基于连续消耗应急系统的多出救点选择问题

针对应急系统多点出救问题的特点,本文引入了连续可行方案的概念,并提出了以最早应急开始时间为目标的数学模型及相应的求解算法。算例及实际应用取得了令人满意的效果     

关于供应链应急事件的发生机理研究

为了探究供应链系统中应急事件发生的机理,本文应用非线性动力学中研究流体同步的方法,建立了供应商和零售商在多周期销售中运作协调的动态模型.该模型从定量的角度描述了供应商和零售商从运作协调到发生应急事件的全过程,并给出了应急事件持续时间的求解方法.     

一类离散应急供应系统的两目标优化模型

本文从应急问题的特点出发,考虑时间紧迫性及系统稳定性因素,建立了应急时间最早、出救点数目最少的多目标数学模型,并运用模糊集理论,通过定义模糊决策集,采用模糊优化方法成功给出了该问题的求解方法。     

基于应急物流的减灾系统LRP研究

减灾系统的定位-路径问题(Location-Routing Problem,LRP)的研究对提高应对突发事件能力和灾害应急管理水平有着重要的意义。本论文将见在系统的定位-路径问题划分为应急服务设施选址和应急资源运输路线安排两个子问题,针对两个子问题建立了以总成本(包括应急服务服务设施点建设成本,运输成本和灾害损失成本)最小为目标的LRP模型,并利用一个两阶段的启发式算法进行求解。最后选择以某一典型台风灾害为例,对基于应急物流的减灾系统的LRP模型进行算例分析,结果表明了模型的合理性和算法的可行性。     

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.     

应急系统多出救点选择问题的模糊规划方法

针对应急系统的特点 ,提出了基于“时间最短”、“出救点数目最少”的多目标数学模型。考虑决策者偏好的模糊性 ,本文采用模糊规划的思想方法处理该问题 ,并给出了相应的求解算法。算例及实际运用令人满意     

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。     

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。     

一类应急物资调度的双层规划模型及其算法

应急物资的调度是应急管理中的一个重要分支,衡量其运作效果的两个最常用的指标是应急时间和应急成本,目前的建模大多都以多目标规划为主.但现实告诉我们,面对应急事件,应该在最短的允许时间奔赴现场,而成本与此目标相比要次之.为此,建立了单资源应急物资调度的双层规划模型,上层为时间目标,下层为成本目标,并将此模型的求解转化为若干个有限制的运输问题的求解,利于计算机实现.算例证明了算法的有效性.     

多级覆盖设施选址问题建模及求解方法研究

针对应急服务网络优化设计中的设施选址决策问题,分析了应急服务的特殊性以及现有的设施选址模型在求解该问题时的局限性,基于传统的最大覆盖模型提出了应急服务设施的多级覆盖选址模型.该模型对"覆盖"概念的定义进行了扩展,考虑了应急服务的多源服务特性以及对多个服务源提供服务时可存在的差异性.之后基于分布估计算法(Estimation of distribution algorilhms,简称EDAs)提出了模型的启发式求解方法,并基于实验结果对算法参数选取给出了参考意见.     

基于事件链的知识导航模型研究

当前突发公共事件应急管理研究已经成为热点问题.本文从应急管理知识需求出发,研究突发事件对象要素属性关系特点,提出了基于事件链的知识导航模型.该模型由资源模型、知识本体模型、应急处置模型、事件对象模型及事件链模型组成.通过对不同模型间的关系研究,向用户提供方便、快捷、适时的知识导航功能,最后给出应用实例.     

一类应急物资调度的优化模型研究

本文讨论物资需求约束条件下多出救点的紧急物资调度问题。根据连续应急问题的特点,给出了应急时间最早前提下出救点数目最少以及限制期条件下出救点数目最少的应急模型。并且从理论上证明了模型求解方法的正确性。     

面向多灾点需求的应急资源调度博弈模型及优化

非常规突发事件爆发后经常会造成多个灾点,而各灾点的需求往往是不同的,单独的应急资源中心很难同时满足这种要求,因此如何把多个应急资源中心所储备的应急资源公平合理地调配到各个灾点成为应急决策者亟待解决的现实问题。本文首先描述了各灾点对应急资源需求变化的动态过程即按照应急资源需求信息的变化将整个应急资源调度过程划分成若干阶段,在此基础上构建了基于多灾点多阶段的应急资源调度过程理论模型。随后以博弈论为工具,在进行一系列模型假设和确定各灾点灾情的前提下,建立面向多灾点需求的应急资源博弈调度模型,并采用改进的蚁群算法进行求解,实现对各灾点以最小的“虚拟成本”进行所需应急资源的调度。最后的模型仿真测试和算例分析验证了所建模型的有效性和可行性。该模型与算法也为商业物流中的资源配送提供了新的解决方案和实现途径。     

