带时变供求约束的应急物资分配模型

针对应急物流系统中出救点供应量、需求点需求量带时变条件约束的物资分配优化问题,从分析满足需求条件下的最早应急响应时间入手,建立应急响应开始时间最早的带时变供求约束的物资分配决策模型,并结合汶川地震中绵阳市的医用物资分配实际运作特点设计了模拟算例,算例结果证明模型与算法有效地解决了物资分配的问题.     

时变环境下选址问题的一个启发式算法

配送中心选址作为物流管理中的一个重要研究问题受到广泛重视。然而,文献中所涉及的选址模型大都是静态的,即沿一条路径行进时所需的成本(或时间)是与出发时间无关的函数。但在实际生活中,它往往是会随时间的变化而变化的,这类问题被称为时变环境下的选址问题。文中讨论了时变环境下的单配送中心到多个零售点带容量约束的选址问题。由于该问题是NP-完备的,我们给出了一个启发式算法。     

一种多车场多车型VSP问题的求解算法

论文考虑了一种具有模糊指派功能、车辆具有车型利用水平约束和容量约束、顾客有Hard时间窗的多车场车辆调度问题。同时设计出两阶段的求解算法，第一阶段考虑将各个客户需求点模糊指派给各个车场；第二阶段安排优化子路线时，增加了多车型、车型利用水平、硬时间窗的约束。最后给出一个算例。     

基于混合集合规划的多场景应急设施选址优化

多场景应急设施选址问题具有约束复杂、时效性强、规划周期短等特点。以混合集合规划为算法框架的自然约束语言NCL能对该问题进行建模与求解。以一个典型多场景应急设施选址问题为例,证明了NCL可有效求解该类问题。     

基于混合集合规划的多场景应急设施选址优化

多场景应急设施选址问题具有约束复杂、时效性强、规划周期短等特点.以混合集合规划为算法框架的自然约束语言NCL能对该问题进行建模与求解.以一个典型多场景应急设施选址问题为例,证明了NCL可有效求解该类问题.     

时变环境下物流中心选址问题研究

研究了在时变环境下,允许从配送中心出发同时访问R个配送点,且兼顾费用与可靠性的选址问题,这里R是预先给定的容量约束.问题本身是NP完备的,给出了一个确定单个配送中心的启发式算法.     

基于公平性的灾前多目标公交疏散模型

面对洪涝、海啸等重大可预测性自然灾害的发生,高效的灾前疏散可以减少人员伤亡。针对当前疏散行动片面关注效率而忽略公平这一问题,采用相对剥夺成本衡量集结点之间获得疏散服务的公平程度,以驾驶时间、相对剥夺成本和公交数量最小化为目标,将集结点时间窗、公交载客量等作为约束,构建多目标公交疏散路径优化模型以协同优化疏散效率和公平;模型采用NSGA-Ⅱ算法求解。算例结果表明,疏散时效与公平、疏散时效与所用公交数量之间存在悖反关系,疏散公平与所用公交数量呈正相关;与仅考虑疏散时效的单目标模型对比,兼顾疏散效率与公平的疏散方案能在略微降低疏散时效和略微提高所用公交数量的基础上,显著降低疏散的不公平程度。结果有助于为实际疏散行动提供有效参考。     

一种面向多资源约束的关键链识别方法

针对资源受限项目调度问题,提出了一种面向多资源约束的关键链识别算法。该算法首先基于优先规则的资源受限并行调度算法生成调度结果,接着依据此结果将活动的资源约束转换为活动的时序约束,形成新的时序约束活动依赖矩阵,然后以此为基础结合传统向后调度算法计算在此时序约束下活动的总时差,最后根据总时差值来确定关键活动,并组成关键链。     

基于公平的拥堵收费和可交易电子路票的多模式物流网络优化模型

基于拥堵收费和可交易的电子路票建立了多模式物流网络优化模型。模型考虑了商品所有者的收入对多模式物流网络下配送需求的产生、配送模式的选择以及配送路径选择的影响,得到了物流网络流畅策略对不同配送商品价值和地域分组的物流需求分配的影响。模型被描述为带有均衡约束的数学规划问题,并可通过无导数算法求解。     

物流企业多式联运的运输方案优化选择

针对物流企业多式联运运输方案的优化选择问题进行研究,考虑运输费用、时间窗等约束问题建立数学规划模型,并把问题转换为网络问题,根据网络特性采用动态规划方法进行求解。通过实例分析表明,算法可行、有效,优化方案可为决策者提供依据。     

自然灾害下的公交疏散路线模型

自然灾害通常给社会带来巨大的危害,以自然灾害所引起的紧急情况作为研究背景,进行应急交通疏散预案研究。讨论自然灾害下的应急交通疏散问题并重点探讨疏散路线设计、疏散时间计算,用以达到在自然灾害发生前将人们转移至安全地点的目的。利用公交车作为疏散工具,将公交疏散路线问题归为带时间窗的车辆路径问题(VRPTW),建立基于VRPTW的公交疏散路线模型,以某地区为案例利用TransCAD软件中的车辆路径功能(VRP功能)设计疏散路线,得到标有疏散时间和疏散路线的地区图形,并最终计算得到每条路线的疏散时间、疏散人数、公交车数等信息。     

Modeling emergency evacuation with time and resource constraints: A case study from Gujarat

This study develops an off-site emergency response plan for a nuclear power plant in Gujarat, India subject to time constraints with resource limitations and risk of radiation exposure to victims. We formulate an optimization model to capture the effect of delay in evacuation, limited resource availability, and costs associated with resource allocation. A single chain closed queuing network model with class switching is used to model traffic congestion during evacuation. The throughput measures from the queuing network are used as inputs in the optimization model. Further, two resource allocation strategies are suggested and genetic algorithm is used for optimizing resource utilization and evacuation risk. The results indicate that pooling resources among a cluster of affected areas is most suitable for evacuation. Numerical experiments are conducted to analyze the time trade-offs and the effect of service time variability on the expected evacuation time. The proposed model can serve as an important resource planning and allocation tool for emergency evacuation.     

