城市公共交通时间差别定价模型

为解决城市公共交通出行时间需求不均衡的问题,构建了时间差别费率定量计算与复合城市交通网络流量分配双层规划模型。上层为社会福利最优模型,下层通过非必要性出行系数描述多方式分时段复合城市交通网络随机弹性需求的公交时间差别定价模型。运用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,运用时间差别定价方案比高峰/平峰同一费率定价方案社会福利目标函数高37．1,乘客对平峰时段降低票价的弹性高于对高峰时段增加票价的弹性,实行高峰增加票价同时平峰降低票价的方案效果最为明显。     

打车软件混合随机均衡模型时间可靠性研究*

为了探究打车软件出现后对乘客打车行为的影响,揭示是否使用打车软件对乘客候车时间长短的变化规律,基于路网混合随机均衡模型理论,在考虑打车软件对出租车司机搜索行为影响和随机变化的OD对需求条件下,构建基于弹性需求的打车软件随机用户均衡模型;然后结合乘客等车、司机空驶时间和可靠性计算模型,引入连续平均（MSA）算法和随机路径流量分配（Dial）算法进行求解;最后通过算例验证了所建模型和算法的有效性。研究结果表明不同需求弹性下、是否使用打车软件对会乘客候车时间及候车时间可靠性产生明显的差异。     

基于公交刷卡数据的城市通勤时空分析

目前大部分公交卡仅用于收费,刷卡记录中不包含乘客的出行站点信息,无法研究乘客的空间出行特征.为了获取乘客的上车站点,将公交刷卡数据和GPS定位数据相匹配,增加到离站弹性阈值,改进传统的上车站点推导算法;通过对公交刷卡行为进行分析,将乘客个体融入到公交乘客整体中,提出下车站点推导算法;在此基础上提出出行时间链的概念,结合PTD模型来识别通勤乘客.以青岛市西海岸新区的公交大数据为例验证了该算法的可行性,并对其进行了通勤时空分析.     

柔性能力需求计划算法研究与设计

能力需求计划是企业资源规划中的一个重要的子系统,为实现企业的生产任务提供能力上的保证,是连接计划和生产的桥梁.分析了企业资源规划中现有的能力需求计划算法,发现了在柔性方面现有的能力需求计划算法存在着问题,并且对存在的问题进行了详细的研究,解决了算法中的多工艺路线问题,并针对计算过程中提前期不足的问题提出了提前期不足计划,这些方法使得能力需求计划能够适应我国大多数企业的需求,具有较好的柔性.     

带时间窗和服务顺序约束的多需求车辆路径问题

研究顾客具有多种需求,分别需要由不同类型车辆提供服务,且同一顾客多种需求的服务时间具有固定先后顺序的车辆路径问题.在考虑各个顾客需求的服务时间窗,同一顾客不同需求的服务顺序、服务时间间隔,以及各种车型的车容量及最大行驶时间等约束的前提下,以满足顾客多种需求的车辆运行成本和等待成本之和极小化为目标,建立混合整数规划模型.进一步设计求解模型的联合优化遗传算法,并利用车辆路径问题的标准测试集构造具有两种需求的测试算例,分别利用所提出的联合优化遗传算法与文献中的两阶段遗传算法进行模拟计算与分析,验证所提出算法的有效性.将经典的单一需求车辆路径问题推广到多种需求的情形,建立带约束的多需求车辆路径问题的数学模型并设计求解模型的有效算法,为解决实际问题提供了决策依据.     

分布式数据库数据副本最优安置问题的研究

对树形网络上的数据副本最优安置问题,在已有K子树中心优化模型的基础上提出了K节点中心的改进模型.改进模型相对于原有模型优化了分布式数据库更新操作的执行代价.给出了两个动态规划算法来求解树形网络K节点中心问题,一个是非常简单的复杂度较高的动态规划,另一个是使用分治的较复杂的高效动态规划,最后通过实验验证了模型的优化作用.     

基于换乘次数最少的公交网络最优路径模型研究

结合乘客出行心理分析,提出以换乘次数最少为目标的公交乘车模型.在公交网络建模方面,综合考虑公交站点空间关系,提出空间数据到拓扑模型再到搜索模型的公交网络双层建模方案.通过搜索模型的建立,将最小换乘次数问题转化为两点间的最短路径问题进行求解.在搜索算法的设计上,首先提出改造的边权为1的Dijktra算法,以此为基础设计前驱节点算法.并以前驱节点算法为前提,设计所有最短路径算法,能够高效地求解两点间的所有换乘次数最小的乘车方案.最后,以大连市公交数据为例,验证了建模方案和算法的可行性.     