基于非概率可靠性的铁路应急设施选址-路径鲁棒优化

鉴于铁路应急设施选址研究中很难合理估计参数的概率分布或确定其隶属函数的问题,将选址-路径问题与区间非概率可靠性方法结合起来,以复杂网络理论为基础,提出网络节点区间权重的确定方法,同时考虑节点权重、边权及径权区间不确定性的共同作用,构建铁路应急设施选址节点加权网络。基于区间非概率可靠性理论及区间运算规则,提出路径的非概率可靠性度量及最优时间可靠度路径选择方法,建立节点权重、边权及径权均为区间数的非概率可靠性铁路应急设施选址-路径鲁棒优化模型,并给出了求解算法,确定了基于区间模型的铁路应急设施鲁棒选址的最优方案。算例表明,本文的优化方案能更好地保证救援的时间鲁棒性,能有效地规避不确定因素波动对设施选址的长期风险,具有很好的实际应用价值。     

突发事件应急救援人员的派遣模型研究

本文针对突发事件应急救援人员派遣问题,考虑位于不同出救点的救援人员到达救援需求点执行救援任务的应急救援时间满意度,以及救援人员对不同应急救援任务的胜任程度,以应急救援时间满意度最大以及救援人员完成救援任务的"效果"最佳为目标,建立了突发事件应急救援人员派遣的优化模型。针对优化模型的特点,给出了有效的求解方法。最后,通过一个算例分析说明了构建的模型及所给出的求解方法的可行性和有效性。     

基于煤矿透水事故应急响应时效性的水泵布局鲁棒优选问题研究

为了提高煤矿透水事故中矿工获救和恢复健康的可能性，本文基于应急响应时效性研究关键资源-水泵布局问题。已有应急资源布局问题的研究多从成本、时间、资源需求满足度等方面进行，煤矿透水事故应急响应时效性是综合考虑被困矿工获救时间以及被困过程中被困位置最低氧气含量和食物缺少量三方面要素的事故应对效果。首先，建立了基于煤矿透水事故发生、发展和应急响应机理的三方面要素计算方法；然后，在供氧和供食物所需资源布局给定的前提下，设计水泵布局的目标函数和约束条件，建立水泵布局鲁棒优选模型，通过机会时间窗概念的引入和分支定界算法的思想，给出水泵布局不可行方案的判别准则，并在此基础上进行优选算法的设计；最后，在实际发生的煤矿透水事故典型案例基础上，构造算例，检验水泵布局鲁棒优选模型的有效性。     

Context‐Specific,Scenario‐Based Risk Scales

Reacting to an emergency requires quick decisions under stressful and dynamic conditions. To react effectively, responders need to know the right actions to take given the risks posed by the emergency. While existing research on risk scales focuses primarily on decision making in static environments with known risks, these scales may be inappropriate for conditions where the decision maker's time and mental resources are limited and may be infeasible if the actual risk probabilities are unknown. In this article, we propose a method to develop context‐specific, scenario‐based risk scales designed for emergency response training. Emergency scenarios are used as scale points, reducing our dependence on known probabilities; these are drawn from the targeted emergency context, reducing the mental resources required to interpret the scale. The scale is developed by asking trainers/trainees to rank order a range of risk scenarios and then aggregating these orderings using a Kemeny ranking. We propose measures to assess this aggregated scale's internal consistency, reliability, and validity, and we discuss how to use the scale effectively. We demonstrate our process by developing a risk scale for subsurface coal mine emergencies and test the reliability of the scale by repeating the process, with some methodological variations, several months later.     

Do Faster Rescues Save More Lives

The primary mission of search and rescue (SAR) is the saving of lives. To assess SAR operations from a planning perspective, one must draw a connection between operations and the number of lives saved. Our approach is to model the probability that an incident results in at least one fatality, given the response time between the time of incident occurrence and time of rescue. We show that incidents involving air crashes, capsizing, foundering, grounding and other/unknown types of incidents tended to have higher probabilities of fatalities as the response time became higher. However, other emergency types did not exhibit the same overall tendency as these did. These statistical results do not prove causality between faster response times and lower fatality incidence for the above-mentioned emergency types. They can be used, however, for estimating the average number of fatalities for a given distribution of response time, and ultimately the marginal savings in lives for a change in the mix of resources and locations.     

Surgical scheduling under uncertainty by approximate dynamic programming

Surgical scheduling consists of selecting surgeries to be performed within a day, while jointly assigning operating rooms, starting times and the required resources. Patients can be elective or emergency/urgent. The scheduling of surgeries in an operating theatre with common resources to emergency or urgent and elective cases is highly subject to uncertainties not only on the duration of an intervention but mainly on the arrival of emergency or urgent cases. At the beginning of the day we are given a candidate set of elective surgeries with and an expected duration and a time window the surgery must start, but the expected duration and the time window of an emergency or urgent case become known when the surgery arrives. The day is divided into decision stages. Due to the dynamic nature of the problem, at the beginning of each stage the planner can make decisions taking into account the new information available. Decisions can be to schedule arriving surgeries, and to reschedule or cancel surgeries not started yet. The objective is to minimize the total expected cost composed of terms related to refusing arriving surgeries, to canceling scheduled surgeries, and to starting surgeries out of their time window. We address the problem with an approximate dynamic programming approach embedding an integer programming formulation to support decision making. We propose a dynamic model and an approximate policy iteration algorithm making use of basis functions to capture the impact of decisions to the future stages. Computational experiments have shown with statistical significance that the proposed algorithm outperforms a lookahead reoptimization approach.     

A hybrid inventory management system responding to regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.