震后大型商场人群疏散演化博弈分析

地震发生后,为了减少地震带来的损失,大型商场人群需要及时进行疏散.主要对商场中疏散出口位置上的疏散行为进行研究.首先分析了地震后商场人群的疏散场景;然后从疏散心理效用角度构建了疏散行为模型,并基于演化博弈分析人群疏散行为演化情况,结果表明,自组织情况下,商场人群不能向有序疏散演化;而通过商场工作人员引导疏散,修正模型,其引导力度决定着疏散演化的结果;最后,通过仿真分析,说明商场地震人群疏散演化博弈模型的适用性.     

自然灾害区域疏散的预警优化模型研究

在可预知的灾害来临前,交通堵塞问题是影响应急疏散效率的主要因素。在灾前防御阶段,有策略的发布预警消息可以使疏散更加有秩序的进行,从而提高疏散效率。本文为区域应急疏散预案构建了应急疏散预警发布研究框架,首先建立了避难点分配模型,将其结果代入到疏散预警模型,来优化特定地区预警发布时间和类型。其中,预警模型加入了时间成分,构建成了多时段模型,并使用贪婪的启发式搜索过程求解非线性的公式。最后,通过算例分析了模型算法的应用范围及其可行性,并用模拟退火算法进行了计算,验证了本算法的有效性。本研究更改以往在同一时间通过全部渠道发布消息的方式,通过疏散预警信息的发布策略的优化,可以有效避免在区域内大规模人群同时出发所导致的交通拥堵现象,为政府制定科学的应急疏散预案提供理论和技术支持。     

一种改进的公交网络最优路径算法

通过对公交网络模型进行分析,考虑公交线路票价变化,按照出行时间最短同时保证换乘次数较少的原则,对现有解决公交网络最短路问题的算法进行改进.应用了将公交线路抽象为顶点,建立邻接矩阵的方法处理换乘问题.通过实际问题计算验证了算法的有效性.     

基于系统动力学的地下空间大规模人群疏散模型

基于系统动力学提出了建立兼顾模拟精度和模拟效率的地下空间群体疏散模型的新思路.借助专业软件Vensim进行定量模拟,在总初始人数为分别为350、400、450、500、1000、2000、4000七种工况下,模拟结果与专业模拟软件Pathfinder的模拟结果相近,且计算速度更快.该模型可再现"快即是慢"等群体疏散行为中的典型现象.通过对大规模群体疏散过程中的关键影响因素"总初始人数""恐慌状态"进行分析,发现了以下现象:1)总初始人数过多会导致系统疏散能力降低,疏散结束时间大幅滞后;2)恐慌程度过低会导致系统疏散效率长时间保持在较低水平,不利于被困人群的及时疏散.     

基于一体化的多层次公交线网优化研究

探讨一体化公交系统下多模式、多层次公交网络的优化方法,采用双层规划模型对公交线网进行一体化规划,并利用遗传算法和公交线路的规划模型确定最优线路集合.通过对交通网络的分析,确定公交网络的分布方法.最后通过实例以及方案的对比分析,获得了较好的优化结果.     

公交车高峰和平峰转换的调度研究

旨在对城市公共交通系统建立合理的数学模型,对公交车的调度进行合理规划,为城市建设提供借鉴作用.从深圳市的实际情况出发,建立了合理的公交运营调度模型,让公交的使用经济最大化、合理化.通过对大数据的合理分析,给出高峰和平峰的定义;通过对公交车的发车频率、运营所得的总收入与时间的关系的分析,给出高峰和平峰转换期的调度方案;借...     

布谷鸟搜索算法在人群疏散中的应用研究

针对阶梯教室及大型场馆应急疏散的路径优化问题.提出了一种布谷鸟搜索算法的疏散模型,通过对原有算法的分析改进,设计特殊目标函数,使之适应求解人群疏散.最后将方法应用到多障碍物、多通道、多出口、多灾害源的复杂场景实现近千人的仿真实验.仿真结果表明,方法直观地展现整个人群疏散的运动过程和在多个场景中都取得了较好地路径规划效果,并开发两个疏散工具为人群疏散演习提供了平台和疏散方案.     

