基于成本效益分析的救护车多时段布局优化研究

院前急救服务水平和救护资源之间存在悖反效益,本文综合考虑急救服务效果与急救网络成本,应用延误成本刻画急救效果,运营成本刻画急救资源使用,同时考虑需求规模、需求空间分布、救护车行驶速度以及救护车不可获得率随时间变化的影响,建立以最小化社会总成本为目标的救护车多时段布局优化模型,应用上海市松江区的实际数据,系统研究多时段救护车布局优化问题。计算结果表明优化后的系统在保证80%的高标准覆盖水平下,社会总成本比原系统下降32.23%。相比静态的情况,考虑时变因素可以使社会总成本下降15.8%,双覆盖率提高12.84%,各时段车辆繁忙率方差下降91.67%。     

基于改进NSGA-II算法的多级服务设施备用覆盖选址决策模型

为了应对跨区域突发事件过程中受灾点服务差异化需求的问题,建立了应急储备设施点的多级备用覆盖选址决策模型,即一个需求点由多个应急设施提供不同质量水平的服务,并考虑设施繁忙状态下由其他设施点提供服务的状况,使模型更加符合实际应用。首次通过设计分段的染色体编码方式改进NSGA-II算法提升运算效率以更好地解决多目标选址决策问题,将改进方法下得到的Pareto解分布与NSGA-II算法下的仿真结果进行对比分析,结合设施点的部署策略得到不同的空间布局方案。证明了模型的可行性及改进NSGA-II算法在解决设施点多目标选址决策问题时的有效性。     

“互联网+”时代不同时空域出租车资源供需匹配模型

本文首先收集长沙市2001-2012年影响出租车供求关系数据,并进行主成分分析,得出影响供求匹配的最主要影响指标——空驶率;然后对不同城市进行需求量分析后得到需求函数,求出这些城市的需求量,再与当地出租车拥有量进行比较分析出供求关系;最后对于不同时段的出租车需求建立模型,得出高峰时期交通需求的增加会使空驶率有所降低的结论.     

重大突发事件应急设施多重覆盖选址模型及算法

为了解决应对重大突发事件过程中应急需求的多点同时需求和多次需求问题,本文研究了应对重大突发事件的应急服务设施布局中的覆盖问题:针对重大突发事件应急响应的特点,引入最大临界距离和最小临界距离的概念,在阶梯型覆盖质量水平的基础上,建立了多重数量和质量覆盖模型。模型的优化目标是满足需求点的多次覆盖需求和多需求点同时需求的要求条件下,覆盖的人口期望最大,并用改进的遗传算法进行求解;最后给出的算例证明了模型和算法的有效性,从而应急设施的多重覆盖选址模型能够为有效应对重大突发事件的应急设施选址决策提供参考依据。     

城市医疗急救绩效分析研究

为了准确评估城市医疗急救(120急救)系统的救援绩效,面向救护车救援调度过程,通过将救护车状态定义为空闲或繁忙建立救护车队的救援状态空间,基于条件概率的乘法规则以及生灭过程平衡方程,构造求解各救护车工作强度(救护车处在繁忙状态的时间占比)的近似线性方程组及其迭代求解算法,由此提出了救护车工作强度近似模型。基于救护车工作强度,给出了救护车跨区救援比例、响应时间等救援绩效计算方法。为了验证上述模型,评价了苏州市区120急救系统的绩效指标,据此改善了救护车救援系统配置方案。研究表明,救护车工作强度近似模型克服了以往救护车数量较多时(大于20辆)难以求解的困难;根据救援距离设置救护车指派优先级能够实现救护车共享、平衡各救护车工作强度以及跨区救援比例;在不增加救护车总数的情况下,基于救援绩效能够改善救护车分布以及急救辖区划分从而有效缩短医疗急救响应时间。     

化工园区应急设施区间规划选址模型研究

应急设施选址受应急物资需求量的影响。为优化应急设施选址布局,提高突发事件应急处置能力,以化工园区突发事件为研究背景,对化工园区突发事故下应急设施选址进行研究。考虑到化工园区突发事件的随机性和复杂性、突发事件应急物资需求的不确定性等特点,以应急设施选址安全性最大、经济性和服务效益最好为目标,基于传统确定性应急设施选址模型,构建了不确定需求条件下化工园区应急设施选址区间规划数学模型。模型中应急物资需求量是一个区间值,通过引入区间规划理论和模糊理论对模型进行求解,不仅避免了不确定参数随机概率分布的波动率,而且也降低了模型求解过程中的不确定性。最后,以园区各企业潜在事故为工程背景进行实例分析,得到园区应急设施的布局方案。结果表明,模型的求解效果较好,可为园区应急设施选址决策提供参考依据。     

及时获得信息的销售存储策略研究

传统的零售商的最佳订货量模型中,单位时间的需求量(率)是确定的或不确定的.本文考虑了需求量(率)不确定时并且可继续获取需求信息时的最优订货量问题的矫正策略.     

带双阈值的一类更新风险模型的Gerber-Shiu折现罚函数

本文考虑一类索赔时间间隔为Erlang(2)分布的"双界限"分红模型,在这种模型中,当盈余超过上限时分红以不超过保费率的速率付出,低于下限后保费率增大,得到了关于Gerber-Shiu罚金折现期望函数满足的积分-微分方程以及更新方程,进一步讨论了更新方程的解.     

