高速公路行车时间估计和最优路径

行车时间估计和最优路径选择是智能交通系统中的研究热点,特别是对于车辆导航系统更具有深远的意义.首先以传统的交通流理论为基础,采用间接模型和动力学模型进行行车时间估计,通过仿真实验比较了两模型的优劣,并使用实测数据分析得到的车流量信息对动力学模型进行改进.然后使用Dijkstra算法寻找出静态状态下的最优路径,再结合前面建立的时间估计模型,给出了适用于动态随机状态下的路径寻优算法,用于解决路段行车时间期望随出发时刻动态变化的问题.最后指出了交通实时信息对解决动态随机最优路线问题的重要性,并结合卡尔曼滤波算法对路段相关的情况作了进一步讨论.     

道路维修施工路段交通分流信控优化模型

针对道路维修施工期间常采用的部分路面封闭施工且利用辅路进行分流的情形,探讨了交通分流信控优化模型。借助交通流波动理论,分析了施工路段及其前后车流拥挤排队及疏散特征和规律,分析了对车流进行控制需满足的约束,并分析了车流的延误计算公式。以总的车辆行驶时间最小化目标,原路径及分流路径的绿时分配及信号周期为优化参数,考虑交通分流控制的各种约束,建立了道路施工路段交通分流信控优化模型。分析了该模型属于非凸问题,因此提出了一种近似求解最优解的办法。通过一个示例对模型和求解算法进行了验证,并对一些规律性结果进行了分析。     

弹性需求下路段相互影响的交通网络均衡模型

本文利用网络用户均衡原理,对弹性需求下路段相互影响的交通配流问题进行研究,给了弹性需求下路段相互影响的网络均衡条件,建立了与均衡条件等价的变分不等式模型,论证了模型解的存在性和唯一性.     

路段关联影响下出行路径选择的一个非线性互补模型

在网络供需都不确定的情况下,提出一个考虑路段关联的路径选择模型.假定网络中每一用户的路径选择效用受到三个因素的影响:出行时间均值、出行成本均值和以出行时间方差表现的出行时间可靠性.在出行路径选择效用最大化前提下,修改了出行阻抗单调可分的常见假设,考虑出行阻抗非单调且不可分的情形,建立了模型的均衡公式和约束条件.探讨了模型解的存在性和唯一性特征,并将之表示为一个非线性互补问题,研究非线性互补问题的解得到结论:其唯一性不保证.最后用一个小型网络进行测试来表现模型的特征,测试结果表明出行阻抗的非单调、不可分性质通过影响出行时间方差来影响网络均衡流分配,造成均衡路段和路径阻抗的增加.     

高速公路网络上行车时间估计和最优路线的选取

首先建立交通流动力学模型求解问题Ⅰ.在不考虑流量和考虑流量的两种情况下,该模型都能够解出在任意给定的时刻t位于第一个传感器的车辆到达第5个感应器的行车时间.我们还从四个方面给出了判断交通堵塞的衡量标准,并且利用神经网络方法准确地对未来的车流状态进行了预测.问题Ⅱ建立了交通网络的加权有向图模型,引入协方差矩阵描述网络中道路之间的相关性,并设计了查找最优路径的动态Dijkstra算法.问题Ⅲ构建了统计多目标规划模型,利用车比雪夫不等式,成功找到了从端点3到14和14到3的最优路径,并估算出了对应的行车时间.     

基于道路堵塞及顾客时间要求的快件配送最优路径选择

针对道路堵塞如节假日导致的临时最短配送路径失效的问题,提出配送网络最优路径选择模型,并设计了求解快递配送网络关键边和最优路径的算法。首先,计算出整个网络的关键边,掌握配送网络特征;其次,考虑顾客时间要求,研究不完全信息(中断无法提前预知,只有到达中断边的起点处才可知)下的最优路径,根据最短路径上各边新的特点,计算出每条边中断后对应的一组备用路径,再选择运输时间小于或等于顾客可等待时间的路径为有效路径,考虑道路堵塞情况,从有效路径中选择最优路径;最后,结合配送网络的实际情况对最优路径进行了算例分析。     

基于动态服务网络的铁路紧急输送计划编制模型和算法

根据特殊条件下铁路输送计划问题的动态性、多目标性、时效性等特点,采用时空网络构建铁路输送计划网络模型.并建立了基于动态路径的铁路输送计划编制数学模型.模型属于大规模的整数规划,以追求时间效益最大化和灾害损失最小化为目标.根据模型的特点,提出了松弛求解算法,借助LINGO求解工具求解松弛模型,通过逐步固定变量为整数值求得最优解.算例研究表明,算法可行有效.     

