无预警紧急疏散中公交车辆路径的确定方法

针对无预警式紧急疏散中公交救援车辆的最佳路径确定问题,提出了一个非线性混合整数规划模型.模型不仅考虑了有接收能力限制的多避难所系统,还对如何处理具有不同载客上限的公交救援车进行了分析.利用添加了虚拟路段和节点的时空网络,在以加权的综合疏散时间最小为目标的同时实现了疏散伤亡最小化.通过分析实际疏散的实施过程,得到了一种产生模型可行解的有效方法.通过将时间滚动式的流量加载模式与经典遗传算法相结合,给出了新模型的实用解法.最后,通过算例验证了模型和算法的有效性.     

多方式弹性需求下公交定价双层规划模型

为描述多方式城市交通网络下公交定价与出行选择行为的相互作用与影响,将出行方式选择与路径选择涵盖于同一网络,建立了上层模型分别以企业利润最大化、乘客出行成本最小化和社会福利最大化为目标函数,下层模型为多方式弹性需求随机用户配流模型的公交定价双层规划模型。运用改进遗传算法对模型整体进行求解,下层模型采用综合对角化算法和MSA算法的组合求解算法。最后,设计了一个算例以说明模型应用。结果表明:运用双层规划模型所确定的公交票价较传统静态票价可使政府、企业及出行者三方都获得更高收益,且上层模型以社会福利最大化为目标函数能代表社会群体中多数人利益,优化效果最为理想。     

基于在险价值的物流网络规划模糊两阶段模型与精确求解方法

针对物流网络规划问题中顾客需求和运输成本的不确定性,使用在险价值量化投资风险,建立了以投资损失的在险价值最小化为目标的模糊两阶段物流网络规划模型。对于模型中不确定参数均为规则模糊数的这一类模糊两阶段规划模型,本文通过理论分析和证明将其转化为等价的确定一阶段规划模型进行求解,从而将无穷维的优化问题转化为有限维的经典优化问题,降低了计算难度且得到了模型的精确解。不同规模的数值实验证实了所提出模型及其求解方法的有效性。     

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。     

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.     

基于变分不等式的城镇物流网络优化研究

为实现新型城镇化发展下的城乡物流一体化,通过分析城市物流向乡镇物流延伸、乡镇物流向城市物流渗透特点,设计工业品及农产品双向流动的城镇物流网络,在考虑物流费用和时间成本基础上,建立了多目标的网络优化模型.然后将其转化为等价的变分不等式问题,并设计欧拉算法进行求解.通过模型仿真分析物流通道的供给能力、单位配送时间及固定时间等函数,发现合理规划城镇物流网络通道、优化布局城镇物流中心、集中调配物流车辆等是提高城乡物流效率的手段,进而得出城市与农村物流资源整合及共同配送是实现城乡物流一体化的关键所在.     

上海市公交超网络拓扑特征与鲁棒性分析

针对复杂公交网络从单一线路或站点角度分析的局限以及二分图中节点异质性,基于超网络理论多层次研究公交系统结构.将公交站点视为超边并将线路视为节点构建上海市公交超网络模型,分析网络节点度与节点超度、超边度与超边超度、平均路径长度以及聚类系数等拓扑特征,基于网络效率分析其鲁棒性.研究发现上海市公交超网络超边指标呈幂律分布,节点指标呈分段指数分布,有较小平均路径长度和较大聚类系数.公交超网络在受到随机攻击时鲁棒性较强而在受到基于节点度的蓄意攻击时鲁棒性最弱.对城市交通规划与设施管理具有指导意义.     

公交枢纽内始发线路优化配置模型及其模拟退火算法

城市公交枢纽由于用地规模有限、公交线路集中,其内部始发站的设置不同于一般的公交首末站.本文提出了公交枢纽内始发站的布置方法,建立了公交线路和站台间优化配置模型,并提出运用模拟退火算法对模型进行求解.最后通过实例分析了提出的优化模型及解法的有效性.     

循环取货模式下的多车场协同运输问题研究

分析循环取货模式和协同运输问题的关联性,提出了供需节点分离下的多车场一体化协同运输路线优化问题,考虑运输价值和运输成本,引入节点-弧流量的概念,通过比较流量大小确定节点集合,构建了问题的多供应点、多需求点运输模型.考虑取货的单向性和送货的闭合性,构造了求解模型的两阶段算法,运用动态规划的递推解法确定取货最优路线,然后基于余弦定理的几何法求解出发点和返回点不相同的送货路径优化问题,最后通过算例分析,说明了模型的合理性和算法的有效性.     

模糊遗传规划方法在预测深部开采岩体移动中的应用

运用模糊(Fuzzy)系统理论,给出了地下深部开采岩体移动变形预测分析的Fuzzy模型,对岩体移动参数采用遗传规划(GP)方法进行确定,进而形成了模糊遗传规划方法.用工程实测数据对遗传规划网络进行了训练,并用测试样本对GP模型进行了测试,证明了模型的预测性能是令人满意的.通过工程实例计算分析表明,采用本文提出的模糊遗传规划方法所获结果符合工程实际.     

OD估计双层规划扩展模型

利用双层规划模型进行OD估计,建立双层规划扩展模型.考虑OD估计问题中的随机误差,基于Bayes估计和多元正态分布建立上层目标函数;考虑用户路径选择行为的随机性,基于随机用户均衡建立需求可变动的下层目标函数,同时该扩展模型能适应我国混合交通的实际,既能适用于拥挤网络、也能适用于非拥挤网络,最后通过算例证明此模型的有效性.     

