突发事件下的医院应急资源冗余配置优化模型

突发事件发生前,医院需要保持一定的应急资源冗余量。针对突发事件下医院应急资源冗余的配置优化问题,首先,基于效用理论,通过分析医院应急资源冗余的效用体现来对应急资源冗余进行定义和分类,确定了符合边际规律的效用函数;其次,建立了总效用最大化的医院应急资源冗余配置模型,并给出应急资源储存量的上限及应急合理度的下限作为模型的约束条件;最后,把粒子群算法和序列二次规划法相结合进行寻优求解。通过案例分析,得出了医院四种应急资源冗余的优化方案,总结出了医院的应急水平对医院应急资源冗余的需求程度。研究表明,应急资源冗余配置优化模型能够帮助医院在突发事件下很好地进行应急救援,提高医院应急资源的利用效率。     

基于马尔可夫决策的应急物资动态分配模型

研究供需不平衡环境下的应急物资动态分配问题.考虑到台风灾害演变导致应急物资需求不断增长与应急物资供应相对紧缺之间的矛盾,将需求的演变设计成一个马尔可夫决策过程,建立基于马尔可夫决策的应急物资动态分配模型.通过二进制粒子群优化算法求解,最后将所提出模型应用于某台风发生时的救灾实例.实例分析表明,马尔可夫决策方法可以动态地做出合适的需求扑灭策略,使得整体的需求演变趋势保持平稳,整体的需求水平降到最低.     

突发事件快速应急救援系统的设计研究

针对当前突发事件应急救援体系存在的救援不及时、预案不完备等问题,本文利用计算机开发工具设计了一款突发事件快速应急救援系统,主体架构由基础层、数据层、应用层组成,系统集情报搜集、分析决策、动态反馈三大主要功能于一体。利用基础情报搜集设施进行大范围的数据采集,通过情报搜集,筛选出有价值的信息,系统自动进行分析决策,预案自动生成模块中内置各类突发事件分类、突发事件危害分级标准,并根据突发事件和应急预案实例建立相关的模版数据库。系统根据突发事件的实时进展进行应急预案的相应更新,完成动态反馈,进而为快速、高效的应急救援工作提供参考方案和决策支持。     

面向突发事件的应急资源调度模型的构建和求解

针对突发事件发生后存在多个事故、应急资源有限的情况,提出了基于非合作博弈的应急资源调度模型和算法。在该调度模型中,各个事故点被视为博弈模型的局中人,可能的资源调度方案映射为策略集,将应急资源的调度问题转化为对非合作博弈调度模型的Nash均衡点求解问题,并对基本蚁群算法进行了改进,嵌入了遗传算法的交叉操作和变邻域搜索策略,将其应用于求解该博弈模型的Nash均衡解。最后通过算例测试验证了模型和算法的有效性和可行性。     

复杂突发事件应急响应模型研究与仿真

研究突发事件应急模型的准确响应问题。针对当前的突发事件的属性越来越复杂,事件中的属性的描述特征也越来越多,导致当前的应急资源响应模型中对事件越来越复杂的多属性描述不精确。当前的突发事件响应模型过于依赖应急资源响应模型对事件的属性描述精度,一旦事件复杂,造成描述精度不高,将造成应急响应匹配准确度不高。为解决上述问题,提出了一种响应模糊概率算法的应急模型响应方法。提取突发事件的特征,建立突发事件与响应模型特征之间的映射联系。通过运算获取不同种类应急模型响应的模糊概率,从而实现应急模型响应。实验结果表明,改进算法能够有效提高应急模型响应的准确性。     

非常规突发事件应急决策冲突消解协调方法

针对应急决策冲突性特点和难以形成高一致性决策方案的问题,分析了应急群体决策冲突产生的原因,构建了应急决策冲突消解协调框架,提出了应急决策群体冲突测度方法,以及应急决策冲突消解模型和方法,形成了非常规突发事件应急决策冲突消解协调机制,使应急群决策冲突逐渐收敛以获得冲突程度足够低的应急决策方案。最后通过算例分析验证了冲突消解协调方法和机制的有效性。     

