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。     

基于双层规划的反恐应急设施选址模型及算法

恐怖袭击常以人流密集地区的平民作为袭击目标，并存在突发性和随机性等特点，极易造成严重的袭击后果。通过反恐应急设施的合理布局可以缩短救援人员和物资的到达时间，从而减轻袭击后果。首先，对反恐应急设施选址问题进行描述，并将其构造为一类离散双层规划模型。其中，上层规划是关于政府选址的0-1规划问题，下层规划则是关于恐怖分子袭击目标选择的0-1规划问题。其次，结合模型和问题的特征设计算法，利用分支定界算法实现上层选址变量的隐枚举，同时通过下层问题的求解来确定上下界并判断是否满足分枝或剪枝的条件。最后，结合南疆地区的交通拓扑网络进行算例分析，结果证明有效的选址方案可以大大降低袭击损失。     

基于场景分析的应急资源布局模型研究

突发事件越来越频繁地发生,对应急物资保障提出了更高的要求,好的资源布局方案对地区需求的保障程度至关重要。本文利用场景分析的方法,给出一个适于多点需求,多点救助的应急资源布局多目标规划模型,并根据资源的具体配置情况决定选址,以期为选址决策者提供选址的依据。求解该问题时,针对模型设计了算法,并进行了算例分析。     

基于随机运输路径选择的物流中心选址模型

旨在以物流系统总费用最小化为目标进行物流中心选址规划.政府做出物流中心选址规划后,客户会选择合适的供应商进行交易,并根据已有物流中心与交通流分布进行货物运输路线决策,使其总费用最少.论文提出了物流中心选址双层规划模型,考虑投资费用的约束,保证用户平衡的同时使整个物流系统总费用最低.其中上层规划目标是使物流系统总费用最小化,下层规划建立了一个Logit随机用户均衡模型,并构造了一个等价的凸规划问题.最后针对模型提出了一个算法,并通过算例说明其可行性.     

应急储备库选址与资源配置随机规划模型研究

研究在突发事件多发区域进行应急储备库的选址与资源配置规划问题.假定了若干随机的突发事件情景,每种情景下物资需求量与运输时间不确定,结合情景分析法,建立了一定应急限制期条件下的两阶段随机规划模型,并设计了混合遗传算法对模型进行求解.研究结果不仅找到了最优的选址与资源配置方案,而且发现牺牲需求满足率与应急限制期带来的成本降低是不经济的.     

考虑最坏中断损失下的P-中位设施选址问题的模型与算法研究

蓄意突袭以及恐怖袭击会造成设施服务的突然中断成为网络系统的主要危害之一,因此网络设施选址决策应该同时考虑正常和紧急状态下系统的运作成本.本文研究考虑最坏中断损失下的网络设施选址问题,建立了该问题的双层规划模型,上层规划涉及设施选址决策,下层规划研究确定设施位置后,设施中断产生最大损失的问题.本文运用基于拉格朗日松弛的混合遗传算法来求解该双层规划问题.将European150数据集作为研究对象,对比研究了本文研究问题与传统的P-中位选址问题的结果,分析不同选址策略下网络系统的效率被中断影响的程度是不同的.最后通过改变一些关键参数,比如常规运作权重、设施数量、中断设施数量,对相关结果进行了分析.     

随机需求下联合选址-库存模型研究

研究了一类具有季节性需求特性的商品的联合选址-库存模型。在传统的无容量限制的固定费用设施选址问题中考虑了分销中心的运作库存和安全库存的影响,以及规模经济效应和风险分摊效应,同时考虑了季节性商品未来需求的不确定性,将订货决策作为模型的决策变量,建立了一类随机需求下以期望销售收益最大化为目标函数的联合选址-库存模型,拓展了已有的联合选址-库存模型。该模型是一个混合整数规划问题,给出了求解该问题的基于拉格朗日松弛算法的两阶段算法,最后通过随机生成四组不同规模的数值算例,得到的计算结果表明拉格朗日松弛算法可以有效地求解该问题。     