基于动态服务网络的铁路紧急输送计划编制模型和算法北大核心

根据特殊条件下铁路输送计划问题的动态性、多目标性、时效性等特点,采用时空网络构建铁路输送计划网络模型.并建立了基于动态路径的铁路输送计划编制数学模型.模型属于大规模的整数规划,以追求时间效益最大化和灾害损失最小化为目标.根据模型的特点,提出了松弛求解算法,借助LINGO求解工具求解松弛模型,通过逐步固定变量为整数值求得最优解.算例研究表明,算法可行有效.     

改进蚁群算法在智能交通中的应用

为了诱导车辆在出行时选择较高质量的路线,提出并建立了城市道路权值仿真模型.为求解该模型,从分析基本蚁群算法入手,通过在状态转移规则中加入扰动因子,改进全局更新规则,以及引入信息素更新算子改进了蚁群算法.然后利用道路权值模型对两种算法在路径寻优效果上做了比较和分析,实验结果表明改进后的蚁群算法能有效地避免停留在局部最优解,并提高计算效率,具有良好的寻优性和收敛性,能准确找出路网中满足综合要求的最优路径.     

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.     

Optimal investment in setup reduction in manufacturing systems with WIP inventories

A number of models have been proposed to predict optimal setup times, or optimal investment in setup reduction, in manufacturing cells. These have been based on the economic order quantity (EOQ) or economic production quantity (EPQ) model formulation, and have a common limitation in that they neglect work-in-process (WIP) inventories, which can be substantial in manufacturing systems. In this paper a new model is developed that predicts optimal production batch sizes and investments in setup reduction. This model is based on queuing theory, which permits it to estimate WIP levels as a function of the decisions variables, batch size and setup time. Optimal values for batch size and setup time are found analytically, even though the total cost model was shown to be strictly non-convex.     

Solving TSP by considering processing time: Meta-heuristics and fuzzy approaches

In this paper, general properties of traveling salesman problem has been illustrated, then a model has been introduced to minimize Make-span (time interval which all of jobs will be done) with considering sequence-dependence setup times and processing time. Furthermore, fuzzy sets and its characteristics are applied to a Hard-solvable traveling salesman problem with considering processing times. As it can be seen, traveling salesman problems are NP-hard, so solving problem in huge dimensions is uncontrollably manageable and in other side these kinds of problems in real-world are unavoidable, so a local search can prove their importance. (However this Meta-heuristic methods may not reveal exact optimal solution, but they yield a near-exact optimal solution in undeniable reduced computational time). Here, some of most famous local searches have been explained in their common and normal form, which are Genetic Algorithm (GA), Tabu Search (TS), Simulated Annealing (SA), Ant Colony System (ACO). In rest, a comprehensive comparison through these methods has been shown. This normality in methods structure could help the article to hold no-side-taken and acceptable judgments. In final, point methods analysis and parameter tuning are held.     

最佳灰色回归组合模型及其在中国火灾预测中的应用

火灾每年给国家和人民生命财产造成巨大损失.火灾现象具有随机性、模糊性,是个复杂的灰色系统行为.研究火灾发生规律及发展趋势,具有实用价值.为此,首先给出最小二乘估计(LSE)意义下的最佳组合预测模型的定义,并求得组合模型的权的公式和证明权的唯一性.其次,用回归分析方法建立多个回归模型,并按以下三条标准:①回归指数(或相关系数)r大、②系统误差s小、③模型精度p高,选定最佳非线性回归模型;用灰色理论建立多个灰色模型,并按以下三条标准:①后验差比值c小、②小误差概率P大、③预测关联度ξ大,选定最佳灰色模型;再用最小二乘法将最佳回归模型与最佳灰色模型有机地结合起来建立的中国火灾最佳灰色回归组合预测模型.最佳灰色回归组合预测模型综合利用前两者提供的不同的有用信息,改善了单一模型的局限性,提高了模型的预测精度,减少了预测误差,使预测效果更佳.组合模型预测中国年火灾起数处于动态增长过程.     