进化规划算法的时间复杂度分析

进化规划算法是求解连续优化问题的一类进化算法,是进化计算的一个重要分支.在进化规划算法的理论研究上,已有学者证明了其收敛性.然而,进化规划算法的时间复杂度分析是进化计算领域一大难题,目前相关的研究成果很少.基于吸收态Markov过程模型,以期望收敛时间作为研究进化规划算法时间复杂度的指标,提出了进化规划算法期望收敛时间的估算方法,并以此作为算法时间复杂度分析的理论依据.最后分析了Gauss变异进化规划算法的期望收敛时间,作为提出理论的应用举例.     

共享汽车系统空车调度的鲁棒优化模型

面向共享汽车系统的运营商与潜在用户,针对实现最大利润的空车调度问题,同时考虑乘客需求信息的不确定性对调度过程的影响,利用基于可调决策规则的鲁棒优化方法进行建模与求解.在共享汽车系统中,乘客的出行需求是不确定的,给出相应的不确定集合描述,将乘客的出行需求限制在一定的区间内,并灵活限制时间上的乘客需求之和,以减小模型的保守性.在此基础上引入可调决策规则,使得空车调度的策略可以根据已实现的需求进行调整,提出空车调度的鲁棒优化模型及其可解的线性规划形式.仿真实验利用真实的滴滴订单信息模拟用户使用共享汽车出行的需求,展示该模型所提出的空车调度策略(相较于确定性模型)会投入更多的费用在空车调度上,使运营商在平均意义和最差情况下均获得更大的利润并满足更多的乘客需求,表明所提出模型的鲁棒性和实用性.     

基于禁忌算法的快速公交网络系统仿真优化

针对传统数学规划方法处理BRT网络系统优化存在的局限性,提出了一类直达乘客量最大化的BRT网络规划问题并建立了数学模型。设计了该优化模型的禁忌算法。多次仿真实验结果表明模型合理,算法有效。     

The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network

This paper proposes a network-based model for investigating the optimal transit fare structure under monopoly and oligopoly market regimes with uncertainty in the network. The proposed model treats the interaction between transit operators and transit passengers in the market as a two-level hierarchical problem with the transit operator sub-model at the upper-level and the transit passenger sub-model at the lower-level. The upper-level problem is to determine the fare structure so as to optimize the objective function of the transit operators, whereas the lower-level problem represents the path choice equilibrium of the transit passengers. In order to consider the uncertainty effects on transit network, the proposed model incorporates the unreliability component of transit services into the passenger disutility function, which is mainly due to variations of the in-vehicle travel time and the dwelling time of transit vehicles at stops. With the use of the proposed model, a numerical example is given to assess the impacts of the market regimes and the unreliability of the transit services on the optimal transit fare structure.     

Optimization of Number of Operators and Allocation of New Lines in an Oligopolistic Transit Market

This paper proposes a novel model for determining the optimal number of transit operators and the allocation of new lines in an oligopolistic transit market. The proposed model consists of three interrelated sub-models that are associated with three types of players; namely, transit authority, transit operators, and transit passengers. In practice, the operating cost per unit of transit line of each operator is decreasing in the number of lines that it operates. These effects which are referred to as the scale economies of transit operations are explicitly incorporated in the proposed model. On the basis of a logit-type transit passenger travel choice sub-model with elastic demand, the fares and frequencies of transit services are determined by an oligopolistic competitive equilibrium model (i.e. transit operator sub-model). The transit authority sub-model for optimization of the number of operators and the allocation of new lines is expressed as a 0–1 integer programming problem. It can be solved by an implicit enumeration heuristic solution algorithm. Numerical results show that both the scale economies and the market demand level have significant impacts on the optimal number of operators and the allocation schemes of new lines. Ignoring the effects of scale economies on transit operations may lead transit authorities to make biased decisions.     

