患者延时敏感下按项目与按病种支付的比较

针对我国公立医疗服务系统当前正从按项目支付模式向按病种支付模式转变但缺乏科学依据和运营经验的情况，研究了按项目支付和按病种支付两种模式下公立医院的最优运营决策以及两种支付模式的社会福利比较问题.通过应用三阶段Stackelberg模型，在考虑患者对等待时间和服务质量敏感的情况下，分析了患者、公立医院以及政策制定者三方的均衡性质和系统性能.研究结果发现，当患者延时敏感度、服务质量敏感度和自付比例(或单位服务质量成本)较高(较低)时，政策制定者选择按病种支付模式可以获得更高的社会福利；否则，反之.当患者延时敏感度中等大小时公立医院的服务能力是最大的.此外，研究结果还表明，在按病种支付模式下政策制定者选择适中的医疗服务价格和患者个人自付比例可以使得社会福利最大化.     

信息缺失下的应急方案选择模型及算法研究

信息缺失是应急事件处置中的一个重要特征.对于考虑信息缺失的两阶段应急处置问题,本文提出了应急方案效度的概念,构造了考虑资源约束条件下的应急方案选择模型,并给出了求解算法.数值结果表明模型及算法的有效性.     

DRGs与DIP的对比研究：基于医院运营的视角

打包预付方式将控制医疗费用的责任转移至医方，能激励医院做好成本管控，会对医院的日常运营产生重大影响.本文主要研究按疾病相关组付费(DRGs)和按病种分值付费(DIP)两种支付方式，通过构建排队和竞争博弈模型探讨两种支付方式下的医院间服务速率决策问题以及相同医疗预算下的医疗服务质量、医院利润和社会福利的对比研究，并通过数值方式探讨了医院数量和服务成本系数对系统性能的影响.研究发现：1)DIP与DRGs相比，医院的服务速率相对降低，服务的病人数量较少，但医院利润更大.2)从社会福利来看，患者损失的成本较高时，DRGs较优；反之，DIP较优，且此时DIP是帕累托改进策略.3)只有当医院数量适中、服务成本系数较大时，DIP的优势才更明显.     

基于最小最大后悔值的应急救灾网络构建鲁棒优化模型与算法

应急救灾过程分为两个阶段：第一阶段启动应急救灾网络构建,在灾区附近设立临时应急配送中心,并由应急资源供应方向其紧急调配应急资源;第二阶段将应急资源从临时应急配送中心向灾区受灾点进行调度,以保证救灾过程顺利进行。本文研究第一阶段应急救灾网络的构建问题,考虑到突发灾害初期灾情相关参数概率分布情况难以获取,建立了基于情景的最小最大后悔值准则的应急救灾网络构建鲁棒优化模型。求解模型时,利用有限情景集表示第二阶段的不确定性数据,并将模型化为与其等价的混合整数规划模型,利用情景松弛的迭代算法进行求解。数值试验中给出相应的绝对鲁棒模型与本文偏差鲁棒模型作了比较,结果表明基于最小最大后悔值准则的应急救灾网络优化模型具有良好的鲁棒性,而且算法也是有效的。     

灾后不确定需求下应急医疗移动医院鲁棒选址问题研究

我国灾害医学救援主要采用"现场救治"模式，应急医疗移动医院的选址是否合理直接影响救援效率，但各受灾点伤员数量的不确定性增加了决策的困难。本文引入多面体不确定集合刻画伤员数量的不确定性，同时考虑伤员分类及移动医院分型，构建一个以伤员总生存概率最大化为目标的鲁棒选址模型。利用鲁棒优化理论，将模型转化为等价的混合整数规划问题，通过GAMS软件编程并调用CPLEX求解器求解。最后，以四川芦山地震应急医疗救援为例，验证模型和求解方法的可行性和鲁棒性。结果表明，扰动比例和不确定水平对移动医院的选址和伤员的分配方案有显著影响，决策者可根据自己对不确定性风险的偏好程度选择最佳的扰动比例和不确定水平组合，以获得最优的选址分配方案。     

超视距空战中的多无人机武器目标分配方法

武器目标分配问题是多无人机超视距空战中的关键决策问题之一。本文考虑超视距空战的强对抗、不确定等特点给武器目标分配问题带来的新挑战，基于纳什均衡博弈思想，将超视距空战中的多无人机武器目标分配问题建模为双矩阵博弈模型，其中，基于证据理论设计多级信息融合方法计算多个无人机对抗多个目标时的总攻击有效性。在此基础上，基于纳什均衡中策略被选择的概率与遗憾值之间的关系以及超视距空战需求，将双矩阵博弈模型转换为混合整数规划模型进行求解，并通过典型案例、数值实验、仿真实验对本文方法的计算过程和有效性进行分析。结果表明，本文方法能够有效给出超视距空战中的多无人机武器目标分配方案。     