Some comments on preference order dynamic programming models

A simple deterministic dynamic programming model is used as a general framework for the analysis of stochastic versions of three classical optimization problems: knapsack, traveling salesperson, and assembly line balancing problems. It is shown that this model can provide an alternative to the preference order models proposed for these problems. Counterexample to the optimality of the preference order models are presented.     

基于Copula-ECM-GARCH模型的农产品期货套期保值绩效研究

Copula函数具有可以准确刻画变量间的相依结构、精准描述金融时间序列"尖峰厚尾"分布特点的良好统计性质.针对传统计量模型在计算套期保值比率时存在的局限性,利用Copula函数描述变量的尾部相关性,并结合ECM-GARCH模型,对大豆、小麦、玉米三种国内农产品期货进行套期保值研究,分别计算最优的套期保值比率及其绩效,并与OLS、B-VAR、ECM和ECM-GARCH模型进行比较.结果表明,对于大豆来说,运用Copula-ECM-GARCH模型计算得到的套期保值比率进行对冲操作,可以最大化降低现货市场的价格风险,为投资者提供了一种可以更好规避价格风险的工具选择.     

A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.     

NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

In this paper, we propose a testing-coverage software reliability model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP). Software is usually tested in a given control environment, but it may be used in different operating environments by different users, which are unknown to the developers. Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures, but most of the underlying common assumptions of these models are that the operating environment is the same as the developing environment. But in fact, due to the unpredictability of the uncertainty in the operating environments for the software, environments may considerably influence the reliability and software's performance in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the theory reliability, and also from all its similar applications in other fields. In this paper, a new model is proposed with the consideration of the fault detection rate based on the testing coverage and examined to cover ID subject to the uncertainty of operating environments. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real software failure data based on seven criteria. Improved normalized criteria distance (NCD) method is also used to rank and select the best model in the context of a set of goodness-of-fit criteria taken all together. All results demonstrate that the new model can give a significant improved goodness-of-fit and predictive performance. Finally, the optimal software release time based on cost and reliability requirement and its sensitivity analysis are discussed.     

基于CAViaR的DCC模型及其对中国股市的实证研究

VaR是金融风险度量方面研究的热点.CAViaR模型可以用来直接计算单个资产的VaR,DCC模型可以用于刻画资产间的相关性.结合这两个模型,通过分位数估计方差的方法,提出了基于CAViaR的DCC模型来计算投资组合的VaR.对中国股市的实证研究表明其具有更好的效果.     

Outlier detection and robust covariance estimation using mathematical programming

The outlier detection problem and the robust covariance estimation problem are often interchangeable. Without outliers, the classical method of maximum likelihood estimation (MLE) can be used to estimate parameters of a known distribution from observational data. When outliers are present, they dominate the log likelihood function causing the MLE estimators to be pulled toward them. Many robust statistical methods have been developed to detect outliers and to produce estimators that are robust against deviation from model assumptions. However, the existing methods suffer either from computational complexity when problem size increases or from giving up desirable properties, such as affine equivariance. An alternative approach is to design a special mathematical programming model to find the optimal weights for all the observations, such that at the optimal solution, outliers are given smaller weights and can be detected. This method produces a covariance estimator that has the following properties: First, it is affine equivariant. Second, it is computationally efficient even for large problem sizes. Third, it easy to incorporate prior beliefs into the estimator by using semi-definite programming. The accuracy of this method is tested for different contamination models, including recently proposed ones. The method is not only faster than the Fast-MCD method for high dimensional data but also has reasonable accuracy for the tested cases.