一种改进的非常规突发事件应急决策集结算法

针对非常规突发事件应急决策集结问题,分析了集结过程中的冲突问题,构建了非常规突发事件应急决策集结框架,在集结过程中通过群体冲突程度指标修正决策偏好信息并提出一种改进的非常规突发事件应急决策集结算法,构建应急决策群体冲突程度指标,并通过该指标来修正决策组成员的偏好信息,改善偏好修正时的主观性和随意性问题,最终获得冲突程度能够接受的应急群体决策方案。最后通过算例验证了该算法的有效性。     

高速公路突发事件应急管理体系研究

随着我国国民经济建设的快速发展,我国的高速公路建设里程数也在不断攀升。根据国家交通运输制定的"十二五规划"(2011-2015),我国高速公路的建设里程到今年年底将突破十万公里大关,达到10.8万公里。届时我国的高速公路网将会把人数超过20万城镇的几乎全部覆盖在内。但是,在我国高速公路建设迅猛发展的同时,高速公路管理工作也面临着严峻的挑战。针对目前我国高速公路突发事件频发的现状,如何能在最短的时间内减少生命和财产损失,保证高速公路的正常运行成为重要的研究课题。     

动态影响图模型研究

部分可观察马尔可夫决策过程在策略空间和状态空间上的计算复杂性,使求解其一个最优策略成为NP-hard难题.为此,提出一种动态影响图模型来建模不确定环境下的Agent动态决策问题.动态影响图模型以有向无环图表示系统变量之间的复杂关系.首先,动态影响图利用动态贝叶斯网络表示转移模型和观察模型以简化系统的状态空间;其次,效用函数以效用结点的形式清晰地表示出来,从而简化系统效用函数的表示;最后,通过决策结点表示系统的行为来简化系统的策略空间.通过实例从3个方面和POMDP模型进行了比较,研究的结果表明,动态影响图模型为大型的POMDP问题提供了一种简明的表示方式,最后在Robocup环境初步验证了该模型.     

面向海上突发事件应急救援任务的协同规划

针对海上突发事件应急救援任务风险大、时间紧、冲突多及协同规划难等问题,急需一套海上应急救援协同机制予以支撑.首先,分析海上突发事件应急救援特点,探索应急联动协同机制;然后,面向海上应急救援任务的时空特点,构建基于时空因素的协同规划模型.进一步,针对指挥协同规划难题,构建基于观察–判断–决策–执行(OODA)作战环的应急救援单元协同联动模型,并着力于分析协同救援单元的一致性及救援时效性,旨在提升应急救援效能.最后,以海上危险化学品泄露救援任务为想定,利用上述模型方法进行规划建模与仿真验证.结果表明,所提出应急救援任务协同规划方法,能够有效的优化协调好海上突发事件的应急救援协同问题,提高了应急救援效能,为海上应急救援部门指挥决策提供了重要支持.     

不确定条件下应急资源布局的鲁棒双层优化模型

针对非常规突发事件中应急资源布局问题,在受灾点需求不确定和应急救援过程分为多个阶段的情景下,建立了省市两级应急储备仓库定位和物资配置的鲁棒双层规划模型。运用相对鲁棒优化方法,将上述具有不确定性系数的双层规划模型转化为从者无关联的确定性线性双层规划,提出了一种混合遗传算法进行求解,实现了省市两级应急资源布局的协同优化。通过实例验证了模型及算法的可行性和有效性。     

A novel model on dynamic resource allocation in optical networks

A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the tra     

A population game approach for dynamic resource allocation problems

We consider a water distribution system as an example of resource allocation, and investigate the use of a population game for its control. We use a game-theoretic approach based on two evolutionary dynamics, the Brown–von Neumann–Nash and the Smith dynamics. We show that the closed-loop feedback interconnection of the water distribution system and the game-theoretic-based controller has a Nash equilibrium as an asymptotically stable equilibrium point. The stability analysis is performed based on passivity concepts and the Lyapunov stability theorem. An additional control subsystem is considered for disturbance rejection. We verify the effectiveness of the method by simulations under different scenarios.     