Evacuation based on spatio-temporal resilience with variable traffic demand

The efficient evacuation of people from dangerous areas is a key objective of emergency management. However, many emergencies give little to no advanced warning, leading to spontaneous evacuation with no time for planning or management. For large emergencies, destinations become less certain, with traffic demand imbalanced and concentrated on a few oversaturated routes familiar to evacuees. Ultimately, this leads to rapid congestion and delay on some routes, while others remain barely used, extending clearance times with an accumulating population at risk. In this study we address these issues through incorporating spatio-temporal traffic resilience dynamics into a destination choice model utilizing the available capacity of the overall network. We validate our model through a post-concert egress event. The results suggest that our method can reduce total egress times and average travel time by 20%–43% over the no-guidance condition. Our method can be used to estimate and quantify emergency conditions to optimally guide destinations and routing choice for evacuees and/or autonomously moving vehicles during evacuations.     

基于SOA的应急疏散仿真平台体系框架设计

对于应急疏散仿真平台,一方面,目前不存在通用的体系结构,开发平台时费时费力,而且构建的平台可重用性差;另一方面,现有的应急疏散仿真使用的数据基本上是历史调查的数据,根本反映不了突发事件的不确定性、动态性,很难实现复杂情景下的有效疏散。因此,文章提出基于SOA的实时应急疏散仿真平台体系结构。在广西气象灾害应急决策平台关键技术研究项目中的应用,证明该框架的有效性和实用性。     

Passenger emergency evacuation from bus carriage: Results from realistic data and modeling simulations

One of critical challenges in the development of pedestrian models is the lack of relevant data under emergency conditions. In this paper, we reveal several behavior characteristics of passenger emergency evacuation from a burning bus carriage, by observing and analyzing a video recording, attempting to close the gap between practical observation and theoretical modeling. The analysis results show that there are considerable differences between real emergency evacuation and experimental normal evacuation with respect to the cumulative flows, flows, and time gap distributions. Additionally, the behavior of falling-down, which contributes to the formation of movable obstacles, interrupts the continuity of the evacuation process. Then, we incorporate these behavioral characteristics into a microscopic pedestrian model with a fine lattice space representation. The simulation results indicate that the escape is slowed down by the falling behavior, as well as by the luggage-carrying behavior.     

A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters

Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.     

基于遗传算法的煤炭港口泊位分配优化模型

泊位资源是煤炭港口的稀缺资源,由于场存及煤炭需求制约等原因导致其利用率不高,疏港压力增大。通过调查研究了输出型煤炭港口的服务系统特征,建立了一个周期内,以船舶在港总时间最短及装煤量最多为目标的线性规划模型,采用泊位-次序的二维编码方式,利用遗传算法对模型求解。选取河北省某煤炭港口48小时内数据进行仿真分析,结果表明模型达到了优化的目的。与传统方法相比较,船舶的平均等待时间减少了34%,总装载量提高了2.5%。     

时变路网无能力约束车辆路径优化

讨论了一类时变路网下的无能力约束车辆配送路径优化问题,建立了基于时变路网的以配送总耗时最短为优化目标的无能力约束车辆配送路径优化模型。提出了基于实时Dijkstra算法的模型求解方法。数值算例表明考虑路网时变特性得到的优化配送方案将更加符合配送实际。     

一次性消耗的应急系统资源调度问题研究

文章研究了一次性消耗的应急资源调度问题,将参与应急的出救点数目最少和应急开始时间最短同时作为优化目标,首先给出了参与应急出救点数目取值范围的判定定理,然后将问题转化为出救点数一定条件下的应急开始时间最早的单目标优化问题,从而得到一组Pareto最优解。并从理论上证明了求解方法的有效性和正确性。     

Assessing perceived driving difficulties under emergency evacuation for vulnerable population groups

The devastating impacts of natural hazards, including loss of lives and properties, underline the importance of efficient hazard preparedness, especially in the areas with frequent hazard occurrence. Several studies indicated that driving during emergency evacuation is quite challenging due to dense traffic flow, inclement weather conditions, and unexpected maneuvers of other evacuees. However, limited research has been directed towards assessing the perceived driving difficulties of individuals, including vulnerable population, under emergency evacuation. This study deploys a driving simulator in order to emulate realistic emergency evacuation scenarios and to quantify the perceived driving difficulties of individuals under emergency evacuation. Based on the data, collected using a driving simulator, a number of statistical models are proposed to determine a set of performance indicators, including the mental demand, physical demand, temporal demand, performance, effort, and frustration, experienced by individuals as a result of emergency evacuation. The statistical models also capture a variety of different driver characteristics, traffic characteristics, driving conditions, and evacuation route characteristics. The analysis results suggest that the considered performance indicators are significantly influenced with a number of factors, including age, gender, education, race, presence of chronic diseases, and self-reported driving ability. The insights from the conducted research can be applied at the hazard preparedness stage to mitigate the perceived driving difficulties of individuals under emergency evacuation and ensure their safety.