考虑灾民痛苦感知的应急避难所选址与物资分配优化

大规模灾害发生初期，应急物资往往不能及时供应，受灾群众因缺乏物资和服务产生痛苦感知。针对该问题，设计数值评定量表（NRS）构建痛苦函数，刻画灾民痛苦感知成本，将灾民感知因素纳入应急总成本的决策考量。提出以总社会成本（物流成本和痛苦心理成本之和）最小为应急救援目标，构建了一个考虑灾民疏散与重新安置、临时避难所选址和物资分配的集成优化模型。设计经典混合整数规划方法和改进的遗传算法对所构模型进行求解，并应用于海南威马逊台风案例。案例分析表明，模型和算法能有效解决考虑灾民痛苦的避难所选址与物资分配问题，揭示了随着受灾规模的增加和疏散时间的减少，遗传算法的求解结果优于经典求解方法。     

供需不确定条件下基于CVaR的零售商库存鲁棒优化模型

在经典报童模型下考虑供应和需求不确定性,研究了具有风险厌恶的零售商库存优化问题。采用条件风险值（CVaR）对库存绩效进行度量,构建了基于CVaR的零售商库存运作模型;在此基础上,考虑上游供应商供货能力和下游市场需求不确定性,并采用一系列未知概率的离散情景进行描述,给出了供需不确定条件下基于CVaR的零售商库存鲁棒优化模型。进一步,采用区间不确定集对未知情景概率进行建模,给出了基于最大最小准则的鲁棒对应模型。针对同时考虑供需不确定性导致的模型非凸性,采用标准对偶理论将其转化为易于求解的数学规划问题。最后,通过数值计算分析了不同风险厌恶程度和不确定性程度对零售商库存决策以及库存绩效的影响。结果表明,供需不确定性的存在虽然会导致零售商库存绩效损失,但损失值较小。特别地,依据文中模型得到的鲁棒库存策略在多数情况下能够保证零售商获得更优的库存绩效。此外,不确定性和风险厌恶程度的增加虽然会影响零售商库存决策和运作绩效,但在同等风险厌恶态度下,随着不确定性程度的增加,基于文中方法得到的鲁棒库存策略仍能确保零售商获得理想的库存绩效,表明文中所建模型在应对供需不确定性方面具有良好的鲁棒性。     

基于两阶段资源分配协调机制的分布式多项目随机调度

企业趋向于多项目共享全局资源的分布式协同管理。但在多项目实际执行时，全局资源可用量往往由于外部环境的动态变化而存在不确定性，活动中断、资源浪费等现象频发，项目管理变得愈加复杂。本文将不确定的全局资源可用量建模为随机变量，设计两阶段资源分配协调机制，在预分配阶段，考虑项目允许的最大活动中断次数约束，建立各项目调度的马尔可夫动态决策过程模型；预分配结束后，基于活动重要度依次对剩余全局资源进行协调再分配，以提高资源利用率并减少平均项目延期。设计基于全局资源协调分配的Rollout近似动态规划算法进行求解。开展问题库算例实验研究与案例分析，验证协调机制与求解算法的性能；同时，探讨并分析不确定参数对目标结果的影响。     

基于韧性城市视角的突发事件下分布式应急手术调度研究

针对城市突发事件下的应急手术问题,进行了基于韧性城市视角的分布式应急手术调度研究。首先,提出了韧性城市视角下同时考虑救援时间和患者恶化成本的调度目标;其次,结合应急手术中的疲劳阈值效应,截断学习效应和患者恶化成本等典型特点,以及救援医院的可重入层流手术特点,构建了分布式应急手术调度模型;然后,设计两阶段算法求解患者在医院间的分配,以及在医院内的手术排序与资源安排;最后,通过数值实验测试了四种启发式算法下带深度搜索策略的混合教与学优化算法（HTLBO-LS）的寻优性能,并通过仿真案例进一步探讨了不同算法的应用效果,以及在城市韧性视角下的医疗资源配置方案。研究结果为城市突发事件下分布式应急手术调度提供方法借鉴和决策参考。     

A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design

Various types of healthcare waste (or medical waste) generated by urban healthcare activities have increased due to the expansion of urban population and medical needs. As healthcare wastes are harmful to both the environment and human health, managing medical waste is becoming progressively more important. Constructing an optimized medical waste recycling network is one of the key problems in the management of urban healthcare waste. This paper conducts a two-stage reverse logistics network design for urban healthcare waste. The first stage involves the prediction of the amount of medical waste. Based on the Grey GM(1,1) prediction model, the amount of medical waste in multi-period of the target hospitals is predicted. In the second stage, a multi-objective model aimed at minimizing operating costs and minimizing environmental impact is developed for facilities allocation decisions, which include the configuration of key facilities such as hospitals, collection centers, transshipment centers, processing centers, and disposal sites, as well as medical waste flow control among facilities. A dynamic approach for the healthcare waste reverse logistics network is constructed by combining the Grey GM(1,1) prediction method with multi-objective optimization model. Sensitivity analysis of key parameters has been performed to analyze their impact on network performance. Some insightful management practices have been revealed.