Estimation of Transit Origin–Destination Matrices from Passenger Counts Using a Frequency-Based Approach

This paper investigates the transit passenger origin–destination (O–D) estimation problem in congested transit networks where updated passenger counts and outdated O–D matrices are available. The bi-level programming approach is used for the transit passenger O–D estimation problem. The upper level minimizes the sum of error measurements in passenger counts and O–D matrices, and the lower level is a new frequency-based stochastic user equilibrium (SUE) assignment model that can determine simultaneously the passenger overload delays and passenger route choices in congested transit network together with the resultant transit line frequencies. The lower-level problem can be formulated as either a logit-type or probit-type SUE transit assignment problem. A heuristic solution algorithm is developed for solving the proposed bi-level programming model which is applicable to congested transit networks. Finally, a case study on a simplified transit network connecting Kowloon urban area and the Hong Kong International Airport is provided to illustrate the applications of the proposed bi-level programming model and solution algorithm. This revised version was published online in July 2006 with corrections to the Cover Date.     

Optimization of transit route network,vehicle headways and timetables for large-scale transit networks

This paper presents a metaheuristic method for optimizing transit networks, including route network design, vehicle headway, and timetable assignment. Given information on transit demand, the street network of the transit service area, and total fleet size, the goal is to identify a transit network that minimizes a passenger cost function. Transit network optimization is a complex combinatorial problem due to huge search spaces of route network, vehicle headways, and timetables. The methodology described in this paper includes a representation of transit network variable search spaces (route network, headway, and timetable); a user cost function based on passenger random arrival times, route network, vehicle headways, and timetables; and a metaheuristic search scheme that combines simulated annealing, tabu, and greedy search methods. This methodology has been tested with problems reported in the existing literature, and applied to a large-scale realistic network optimization problem. The results show that the methodology is capable of producing improved solutions to large-scale transit network design problems in reasonable amounts of time and computing resources.     

A Model for Evaluation of Transport Policies in Multimodal Networks with Road and Parking Capacity Constraints

This paper presents a model for evaluation of transport policies in multimodal networks with road and parking capacity constraints. The proposed model simultaneously considers choices of travelers on route, parking location and mode between auto and transit. In the proposed model, it is assumed that auto drivers make a simultaneous route and parking location choice in a user equilibrium manner, and the modal split between auto and transit follows a multinomial logit formulation. A mathematical programming model with capacity constraints on road link and parking facilities is proposed that generates optimality conditions equivalent to the requirements for multimodal network equilibrium. An augmented Lagrangian dual algorithm embedded by partial linearization approach is developed to solve the proposed model. Numerical results on two example networks are presented to illustrate the proposed methodology. The results show that the service level of transit, parking charges, road link and parking capacities, and addition of a new parking location may bring significant impacts on travelers’ behavior and network performance. In addition, transport policies may result in paradoxical phenomenon.     

The Transit Route Arc-Node Service Maximization problem

This article presents a new method for determining optimal transit routes. The Transit Route Arc-Node Service Maximization model is a mathematical model that maximizes the service value of a route, rather than minimizing cost. Cost (distance) is considered as a budget constraint on the extent of the route. The mathematical formulation modifies and exploits the structure of linear programming problems designed for the traveling salesman problem. An innovative divide-and-conquer solution procedure is presented that not only makes the transit routing problem tractable, but also provides a range of high-quality alternate routes for consideration, some of which have substantially varying geometries. Variant formulations are provided for several common transit route types. The model is tested through its application to an existing street network in Richardson, TX. Optimal numeric results are obtained for several problem instances, and these results demonstrate that increased route cost is not correlated with increased service provision.     

The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.     

机场新增卫星厅对登机口影响的评估方法

为了解决航站楼客流量饱和的问题,采用一种增加卫星厅的方法,实现了旅客分流.基于单目标整数线性规划和多目标优化的方法,分别构建了登机口优化分配网络模型和多目标优化模型.利用登机口优化分配网络算法筛选出所使用的共同登机口,建立了目标函数并列出约束条件,采用目标约束法对建立的模型进行求解.在此基础上,根据目标建模的思想,建立了可供中转旅客总体流程时间最短且使用登机口数量最小的航班-登机口分配模型.利用MATLAB计算可知,利用42个登机口即可实现303架航班的正常运转.     

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.     

Integrating link-based discrete credit charging scheme into discrete network design problem

In this paper, we present an optimization model for integrating link-based discrete credit charging scheme into the discrete network design problem, to improve the transport performance from the perspectives of both transport network planning and travel demand management. The proposed model is a mixed-integer nonlinear bilevel programming problem, which includes an upper level problem for the transport authority and a lower level problem for the network users. The lower level sub-model is the traffic network user equilibrium (UE) formulation for a given network design strategy determined by the upper level problem. The network user at the lower level tries to minimize his/her own generalized travel cost (including both the travel time and the value of the credit charged for using the link) by choosing his/her route. While the transport authority at the upper level tries to find the optimal number of lanes and credit charging level with their locations to minimize the total system travel time (or maximize the transportation system performance). A genetic algorithm is used to solve the proposed mixed-integer nonlinear bilevel programming problem. Numerical experiments show the efficiency of the proposed model for traffic congestion mitigation, reveal that interaction effects across the tradable credit scheme and the discrete network design problem which amplify their individual effects. Moreover, the integrated model can achieve better performance than the sequential decision problems.