虚拟组织服务资源的优化配置模型

为了提高虚拟组织服务资源配置的效率,提出了以服务成本、服务时间、服务满意度为目标的资源优化配置模型,采用遗传算法进行求解。在求解中为提高遗传算法的搜索性能,对不可行染色体进行筛选,同时在交叉变异过程中利用邻域搜索提高算法的收敛速度。通过一个具体的实例验证了遗传算法在资源优化配置模型中的有效性。     

部分CSI下C-V2X资源分配算法研究

针对蜂窝车联网（C-V2X）环境中,车辆高速移动导致无法获取完备信道状态信息（channel state information,CSI）,干扰车辆复用蜂窝网络资源的问题。在已知部分CSI下,在满足车辆到设施（vehicle to infrastructure,V2I）和车辆到车辆（vehicle to vehicle,V2V）的可靠性约束的条件下,研究最大化系统遍历总速率的资源分配优化问题,提出联合功率控制和信道复用的资源分配算法。该算法根据可靠性约束,使用几何规划分析功率可行域,求出任意单个复用对的最优功率控制。该算法将信道复用转换为最大权重二分图匹配问题,将复用对的遍历速率作为二分图的权重,并使用KM（Kuhn Munkres）算法进行求解。仿真结果表明,所提资源分配算法较其他算法,可以在保证车辆可靠通信下,优化资源分配,有效控制干扰,并提高系统遍历总速率。     

Optimal resource allocation for time-reservation systems

This paper studies the optimal resource allocation in time-reservation systems. Customers arrive at a service facility and receive service in two steps; in the first step information is gathered from the customer, which is then sent to a pool of computing resources, and in the second step the information is processed after which the customer leaves the system. A central decision maker has to decide when to reserve computing power from the pool of resources, such that the customer does not have to wait for the start of the second service step and that the processing capacity is not wasted due to the customer still being serviced at the first step. The decision maker simultaneously has to decide on how many processors to allocate for the second processing step such that reservation and holding costs are minimized. Since an exact analysis of the system is difficult, we decompose the system into two parts which are solved sequentially leading to nearly optimal solutions. We show via dynamic programming that the near-optimal number of processors follows a step function with as an extreme policy the bang-bang control. Moreover, we provide new fundamental insights in the dependence of the near-optimal policy on the distribution of the information gathering times. Numerical experiments demonstrate that the near-optimal policy closely matches the performance of the optimal policy of the original problem.     

Reinforcement learning based resource allocation in business process management

Efficient resource allocation is a complex and dynamic task in business process management. Although a wide variety of mechanisms are emerging to support resource allocation in business process execution, these approaches do not consider performance optimization. This paper introduces a mechanism in which the resource allocation optimization problem is modeled as Markov decision processes and solved using reinforcement learning. The proposed mechanism observes its environment to learn appropriate policies which optimize resource allocation in business process execution. The experimental results indicate that the proposed approach outperforms well known heuristic or hand-coded strategies, and may improve the current state of business process management.     

Incorporating structural improvement into resource allocation for business process execution planning

Resource management has been recognised as an important topic for business process execution for a long time. Most existing works on resource allocation for business processes simply assume that the structure of a business process is always fixed, and therefore do not discuss the possibility of optimising resource allocation by adapting process structures to actual resource situations. To fill this gap, we propose a resource optimisation approach of improving process structures according to resource situations and thereby pursuing the best resource utilisation efficiency. This approach comprises a role‐based business process model for resource allocation and the strategies for optimising resource allocation in conjunction with a business process improvement. A set of heuristic rules are established to guide the resource allocation for the purposes of preventing resource conflicts, shortening the total execution time, minimising the total cost, etc. Particular algorithms are also developed to implement the resource allocation according to these rules. In addition, an experimental study is conducted to discuss the incorporation of business process improvement into resource allocation for optimal process execution. Copyright © 2012 John Wiley & Sons, Ltd.     

面向效用最大化的数据中心动态资源分配

传统数据中心的负载均衡只追求资源利用的最大化,而忽略了不同类型任务对完成时间的需求是不同的,使得系统总体服务质量无法达到最佳.针对不同任务的需求差异,引入时间效用函数以表征不同类型任务的完成时间与服务质量的关系,并形式化定义了面向效用最大化的动态资源分配问题.由于该问题是NP难的,设计了一个利用任务优先关系的调度机制,其主要思想为将原问题分解为若干同构的小规模子问题,并利用任务间的优先关系,决策为哪个任务优先分配资源.通过理论分析可以证明,当并行的任务的完成时间满足一定的序列性质时,该算法可以取得全局最优解.实验显示,该算法产生的收益是常用的先进先出公平调度机制产生收益1.5倍到2倍,并且比当前最新的算法2DFQ(多租户二维公平排队算法)的总效用高约17％.