     

全球视角下跨境医疗服务研究述评与展望

本文从跨境医疗研究现状出发,对跨境医疗新趋势进行分析。研究发现关于跨境医疗的研究成果较多基于传统的医疗管理和公共卫生理论与模式。互联网时代跨境医疗的特征和医疗服务模式发生了变革,在这一背景下的相关研究较少。本文系统分析了跨境医疗整体概况及测度研究、驱动机制与服务模式研究成果,有针对性地提炼跨境医疗研究的理论框架,探讨跨境医疗带来的全球医疗资源均等化配置问题,提出新趋势下跨境医疗研究的关键科学问题与相关研究建议。研究成果对于推动国内外学者开展跨境医疗前沿领域研究,贡献全球健康事业有着重要的理论价值和实践意义。     

Ambulance allocation for maximal survival with heterogeneous outcome measures

This paper proposes new models for locating emergency medical services (EMS) by incorporating survival functions for capturing multiple-classes of heterogeneous patients. The Maximal Expected Survival Location Model for Heterogeneous Patients (MESLMHP) aims to maximize the overall expected survival probability of multiple-classes of patients, whereby different classes could be defined according to agreed patient categories based on response time targets, or by capturing differing medical conditions each with a corresponding survival function. Furthermore, we propose and demonstrate an approximation approach to solving the extended stochastic version of MESLMHP, which utilizes queuing theory to permit the modeling of congestion and utilization at each ambulance station, and does not require assumptions to be made on the utilization of ambulances. Both models are demonstrated using data from the ambulance service in Wales. We show that our multiple outcome measures and survival-maximizing approach, rather than one based on average response time targets alone or a single patient class provides more effective EMS ambulance allocations.     

基于系统柔性的MTS-MTO混合生产决策

研究生产商采用MTS、MTO混合作业的方式为不同客户提供产品和服务的策略。计划利用一组可灵活控制的动态设备处理那些不同需求的MTS和MTO生产业务，为此，我们开发了一个多服务台的排队模型，利用拟生灭过程和相位型分布得到了MTS、MTO排队系统平衡条件和稳态概率矩阵几何解。通过求解分块矩阵方程组，给出了系统队列长度、平均等待队长、顾客服务水平等绩效测度指标。建立了系统运作成本最优化的数学模型，采用搜索算法，确定了关键参数的边界值，找到了混合系统运作的最优策略。数值模拟和系统绩效比较分析结果显示：（1）动态切换策略能够更快速的帮助MTS恢复目标库存量，控制系统缺货风险，降低库存持有成本；（2）找到了满足顾客服务水平的最少的设备配置数量和库存成本最低的生产切换时间，且动态系统的平均队列长度低于静态系统；（3）混合运作策略减少了约2/3的静态系统平均队列长度，企业在队列长度减小的窗口期内可以接受更多订单和缩短MTO订单交货时间。     

A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy

Breast cancer is the leading cause of cancer deaths among women. The selection of an effective, patient-specific treatment plan for breast cancer has been a challenge for physicians because the decision process involves a vast number of treatment alternatives as well as treatment decision criteria, such as the stage of the cancer (e.g., in situ, invasive, metastasis), tumor characteristics, biomarker-related risks, and patient-related risks. Furthermore, every patient's case is unique, requiring a patient-specific treatment plan, while there is no standard procedure even for a particular stage of the breast cancer. In this paper, we first determine a comprehensive set of criteria for selecting the best breast cancer therapy by interviewing medical oncologists and reviewing the literature. We then present two analytical hierarchy process (AHP) models for quantifying the weights of criteria for breast cancer treatment in two sequential steps: primary and secondary treatment therapy. Using the weights of criteria from the AHP model, we propose a new multi-criteria ranking algorithm (MCRA), which evaluates a large variety of patient scenarios and provides the best patient-tailored breast cancer treatment alternatives based on the input of nine medical oncologists. We then validate the predictions of the multi-criteria ranking algorithm by comparing treatment ranks of the algorithm with ranks of five different oncologists, and show that algorithm rankings match or are statistically significantly correlated with the overall expert ranking in most cases. Our multi-criteria ranking algorithm could be used as an accessible decision-support tool to aid oncologists and educate patients for determining appropriate and effective treatment alternatives for breast cancer. Our approach is also general in the sense that it could be adapted to solve other complex decision-making problems in medicine, healthcare, as well as other service and manufacturing industries.     

