基于道路扩容的道路拥挤定价收入再分配研究

道路拥挤已经成为世界各国面临的一个普遍难题,道路扩容和道路拥挤定价是缓解交通拥挤的有效手段.本文在分析道路拥挤定价收入用于道路扩容后对交通出行者效用的影响的基础上,构建了基于道路扩容的道路拥挤定价收入再分配双层规划模型,并给出求解算法和实证分析.     

O-D需求估计的双层规划模型及基于灵敏度分析的求解算法

本文在考虑三种交通模式 (一般机动车,公交车,自行车)的基础上,建立了一般拥挤网络多模式OD需求估计问题的双层规划模型,给出了一个基于灵敏度分析的启发式求解算法,并进行了数值试验。试验结果表明,本文所给的模型和算法不仅能较好地给出OD需求的估计值,而且同时还给出了各模式路段流量的估计值。     

基于系统最优的城市公交专用道网络设计模型及算法

设置公交专用道是实现"公交优先"的重要手段,然而,专用道设置将改变不同交通方式的道路通行能力,进而影响交通网络的整体性能。本文试图提出一种基于系统最优思想的公交专用道网络设计方法,既保证出行者的利益,又能满足交通系统总费用最优的目标。首先,本文分析了公交专用道设置对公交车辆和社会车辆这两种交通方式道路通行能力的影响,基于经典的BPR函数,构造了考虑专用道设置的不同方式的路段阻抗函数。其次,分析了出行者在多方式交通网络中的模式选择和路径选择问题,采用用户平衡理论分析了城市多方式交通平衡配流问题,给出了相应的变分不等式模型。更进一步,采用双层规划方法构造了基于系统最优的城市公交专用道网络设计模型,该模型以交通网络总费用最小为优化目标,并考虑了不同交通方式的平衡流量约束,采用分支定界算法对该双层规划模型进行求解。最后,通过一个简单算例对模型及算法的可行性和有效性进行了分析和验证。     

公交系统随机平衡网络设计模型及求解算法

本文根据公交网络的具体特点,对公交网络进行了系统的描述,提出一个双层规划模型来描述随机平衡公交网络设计问题。在双层规划模型中,上层模型为一个标准的公交网络设计模型,下层模型是一个公交网络随机平衡配流模型。针对所提出的模型,设计了基于灵敏度分析的求解算法。最后,给出一个简单算例加以证明。     

带公平限制的多时段次优拥挤定价模型

研究在公平限制条件下城市道路系统多时段一般路网的次优拥挤定价问题,建立公平限制条件下城市道路系统多时段一般路网的次优拥挤定价的双层规划模型,设计了模拟退火算法并利用模拟计算方法对模型进行了典型路网的计算及结论分析.     

交通分布模型的应用

交通量分布在道路网络规划中是非常重要的环节之一,目前解决交通量分市模型很多,但真正用于道路运输系统网络规划不多,本文通过对国内通常采用的增长系数法、Fratar法和重力模型法的论述,谈谈交通分布模型的应用。     

交通拥堵问题探究——对公共品问题的反思

道路使用需求的不断增长导致了这一资源的稀缺性以及道路使用负外部性的增加,因此造成了严重的交通拥挤现象。从供给角度解决公路过度使用问题不是根治拥挤的良策,应着重从需求角度出发来解决交通拥挤问题。     

城市道路交通网络容量的建模

交通网络承载着具有意志行为的个体组成的交通流,交通网络容量表示道路网络的交通承载能力,与交通网络的OD结构密切相关.本文在分析城市道路交通网络OD结构特征的基础上,提出表达道路网络容量的基本思想和模型,阐明交通网络容量受到交通流特性、网络要素容量、废气排放量、服务水平和系统效率等因素的影响,基于不同的考虑方法可以建立不同的网络容量模型.基于路段容量约束的网络容量描述了道路系统承载交通流的物理极限;基于路段容量约束和交通环境约束的网络容量则描述了道路系统承载交通流的环境极限;基于服务水平的网络容量描述了系统提供某种服务水平条件下的最大交通承载能力;基于效率的网络容量描述了网络资源使用效率和用户效益同时最大化条件下的道路网络承载能力.文章深入分析了所提出的各种模型之间的关系,在实际应用中可根据交通规划和管理所追求的具体目标,采取相应的模型.     

政策博弈下的道路交通拥挤定价

从经济学看,城市交通兼具内部性和外部性;对拥挤道路收取使用费,使外部性内部化,是优化交通的重要经济手段之一.基于博弈论和 Arnott 瓶颈模型,给出解析模型,探讨政府采用拥挤收费政策参与博弈的情形下,出行者行为以及道路总成本的改变.研究结果表明,作为外部性手段,设置恰当的收费费率,政府可以实现缓解交通拥挤和实现优化交通的管理目标.算例结果支持了模型分析的结论.     