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。     

基于实时信息的游客行程动态规划研究

基于实时信息的游客行程动态规划问题可适用于城市景点的游客行程规划、主题公园的游客行程规划、博物馆的游客游览路线规划等服务系统的实际场景。本文采用重规划方法将该问题转化为离散时间段上的静态规划子问题,建立了对应的混合线性整数规划模型,并证明了该问题的NP难性质。提出了一种分支定界算法来求解静态子问题的优化模型,并设计了一种变邻域搜索算法来求解对应的大规模问题。通过数值实验验证了所提的模型和算法,并进行了算法参数标定和算法比较分析。数值实验的结果表明,所提分支定界算法和变邻域搜索算法的计算性能都明显优于已有文献的算法。所提的模型和算法可以嵌入到管理信息系统中,对于提升服务系统的工作效率、降低顾客的等待时间、优化服务系统的资源配置等具有实际意义。     

考虑自取服务和门到门服务的电动汽车物流网络优化策略研究

在环境意识增长与政府政策支持的有利条件下,电动汽车在物流领域得以快速发展。为提高物流服务的效率,降低企业运营成本,文中研究了考虑顾客服务策略的电动物流汽车服务设施选址与配送路径问题。采取顾客自行取货与配送人员送货上门相结合的多样化服务策略,使得服务站点的建设成本、顾客点配送路径成本以及服务站点补货路径成本之和最小。建立了整数规划数学模型,允许配送车辆在服务过程中前往服务站点接受充电服务。其次,提出了基于改进节约算法和禁忌算法的混合启发式算法MCWSA-TS。随后,在小规模算例将CPLEX运算结果与MCWSA-TS进行对比,证明了算法的有效性。最后,采用多组算例探讨了顾客取货半径对运营成本的影响,并对分离配送策略与联合配送策略进行对比分析。实验结果表明,多样化服务策略有助于企业满足顾客取货时间和取货方式的个性化需求。同时,兼顾运营成本与顾客满意度,促进电动汽车参与的物流服务快速发展。     

A nested-compliance table policy for emergency medical service systems under relocation

The goal of Emergency Medical Service (EMS) systems is to provide rapid response to emergency calls in order to save lives. This paper proposes a relocation strategy to improve the performance of EMS systems. In practice, EMS systems often use a compliance table to relocate ambulances. A compliance table specifies ambulance base stations as a function of the state of the system. We consider a nested-compliance table, which restricts the number of relocations that can occur simultaneously. We formulate the nested-compliance table model as an integer programming model in order to maximize expected coverage. We determine an optimal nested-compliance table policy using steady state probabilities of a Markov chain model with relocation as input parameters. These parameter approximations are independent of the exact compliance table used. We assume that there is a single type of medical unit, single call priority, and no patient queue. We validate the model by applying the nested-compliance table policies in a simulated system using real-world data. The numerical results show the benefit of our model over a static policy based on the adjusted maximum expected covering location problem (AMEXCLP).     

震后多目标动态应急医疗设施选址-伤员转运问题研究

地震等灾害的突发性和破坏性常导致部分伤员无法得到快速有效的救治,同时会给伤员造成一定的负面心理,影响救援效率。本文综合考虑救护车辆(救护车、直升机)和医疗设施容量的动态变化、各类伤员生存概率随时间动态变化以及伤员心理状况变化,构建了最大化伤员生存数量和最小化心理成本的震后伤员二级后送模式的医疗设施选址-伤员转运双目标动态规划模型。运用epsilon约束法有效处理双目标模型,以玉树地震后伤员后送问题为例,采用CPLEX对模型进行求解,通过分析医疗资源数量对伤员转运数量的影响,表明在伤员后送过程中,增加临时医院数量或容量与救护车数量比增加后方医院数量或容量与直升机数量更有效;考虑伤员的心理成本,为了提高伤员存活率,在灾害前期,可以通过增加救护车数量,转运更多的重伤员,而中后期提高临时医院容量,尽量优先转运轻伤员。     

Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty

We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.     