城市公交查询系统的设计与实现

针对含有“公汽、地铁、步行”的复杂公交网络环境,首先对公交问题所提供的数据进行分析,并优化数据的存储结构;其次充分考虑到公交网络客流分配的主要因素一换乘次数、票价、时间,提出了公交网中这三个目标的加权平均最优路径模型及其算法;最后对模型的算法用Matlab软件实现.通过测试,结果显示本系统能快速响应出满足乘客不同需求的公交出行路径。     

考虑路线复杂度的应急疏散双目标路径选择模型

针对路径选择这一疏散计划中最基本的问题，考虑疏散时间以及路线复杂度因素，建立了应急疏散路径选择的双目标优化模型。模型将总疏散时间最短以及路线复杂度最低作为优化目标，同时考虑灾害扩散对疏散网络通行状况的实时影响，将各弧段上的通行速度表示为随时间的连续递减函数。设计了求解模型的蚁群优化算法，仿真结果表明了模型和算法的有效性和可行性。     

基于COX生存分析的大型公共建筑火灾疏散模型

本文根据火灾动力学和建筑物疏散通道的网络结构,以逃生时间为因变量,影响疏散的各因素为自变量,建立了考虑多种安全因素的动态疏散cox风险比例模型,并以大型商场火灾为例对人的有计划疏散行为进行计算机仿真研究.     

Optimal egress time calculation and path generation for large evacuation networks

Finding the optimal clearance time and deciding the path and schedule of evacuation for large networks have traditionally been computationally intensive. In this paper, we propose a new method for finding the solution for this dynamic network flow problem with considerably lower computation time. Using a three phase solution method, we provide solutions for required clearance time for complete evacuation, minimum number of evacuation paths required for evacuation in least possible time and the starting schedules on those paths. First, a lower bound on the clearance time is calculated using minimum cost dynamic network flow model on a modified network graph representing the transportation network. Next, a solution pool of feasible paths between all O-D pairs is generated. Using the input from the first two models, a flow assignment model is developed to select the best paths from the pool and assign flow and decide schedule for evacuation with lowest clearance time possible. All the proposed models are mixed integer linear programing models and formulation is done for System Optimum (SO) scenario where the emphasis is on complete network evacuation in minimum possible clearance time without any preset priority. We demonstrate that the model can handle large size networks with low computation time. A numerical example illustrates the applicability and effectiveness of the proposed approach for evacuation.     

Linear,non-approximated models for optimal routing in hazardous environments

A linear optimisation model is presented for the routing of vehicles through hazardous environments or for routing vehicles carrying hazardous materials. The model seeks to find the route with the minimum cost and the minimum probability of accident. As opposed to previously published models, the formulation is linear and no approximations are needed, even in the case of high risk. Several forms of considering risk are presented, as well as a simple method of solution in polynomial time. An example is shown and solved.     

Decision support for hospital evacuation and emergency response

Evacuation planning is an important part of a hospital’s emergency management plan. In an evacuation the safety and health of patients is the fundamental success parameter. Thus, in this paper we introduce an evacuation model, appropriate for planning and operations, that has the objective of minimizing expected risk, both the threat risk that is forcing the evacuation, and the risk inherent in transporting patients, some in critical condition. Specifically, we study the allocation of patients, categorized by criticality and care requirements, to a limited fleet of vehicles of various capacities and medical capabilities, to be transported to appropriate receiving hospitals considering the current available space in each hospital for each category of patient. The model is an integer program, where the non-linear expected risks are calculated a-priori. This model has a structure that has excellent solution characteristics that permit us to solve large problems in a reasonable time, enabling the model to potentially be used for both planning and operations. To illustrate the solvability of this model and demonstrate its characteristics, we apply it to a realistic case study based on the evacuation of a large regional hospital.     

Scheduling tests in automotive R&D projects

In automotive R&D projects a major part of development cost is caused by tests which utilize expensive experimental vehicles. In this paper, we introduce an approach for scheduling the individual tests such that the number of required experimental vehicles is minimized. The proposed approach is based on a new type of multi-mode resource-constrained project scheduling model with minimum and maximum time lags as well as renewable and cumulative resources. We propose a MILP formulation, which is solvable for small problem instances, as well as several variants of a priority-rule based method that serve to solve large problem instances. The developed solution methods are examined in a comprehensive computational study. For a real-world problem instance it is shown that the introduced approach may enhance the current methods applied in practice.     

Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies

This study investigates the effectiveness of simultaneous and staged evacuation strategies using agent-based simulation. In the simultaneous strategy, all residents are informed to evacuate simultaneously, whereas in the staged evacuation strategy, residents in different zones are organized to evacuate in an order based on different sequences of the zones within the affected area. This study uses an agent-based technique to model traffic flows at the level of individual vehicles and investigates the collective behaviours of evacuating vehicles. We conducted simulations using a microscopic simulation system called Paramics on three types of road network structures under different population densities. The three types of road network structures include a grid road structure, a ring road structure, and a real road structure from the City of San Marcos, Texas. Default rules in Paramics were used for trip generation, destination choice, and route choice. Simulation results indicate that (1) there is no evacuation strategy that can be considered as the best strategy across different road network structures, and the performance of the strategies depends on both road network structure and population density; (2) if the population density in the affected area is high and the underlying road network structure is a grid structure, then a staged evacuation strategy that alternates non-adjacent zones in the affected area is effective in reducing the overall evacuation time.     