考虑交叉口影响的逆向路段选择优化

逆向车道作为提高路网整体通行能力的一种交通组织策略，已在疏散交通组织中得到了大量应用。以往关于逆向车道设置路段选择的研究，大多没有考虑交叉口影响。在拥挤的城市道路网中，这种忽略会导致过高的预期疏散通行能力估计。本文考虑交叉口影响，建立改进的最大流及其关键边模型，对疏散路网中逆向路段的选择进行优化。将交叉口分转向的通行能力表示为节点的方向性权重，将疏散路网抽象为方向性点权网络。定义该类网络中的最大流增流关键边，即一旦扩容会使网络最大流流值增加幅度最大的边。通过在方向性点权网络中寻找最大流增流关键边，得到考虑交叉口影响时疏散路网中对应的逆向车道设置路段。对经典最大流问题求解算法进行相应的改进，给出方向性点权网络中寻找最大流增流关键边的有效算法，并通过一个数值算例进行测试和仿真分析。结果表明，在考虑交叉口影响的情况下，得到的逆向车道设置路段更为合理，疏散时对其进行扩容能更有效地压缩总疏散时间。     

社区应急疏散动态协作调度优化研究

针对灾民数量和路网通行时间的动态性以及灾民疏散反应系数的随机性，本文提出了社区应急疏散协作调度优化流程，并以疏散灾民数量最大化和疏散成本最小化为目标，构建了社区应急疏散多种运输方式协作调度优化模型，并给出了求解该模型的改进多目标遗传算法。然后，论文使用Tansmodeler模拟社区应急疏散协作调度优化过程，加载疏散灾民动态需求和历史出行时间表，并对模型和算法进行验证。结果表明，该模型和算法可以在有效刻画疏散灾民数量和路网通行时间的基础上，为不同时刻的交通工具配置及疏散路径选取提供决策。     

A PRACTICAL APPROACH TO THE LARGE-SCALE FOREST SCHEDULING PROBLEM*

A large-scale forest cutting schedule problem involving 1166 forest units to be cut over a 24–year planning period is discussed. The problem is formulated as a generalized version of the basic transportation problem. The conversion procedure for such a problem to the standard transportation format is outlined. The proposed approach is then compared with a linear programming decomposition approach on the basis of operating results obtained and computer time required by each approach. It is shown that this new approach will solve a real world problem about 40 times faster than the usual linear programming decomposition approach.     

基于二阶段随机规划的城市医疗废弃物回收网络设计

城市医疗废弃物日益增加,且回收需求量受诸多因素的影响,难以准确预测,假定回收需求为确定值的医疗废弃物网络优化设计不能与实际需求相匹配。本文考虑了离散随机参数环境下,医疗回收网络设计中选址规划、分配计划及运输规划的协同优化问题,建立了以选址成本、运输成本最小为目标,设施与车辆能力限制为约束的二阶段随机规划模型。根据模型特点,设计了基于Benders decomposition的求解算法,同时,设计了一系列加速技术用于提高算法的求解效率。最后,以国内某城市医疗回收网络为背景设计算例,检验本文模型和求解策略的可行性和有效性。结果表明：相比确定性规划,随机规划的解能够节约总成本,结合一系列加速技术的Benders decomposition方法比CPLEX与纯的Benders decomposition更有优势。     

Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty

We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.     

Expanding the Spanish high-speed railway network

This paper presents a model for the expansion of transportation networks incorporating specific requirements about population coverage, budget constraints, intermediate goals and origin–destination flows, among others. The model is applicable to the current expansion project of the Spanish high-speed railway network that has been proposed by the Spanish Government under the program Strategic Planning of Infrastructure and Transport (see source [c]).     

Lean road transportation – a systematic method for the improvement of road transport operations

Road transportation has become an important factor in international trade and the management of supply chains. However, this form of product logistics has generally been considered inefficient. Traditionally, practical inefficiencies of road transportation have been addressed through mathematical modelling, operations research-based methods and simulation. This paper presents an alternative systematic approach to improve road transport operations based on lean thinking and the reduction of the seven transportation extended wastes. To do this, the paper reviews the extant literature in the area of lean road transportation, providing a structured research definition of the application of lean thinking in road transport operations and hence guidance on the limited research conducted in this field. The systematic lean transportation method is then presented and empirically tested through a case study in a Mexican firm. The results obtained from the case study indicate that the proposed systematic lean method is an effective alternative for the improvement of road transport operations, with the number of distribution routes and distance travelled being reduced by 27 and 32%, respectively. The proposed method can be used by organisations as a guide to help them improve their road transport operations. In addition, the paper’s aim is to contribute by stimulating scholars to further study the application of lean thinking and waste reduction in road transport operations.     

Multimodal hub location and hub network design

Through observations from real life hub networks, we introduce the multimodal hub location and hub network design problem. We approach the hub location problem from a network design perspective. In addition to the location and allocation decisions, we also study the decision on how the hub networks with different possible transportation modes must be designed. In this multimodal hub location and hub network design problem, we jointly consider transportation costs and travel times, which are studied separately in most hub location problems presented in the literature. We allow different transportation modes between hubs and different types of service time promises between origin–destination pairs while designing the hub network in the multimodal problem. We first propose a linear mixed integer programming model for this problem and then derive variants of the problem that might arise in certain applications. The models are enhanced via a set of effective valid inequalities and an efficient heuristic is developed. Computational analyses are presented on the various instances from the Turkish network and CAB data set.     

Using branch-and-price approach to solve the directed network design problem with relays

We present node-arc and arc-path formulations, and develop a branch-and-price approach for the directed network design problem with relays (DNDR). The DNDR problem can be used to model many network design problems in transportation, service, and telecommunication system, where relay points are necessary. The DNDR problem consists of introducing a subset of arcs and locating relays on a subset of nodes such that in the resulting network, the total cost (arc cost plus relay cost) is minimized, and there exists a directed path linking the origin and destination of each commodity, in which the distances between the origin and the first relay, any two consecutive relays, and the last relay and the destination do not exceed a predefined distance limit. With the node-arc formulation, we can directly solve small DNDR instances using mixed integer programming solver. With the arc-path formulation, we design a branch-and-price approach, which is a variant of branch-and-bound with bounds provided by solving linear programs using column generation at each node of the branch-and-bound tree. We design two methods to efficiently price out columns and present computational results on a set of 290 generated instances. Results demonstrate that our proposed branch-and-price approach is a computationally efficient procedure for solving the DNDR problem.