A local search algorithm for finding optimal transit routes configuration with elastic demand

This paper proposes a new local search algorithm for finding the optimal configuration of subroutes from a set of candidate transit routes in a transportation network. It is intended to maximize the transit ridership while holding the budget constraint. In each iteration of the algorithm, route segments that are likely to absorb more transit passengers are added to the configuration and less‐contributing segments are removed, instead. A path‐based model with elastic demand is applied for traffic assignment problem. The algorithm takes advantage of the equilibrium paths information to speed up the calculations for emerging configurations. A numerical experiment on Sioux‐Falls network indicates that the proposed algorithm can achieve high‐quality solutions at different levels of budget. Also, the run‐time and performance of the algorithm are reported over a large problem instance of the Chicago sketch network with 55 artificial candidate routes.     

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。     

基于微粒群算法的城市公交线网模型研究

微粒群算法是求解组合优化问题的一种新的群体智能进化算法,从城市公交乘客选择出行路径的决策因素出发,以微粒群算法进化机理为核心,结合微粒群进化算法中的局部搜索与全局搜索同时进行的优点和运筹学旅行商组合优化理论,系统地建立了规划城市智能交通公交线网最短路径的数学模型进化算法,并通过MATLAB 7.0进行了实例仿真,得到了城市公交线网出行选择模型中总运输里程权重最短的优化目标。仿真结果也表明,该进化算法模型是解决城市公交线网规划的有效方法。     

Solving a type of biobjective bilevel programming problem using NSGA-II

This paper considers a type of biobjective bilevel programming problem, which is derived from a single objective bilevel programming problem via lifting the objective function at the lower level up to the upper level. The efficient solutions to such a model can be considered as candidates for the after optimization bargaining between the decision-makers at both levels who retain the original bilevel decision-making structure. We use a popular multiobjective evolutionary algorithm, NSGA-II, to solve this type of problem. The algorithm is tested on some small-dimensional benchmark problems from the literature. Computational results show that the NSGA-II algorithm is capable of solving the problems efficiently and effectively. Hence, it provides a promising visualization tool to help the decision-makers find the best trade-off in bargaining.     

城市轨道交通网络路径生成方法

结合城市轨道交通网络的结构特点,构建了城市轨道交通路网模型,以及满足路径费用计算要求的路网实体数据结构;制定了路径表达二项组以及符合乘客路径选择行为的有效路径判定条件;设计了基于路网模型的广度优先遍历搜索算法以得到全部有效路径,通过比较路径费用舍弃高耗路径而利用低耗路径。通过北京市轨道交通网络的数据对模型和算法进行验证。     

考虑区间约束的物流网络双层规划模型及算法

考虑物流网络需求的不确定性,利用区间参数度量不确定性变量与参数,建立区间需求模式下的物流网络双层规划模型,设计了一种含区间参数与变量的递阶优化遗传算法,通过定义问题求解的风险系数与最大决策偏差,给出适合物流网络结构的区间运算准则,实现模型的确定性转化。以区间松弛变量与0-1决策变量定义初始种群,通过两阶遗传操作运算,求解不同情景下双层规划目标的区间最优解与节点决策方案。算例测试表明算法求解的可操作性更强,求解结果具有区间最优解与情景决策的优越性。     

Models and a relaxation algorithm for continuous network design problem with a tradable credit scheme and equity constraints

The sustainable problems of transportation have become noticeable in the majority of cities worldwide. Many researchers are devoted themselves into traffic congestion. Generally, traffic congestion could be alleviated via increasing road capacity (supply) or reducing traffic (demand). In this paper, we model CNDP which has a tradable credit scheme and equity constraints in order to research on the way of releasing congestion by combining increasing supply and reducing demand. Firstly, the bilevel programming problem is proposed to model the CNDP with a tradable credit scheme. The upper level (the government) chooses optimal capacity enhancement for some existing links to minimize the total system costs under a budget constraint. The lower level chooses the optimal route based on considering the generalized travel cost in which both travel time and credit charging for using the link are involved. And then, considering the inequity problem in terms of equilibrium O–D travel cost and link travel time, the model is proposed by incorporating equity constraints into CNDP with a tradable credit scheme. After presenting a relaxation algorithm, the experiments on Sioux Falls network are illustrated. Finally, conclusion and some future research directions are presented.     

AFC系统中票/卡密钥安全分发方案的应用研究

在城市轨道交通自动售检票系统中,非接触式智能卡已普遍被用作车票介质.为保障乘客票/卡的有序、安全使用,对票/卡的功能密钥和应用密钥进行严格地管理显得尤为重要.基于公共密钥基础设施框架,研究和设计了轨道交通自动售检票系统中票/卡功能密钥的安全分发技术方案,包括加密传输、传输节点认证和票/卡应用密钥自动更新等关键方法及步骤.