Multi-depot vehicle scheduling problems with time windows and waiting costs

The multi-depot vehicle scheduling problem with time windows (MDVSPTW) consists of scheduling a fleet of vehicles to cover a set of tasks at minimum cost. Each task is restricted to begin within a prescribed time interval and vehicles are supplied by different depots. The problem is formulated as an integer nonlinear multi-commodity network flow model with time variables and is solved using a column generation approach embedded in a branch-and-bound framework. This paper breaks new ground by considering costs on exact waiting times between two consecutive tasks instead of minimal waiting times. This new and more realistic cost structure gives rise to a nonlinear objective function in the model. Optimal and heuristic versions of the algorithm have been extensively tested on randomly generated urban bus scheduling problem (UBSP) and freight transport scheduling problem (FTSP). The results show that such a general solution methodology outperforms specialized algorithms when minimal waiting costs are used, and can efficiently treat the case with exact waiting costs.     

Optimization of air vehicles operations using mixed-integer linear programming

A scenario where multiple air vehicles are required to prosecute geographically dispersed targets is considered. Furthermore, multiple tasks are to be successively performed on each target, that is, the targets must be classified, attacked, and verified as destroyed. The optimal, for example, minimum time, performance of these tasks requires cooperation among the vehicles such that critical timing constraints are satisfied, that is, a target must be classified before it can be attacked, and an air vehicle is sent to a target area to verify its destruction only after the target has been attacked. In this paper, the optimal task assignment/scheduling problem is posed as a mixed-integer linear program (MILP). The solution of the MILP assigns all tasks to the vehicles and performs the scheduling in an optimal manner, including staged departure times. Coupled tasks involving timing and task order constraints are automatically addressed. When the air vehicles have sufficient endurance, the existence of a solution is guaranteed.     

带时间窗的三维装载物流配送优化方法研究

针对城市物流配送优化研究在客户服务时间窗和货物装载方式合理结合方面存在的不足,考虑物流配送车厢货物装载方式与客户访问序列相关的特征对车厢空间进行合理的区域划分。首先,构建了包含配送中心的固定成本、配送车辆的运输成本、维修成本、租赁成本和违反时间窗惩罚成本的物流运营成本最小化和配送车辆空间利用率最大化的双目标优化模型;然后,提出一种结合遗传算法(GA)全局搜索能力和禁忌搜索算法(TS)局部搜索能力的GA-TS混合算法求解模型;最后,结合重庆市某配送中心的三维装载物流配送实例数据进行了优化计算,实验结果给出了带时间窗的三维装载物流配送路径优化方案,并进行了不同车厢空间分区模式下平均装载率、物流运营成本和车辆使用数的比较分析。研究表明,当客户需求货物种类数与车辆的空间区域划分数相等且按货物类型进行区域划分时,物流运营成本最小,配送车辆使用数最少和车辆平均装载率最高。     

校车站点及线路的优化设计

以高校新校区教师校车站点及线路安排为对象,首先针对乘车站点建立了双目标非线性规划模型,其中目标函数包括乘客到达站点的距离偏差最小与所有乘客到达站点的总的距离最小两个方面;站点确定后针对车辆数最少、车辆行驶的总距离最短、各辆车的运行距离均衡及各辆车的负荷均衡这4个目标建立针对线路优化的多目标非线性规划模型,并给出了解决这类问题的启发式优化算法.与目前国内外研究相比较,该模型与算法更实际,更具体的给出了问题的解答.     

Efficient contraflow algorithms for quickest evacuation planning

The optimization models and algorithms with their implementations on flow over time problems have been an emerging field of research because of largely increasing human-created and natural disasters worldwide. For an optimal use of transportation network to shift affected people and normalize the disastrous situation as quickly and Efficiently as possible, contraflow configuration is one of the highly applicable operations research (OR) models. It increases the outbound road capacities by reversing the direction of arcs towards the safe destinations that not only minimize the congestion and increase the flow but also decrease the evacuation time significantly. In this paper, we sketch the state of quickest flow solutions and solve the quickest contraflow problem with constant transit times on arcs proving that the problem can be solved in strongly polynomial time O(nm²(log n)²), where n and m are number of nodes and number of arcs, respectively in the network. This contraflow solution has the same computational time bound as that of the best min-cost flow solution. Moreover, we also introduce the contraflow approach with load dependent transit times on arcs and present an Efficient algorithm to solve the quickest contraflow problem approximately. Supporting the claim, our computational experiments on Kathmandu road network and on randomly generated instances perform very well matching the theoretical results. For sufficiently large number of evacuees, about double flow can be shifted with the same evacuation time and about half time is sufficient to push the given flow value with contraflow reconfiguration.