城市多模式公交网络出行时间可靠性模型研究

随着城市的发展,快速轨道交通和快速公交系统逐渐融入到常规公交系统中,构成了复杂的多模式公交网络,在此公交网络环境下乘客公交出行时间的稳定可靠程度直接影响到公交系统的服务性能,因此,本文以常规公交和地铁组成的典型多模式公交网络为研究对象,利用状态增广技术,建立了换乘约束条件下的出行时间可靠性模型,从而为评价和改善多模式公交网络服务性能提供决策支持。     

Route design for last-in,first-out deliveries with backhauling

The retail route design problem extends the capacitated vehicle routing problem with time windows by introducing several operational constraints, including order loading and delivery restrictions (last-in, first-out), order-dependent vehicle capacity, material handling limits at the warehouse, backhauling, and driving time bounds. In this paper, the problem is modeled on a directed network for an application associated with a major grocery chain. Because the corresponding mixed-integer program proved too difficult to solve with commercial software for real instances, we developed a greedy randomized adaptive search procedure (GRASP) augmented with tabu search to provide solutions. Testing was done using data sets provided Kroger, the largest grocery chain in the US, and benchmarked against a previously developed column generation algorithm. The results showed that cost reductions of $4887 per day or 5.58% per day on average, compared to Kroger’s corresponding solutions.     

Single track line capacity model

The objective of the research described in this paper was to develop a model for computation of an ultimate capacity of a single track line and to provide a sensitivity analysis of this capacity to the parameters which influence it. The model is based in a concept of mathematical expectation of capacity and can be applied under saturation conditions i.e. a constant demand for service. It can serve for planning purposes, computation of single track line capacity on the base of which estimations are possible concerning a single track line performance under given conditions, as well as commercial time‐tables planning, decisions about a partial or complete construction of the second parallel track along the line in service, intermediate stations locations planning and the necessary facilities along the line under construction.

In the sensitivity analysis, the model allows a change of parameters upon which the capacity depends. These are: the length of the line segment which is considered to be bottleneck for calculation of capacity, traffic distributions per directions, train mix, train velocities and train spacing rules applied by the dispatching service when regulating the traffic on a line.     

基于引力搜索BP神经网络的柴油机故障诊断

为了提高柴油机故障诊断的精度,将引力搜索算法用于BP神经网络初始权值及阈值优化,提出了一种基于引力搜索算法和BP神经网络相结合的智能故障诊断方法,并将其运用于柴油机磨损故障的振动诊断。结果表明,该方法与BP神经网络相比,对柴油机的磨合、磨损、极限等故障诊断准确,精度提高明显,验证了该方法的有效性。     

Dynamic Tabu Search Algorithm for Solving Departure Scheduling Problem

IntroductionWith the rapid development of air traffic, the in-creasing demand of air travel has made the airlinespurchase more aircrafts. Under these circumstances,large amounts of congestion are incurred at major air-ports. According to the related data[…     

双线铁路列车运行调整的禁忌搜索算法

关于列车运行调整(TOA)的优化模型和算法,国内外专家学者提出的具有代表性的主要算法可分为仿真方法、运筹学方法和人工智能方法。TOA问题具有约束性强、优化指标众多、动态性、实时性、组合优化特性等特点。因此结合目前铁路运输工作组织实际,以列车晚点率最小为优化目标,通过在可行解空间内部搜索待调整列车最优铺画顺序的方法求解TOA问题。建立了基于混合0-1线性规划模型的双线铁路列车运行调整的优化模型,提出了采用禁忌搜索算法搜索列车优化铺画顺序的方法。经在京广线、京沪线部分区段投入实际应用,验证了模型及算法的有效性和可靠性。     

基于可靠性最短路的实时定制公交线路优化研究

为实时优化定制公交线路，提出一种基于可靠性最短路的线路优化方法，在复杂的交通环境中，可以高效的将乘客送达目的地. 对可靠性最短路进行问题描述，建立可靠性时空网络；给出求解可靠性最短路的算法步骤，构建乘客和定制公交运营商总成本最小的目标函数，采用禁忌搜索算法对问题进行求解；最后，以某市定制公交路网进行实例分析. 结果表明：与最短路线路方案比较，所提方法更接近于实际运营；在高峰时段，交通拥堵的情况下，所提方法可以提高车辆的可靠性，提升定制公交线路的服务水平.     

基于UML2.0活动图的车载设备测试用例生成方法研究

重点研究基于UML2.0活动图的CTCS-2级车载设备测试用例生成方法.介绍车载设备的构成和功能,对活动图进行形式化定义,阐述测试用例的设计,包括车载设备活动图建模规则、测试用例的形式化定义、测试覆盖准则的定义、基于深度优先搜索并且带回溯的遍历算法的测试场景生成,以及测试用例的生成算法,最后实现了测试用例生成.     

基于捕食搜索策略的混合动力汽车参数优化

混合动力汽车模型是一个较复杂的非线性系统,且设计参数较多,为一种处理燃油经济性和排放的多目标问题。文章以一辆实例样车的动力系统和逻辑门限值控制策略为例,分析并建立了以动力性能为控制约束,以最小化油耗和排放为控制目标的非线性规划模型。采用捕食搜索遗传算法,对模型进行了仿真。结果表明,该方法相对于简单遗传算法更能有效地改善车辆燃油经济性和排放。     

Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System

Due to their complementary characteristics, Global Positioning System (GPS) is integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low-cost. In this study, a low cost reduced inertial sensor system (RISS) is considered. It consists of a MEMS-grade gyroscope and the vehicle built-in odometer. The system works together with GPS to provide 2D navigation for land vehicles. With adequate accuracy, Kalman filter (KF) is the commonly used estimation technique to achieve the data fusion of GPS and inertial sensors in case of high-end IMUs. However, due to the inherent error characteristics of MEMS grade devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be handled by KF and its linear error models. To overcome the problem, Fast Orthogonal Search (FOS), a nonlinear system identification technique, is suggested for modeling the higher order RISS errors. As a general-purpose numerical method, FOS algorithm has the ability to figure out the system nonlinearity efficiently with a tolerance of arbitrary stochastic system noise. Even using online short-term training data, this method is still able to build an accurate nonlinear model that predicts the system dynamics. Motivated by the above merits, an augmented KF/FOS module is proposed by cascading FOS algorithm to a traditional KF structure. By estimating and reducing both linear and nonlinear RISS errors, the proposed method is supposed to offer substantial enhancement on the positioning accuracy of MEMS-based RISS during GPS outages. In order to examine the effectiveness of the proposed technique, the KF/FOS module is applied on the low cost RISS together with GPS in a land vehicle for several road test trajectories. The performance of the proposed method is compared to KF-only solution, both assessed with respect to a reference offered by a high-end solution. The experimental results confirm that KF/FOS module outperforms KF-only method. The results also show the applicability of the proposed method for real-time vehicle applications.