基于顾客选择行为的租车需求修复方法研究

为了提高顾客对不同类车型租车需求量的预测精度,需要对历史约束数据进行修复.传统Spill模型能够在一定程度上修复受约束需求数据,但并没有考虑到顾客的主观选择行为.为此,设计顾客租车行为调查,利用多项Logit模型对数据进行处理,得到顾客偏好概率,在此基础上改进Spill模型.通过实例验证分析,改进Spill模型比原模型平均误差更小,说明改进模型更有效.     

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。     

多储备点多品种应急物资配送的多目标优化模型

应急救援的社会化、应急物资需求的多样性、应急物资需求和补给的时变性,对应急物流的配送调度提出了挑战.应急物流的紧急性要求最大程度保障受灾点的物资供应;在经济上则要求应急物流的成本最小化.通过将时间离散化为阶段序列,在应急物资需求和补给是可预测的情况下,建立一个多储备点、多物资品种、单受灾点的应急物资配送的多目标优化模型,来最小化应急物资短缺次数和运输成本.仿真实例表明,该模型可以通过优化引擎快速求解,能够发现导致短缺的应急物资品种和加强供应补给的时间区间.     

An optimization approach for ambulance location and the districting of the response segments on highways

In this paper we present a method to optimize the configuration and operation of emergency medical systems on highways. Different from the approaches studied in the previous papers, the present method can support two combined configuration decisions: the location of ambulance bases along the highway and the districting of the response segments. For example, this method can be used to make decisions regarding the optimal location and coverage areas of ambulances in order to minimize mean user response time or remedy an imbalance in ambulance workloads within the system. The approach is based on embedding a well-known spatially distributed queueing model (hypercube model) into a hybrid genetic algorithm to optimize the decisions involved. To illustrate the application of the proposed method, we utilize two case studies on Brazilian highways and validate the findings via a discrete event simulation model.     

Improving fairness in ambulance planning by time sharing

Most literature on the ambulance location problem aims to maximize coverage, i.e., the fraction of people that can be reached within a certain response time threshold. Such a problem often has one optimum, but several near-optimal solutions may exist. These may have a similar overall performance but provide different coverage for different regions. This raises the question: are we making ‘arbitrary’ choices in terms of who gets coverage and who does not? In this paper we propose to share time between several good ambulance configurations in the interest of fairness. We argue that the Bernoulli–Nash social welfare measure should be used to evaluate the fairness of the system. Therefore, we formulate a nonlinear optimization model that determines the fraction of time spent in each configuration to maximize the Bernoulli–Nash social welfare. We solve this model in a case study for an ambulance provider in the Netherlands, using a combination of simulation and optimization. Furthermore, we analyze how the Bernoulli–Nash optimal solution compares to the maximum-coverage solution by formulating and solving a multi-objective optimization model.     

Ambulance location and relocation problems with time-dependent travel times

EMERGENCY SERVICE PROVIDERS ARE FACING THE FOLLOWING PROBLEM: how and where to locate vehicles in order to cover potential future demand effectively. Ambulances are supposed to be located at designated locations such that in case of an emergency the patients can be reached in a time-efficient manner. A patient is said to be covered by a vehicle if (s)he can be reached by an ambulance within a predefined time limit. Due to variations in speed and the resulting travel times it is not sufficient to solve the static ambulance location problem once using fixed average travel times, as the coverage areas themselves change throughout the day. Hence we developed a multi-period version, taking into account time-varying coverage areas, where we allow vehicles to be repositioned in order to maintain a certain coverage standard throughout the planning horizon. We have formulated a mixed integer program for the problem at hand, which tries to optimize coverage at various points in time simultaneously. The problem is solved metaheuristically using variable neighborhood search. We show that it is essential to consider time-dependent variations in travel times and coverage respectively. When ignoring them the resulting objective will be overestimated by more than 24%. By taking into account these variations explicitly the solution on average can be improved by more than 10%.     

Alternate risk measures for emergency medical service system design

The stochastic nature of emergency service requests and the unavailability of emergency vehicles when requested to serve demands are critical issues in constructing valid models representing real life emergency medical service (EMS) systems. We consider an EMS system design problem with stochastic demand and locate the emergency response facilities and vehicles in order to ensure target levels of coverage, which are quantified using risk measures on random unmet demand. The target service levels for each demand site and also for the entire service area are specified. In order to increase the possibility of representing a wider range of risk preferences we develop two types of stochastic optimization models involving alternate risk measures. The first type of the model includes integrated chance constraints (ICCs ), whereas the second type incorporates ICCs  and a stochastic dominance constraint. We develop solution methods for the proposed single-stage stochastic optimization problems and present extensive numerical results demonstrating their computational effectiveness.     

Ambulance location and relocation models in a crisis

This article deals with the relocation of ambulance vehicles from their origin location (i.e., position before a crisis occurs) to a crisis area and to undercovered areas (i.e., no ambulance vehicle is available for a potential emergency in a given region). Support for a crisis area can lead to insufficient coverage of other emergency patients in other regions, so decision makers need assistance with useful relocation information, including the relocation of vehicles to the crisis area and to undercovered areas. As optimization criteria, this study considers two objectives: undercoverage (i.e., unsupported demand) and the total time needed to get to the crisis area with all vehicles. A proposed model aims to minimize the time required for the vehicle relocation process and avoid relocation mistakes (e.g., sending a vehicle to support the crisis area when it would be better to leave it at its current location). Devising the relocation plan consists of three phases. First, a location model allocates all available vehicles to potential vehicle locations (positioning vehicles before the crisis occurs). Second, the same location model allocates the remaining vehicles in the case of a crisis (all vehicles not needed to manage the crisis). Third, the relocation model (phase three) moves vehicles from their position before the crisis occurs (phase one) to their position during the crisis (phase two).