to generate schedules. The use of the system in various decision making applications is also described.