设施失灵风险下不确定需求应急定位-路径鲁棒优化研究

为抵御突发灾害对路网造成的破坏性和设施失灵风险,降低系统成本,并快速完成应急救援任务,本文考虑到受灾点物资需求量的不确定和风险对救援系统的影响,采用直升机进行物资运送以规避路径风险。建立了最小化应急物流系统总成本和物资到达需求点总救援时间为双目标的应急物流定位-路径鲁棒优化模型,基于相对鲁棒优化方法处理需求不确定,采用偏差鲁棒优化思想描述设施失灵风险损失,采用遗传算法进行求解。通过对三个算例进行数据仿真实验,证明了相对鲁棒优化方法在处理需求不确定和偏差鲁棒优化方法在处理设施失灵风险方面的有效性,进而为解决应急设施点的开设和救援物资的安全及时准确配送,增强应急物流系统的风险应对能力提供了有效的方法。     

A Bicriterion Maximal Covering Location Formulation Which Considers the Satisfaction of Uncovered Demand

There have been many applications of the maximal covering location problem (MCLP). An underlying assumption of the MCLP is that demand not covered (i.e., not within a prespecified maximal distance of a facility) is not served. This may be an unrealistic assumption in many location planning scenarios, especially in the public sector. For example, in cases such as fire protection or ambulance service, calls not technically covered will still be serviced. The MCLP, however, does not consider the distances or travel times necessary to service such demand. This paper presents a bicriterion locational covering model which explicitly considers the travel distance or time necessary to service demand not within the maximal covering distance of a facility. The model may be used to generate noninferior (Pareto optimal) siting configurations which demonstrate the inherent trade-offs between a siting scheme designed to maximize total coverage and one designed to minimize total travel time for uncovered demand to reach its nearest facility. In addition, it is shown that for any particular weighting scheme on the two objectives, the problem can be solved as a p-median problem; a problem for which several efficient solution methods exist.     

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.     

Tactical and operational planning of express intra-city package services

In this paper, we focus on routing and load planning associated with the tactical and operational planning of an express intra-city courier service provider that receives a large number of shipments on a daily basis and is committed to delivering them to their destinations given a short service guarantee. Tactical planning relies on high-level aggregated demand rates over long time periods and takes the form of a multi-commodity service network design where the goal is to identify one path per commodity while maximizing consolidation opportunities in the network. Commodities are transported on their paths by means of a series of continuously operating vehicle cycles, where the structure and number of such cycles are determined concurrently with commodity path assignment decisions in a mixed integer programming. A second model is designed to refine the time allocation along different segments of a commodity path allowing a potential reduction in the number of vehicles required to meet the service guarantee. In operational planning, the focus is narrowed down to a shorter time period, and the baseline plan obtained from the tactical planning phase is adjusted to better fit potential deviations in observed demand patterns compared to the aggregate patterns. Through an extensive computational study designed on the topology of a major US city, we observe that the plans designed at the tactical level guarantee high service levels, which are improved at the operational level by customizing the plan to the special characteristic of a day of operation.     

A multi-period ambulance location and allocation problem in the disaster

To achieve quick response in the disaster, this paper addresses the issue of ambulance location and allocation, as well as the location problem of temporary medical centers. Considering budget and capacity limitations, a multi-period mixed integer programming model is proposed and two hybrid heuristic algorithms are designed to solve this complex problem. The proposed model and algorithm are further verified in a real case study, and the numerical experiments demonstrate the effectiveness of our proposed model. Specifically, we obtain several findings based on the computational results: (1) The best locations of ambulance stations should change in each period because the demand rate changes over time. (2) Involving temporary medical centers is necessary to reduce the average waiting time of injured people. (3) It may not be optimal to allocate ambulances from the nearest ambulance stations because of potentially limited station capacity.

     

Emergency Department Staff Planning to Improve Patient Care and Reduce Costs

In the face of high staffing costs, uncertain patient arrivals, and patients unsatisfied with long wait times, staffing of medical emergency departments (EDs) is a vexing problem. Using empirical data collected from three active EDs, we develop an analytic model to provide an effective staffing plan for EDs. Patient demand is aggregated into discrete time buckets and used to model the stochastic distribution of patient demand within these buckets, which considerably improves model tractability. This model is capable of scheduling providers with different skill profiles who work either individually or in teams, and with patients of varying acuity levels. We show how our model helps to balance staffing costs and patient service levels, and how it facilitates examination of important ED staffing policies.