A stochastic programming model for production planning of perishable products with postponement

This study addresses the production planning problem for perishable products, in which the cost and shortage of products are minimised subject to a set of constraints such as warehouse space, labour working time and machine time. Using the concept of postponement, the production process for perishable products is differentiated into two phases to better utilise the resources. A two-stage stochastic programming with recourse model is developed to determine the production loading plan with uncertain demand and parameters. A set of data from a toy company shows the benefits of the postponement strategy: these include lower total cost and higher utilisation of resources. The impact of unit shortage cost under different probability distribution of economic scenarios on the total cost is analyzed. Comparative analysis of solutions with and without postponement strategies is also performed.     

考虑服务约束的第三方仓库收益与设计优化模型

第三方仓库作为服务提供商, 主要以期望收益最大化为目标, 但是必须满足一定的顾客服务标准。针对高需求环境, 考虑顾客服务水平约束, 提出了一个基于排队论的随机设计优化模型以使仓库的期望收益最大化。采用动态优化算法对模型求解, 选取实例进行了数值实验。结果显示, 模型的优化设计显著地提高了高需求环境下该第三方仓库的期望收益。在此基础上, 找到了服务约束的可行范围和有效范围, 为决策者制定服务标准提供了依据。     

基于云服务的考虑预期调配的应急物资储备策略

储备应急物资是应对突发灾害的有效手段之一。为了满足以人为本、维护稳定和统筹协调的物资储备原则，在储备不足情况下，利用云服务模式统一调配物资。根据应急系统内各储备点的人口覆盖比例和物资预期需求量，以物资储备的公平效用和预期调配成本为目标，构建基于云服务的考虑预期调配的应急物资储备模型，并设计改进的非支配排序遗传算法（NSGA-II）对模型求解。应用该模型对我国应急物资储备系统进行优化配置，确定不同偏好下各储备点的物资配置量。对物资储备总量进行了灵敏度分析，结果表明：物资的购置成本和单位重量是储备总量影响总支出和预期调配成本的决定因素；储备总量的增加会降低预期调配成本，且当购置成本的增加量小于预期调配成本的减少量时，总支出会随着储备总量的增加而减少，直到减少的总支出达到最大值时，得到储备总量的最优值。     

A location–allocation model for service providers with application to not-for-profit health care organizations

In this paper, we develop and solve a model for the location and allocation of specialized health care services such as traumatic brain injury (TBI) treatment. The model is based on and applied to one of the Department of Veterans Affairs’ integrated service networks. A cost minimization model with service proportion requirements is solved using simulated annealing. Large instances of the model with 100 candidate medical center locations and 15 open treatment units are solved in about 1000 s. In order to test the real-world applicability of our model, an extensive managerial experiment is conducted using data derived from our health care setting. In this experiment, the effects of three critical factors: (1) degree of centralization of services, (2) the role of patient retention as a function of distance to a treatment unit, and (3) the geographic density of the patient population are investigated with respect to the important trade-off between the cost of providing service and the need to provide such service. Our analysis shows that all three factors of the experiment are both relevant and useful to decision-makers when selecting locations for their services.     

A fair evaluation of certain stage in a two-stage structure: revisiting the typical two-stage DEA approaches

The efficiency of decision processes with a two-stage structure has been studied by some modified versions of Data Envelopment Analysis (DEA) methodology, such as the relational or centralized model, and the non-cooperative model. After revisiting the rationale of the centralized model provided by the literature, we find that some unfairness exist in its efficiency evaluation of certain stage. This unfairness leads to the usual underestimation of the overall efficiency by the centralized model. Furthermore, because the independent DEA model for one stage ignores its relation and coordination with the other stage and the two-stage system, externalities between these members may arise and lead to the seeming contradiction in efficiencies derived by independent DEA models for the stages and the black box system. Therefore, we argue that the efficiency of certain stage in the context of a two-stage structure should be reevaluated instead of simply using the independent DEA model. A sequence of leader-follower procedures, with data adjustment on intermediates, is proposed to eliminate the externalities and to ensure a fair evaluation. We find that, after this data adjustment, the reevaluation of the second stage for a given Decision Making Unit (DMU) yields the same result as obtained by the standard DEA model regarding the two-stage as a black box. Moreover, some explicit relations are established between the black box model, the centralized model, and the non-cooperative model. Two typical examples taken from the literature illustrate our main results. Our findings also imply that more emphasis should be placed on the game theoretic DEA approach to model the efficiency evaluation of two-stage processes.