旋转惯导捷联算法

针对旋转惯导计算原理,分析了转台测角、测速误差对系统精度的影响。为确定其影响程度,将转台测角、测速误差等效为相应量值的陀螺仪漂移。理论计算和仿真结果表明,转台测量误差特别是测速误差对惯导系统造成较大的影响。提出改进的旋转惯导捷联算法,该算法在姿态更新和导航计算后引入转台转角信息而不涉及转速信息,从算法原理上消除了测速误差对系统精度的影响。     

两种目标定位方法的误差分析

单站球坐标定位法和梯度法是目标定位中经常使用的2种方法.针对2种目标定位方法的定位精度问题,分别介绍了2种方法的原理及误差处理过程.通过理论分析和仿真计算对上述方法的目标定位精度进行比较,结果表明,梯度法比单站球坐标定位法具有更高的目标定位精度.     

Approximate network loading and dual-time-scale dynamic user equilibrium

In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that Pang and Stewart (2008) refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of Friesz et al. (1993) may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to Daganzo (1995). We provide a numerical example based on the much studied Sioux Falls network.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.     

考虑最短距离作业的港口拖轮作业调度优化

为了合理调度拖轮作业,有效提高拖轮的利用率,减少船舶等待时间,针对拖轮作业特点,用多处理器任务调度理论分析了拖轮作业调度问题.在考虑拖轮最短距离作业的前提下,以最小化最大完工时间为优化目标建立了拖轮作业调度模型.采用演化策略算法对拖轮作业调度问题进行优化,提出了一种基于轮盘赌概率分配的编、解码方法,并设计了三点交叉互换的重组算子和个体基因交叉互换的变异算子.算例结果表明:用演化策略算法的优化结果优于仿真结果和粒子群算法的计算结果.采用实数编码时,通过优化算法得到的最大完工时间最小值为356 min,比仿真结果减少了117 min;而采用整数编码时,最大完工时间最小值为369 min,比仿真结果减少了104 min.验证了最短距离作业原则的有效性.     

城市公路客运站选址与班线分配方法研究

以旅客出行的时间消耗降最低为目标,兼顾客运站的建设成本和经营效益,建立公路客运站的选址与班线分配模型。通过遗传算法求解,并请专家对最好的几组计算结果予以评估和修正,得到客运站的最优选址和班线分配方案。     

模拟退火算法在供应链生产调度中的应用

研究一个简单供应链中的生产调度问题,目的是找到一个合理生产排序使得单位时间的成本最小,建立该问题的单目标非线性规划模型。运用模拟退火算法,结合邻域搜索进行求解,最后举例验证算法的有效性。     

������Ⱥ�㷨�Ĺ�·���˿�������Ż�

公路客运快递依托客运线路采用直达模式进行运输获得了较好的经济效益,但直达模式却制约着其运输网络的能力和结构. 为提高运输网络的能力,完善运输网络结构,发挥公路客运时效性好的优势,本文提出转运的思想,并用优化模型设计公路客运快递的运输网络,使快件运输时间最小化. 优化模型是带有时间窗的运输网络设计问题,传统算法难以求解,因此用蚁群算法进行求解. 算例分析表明,基于优化模型设计的运输网络可以节省快件的运输时间,提高公路客运快递的运输能力.     

铁路枢纽内客运站分工的优化模型及算法

为了合理分配引入高速和城际客运专线的铁路枢纽内客运站的接发旅客列车任务,以始发终到客流在各客运站之间的分配、始发终到和通过客流的运行路径为决策变量,以最小化枢纽内始发终到和通过旅客的费用为目标,以客运站始发终到能力和通过能力、铁路线路旅客列车通过能力、旅客运输需求为约束条件,建立了客运站分工多目标非线性混合整数规划模型.考虑铁路枢纽内运行路径少的特点,设计了基于枚举法、多最短路和线性规划的精确求解算法.算例表明,该方法能够对3个客运站的接发旅客列车数量进行定量分配,始发终到能力占用率分别为100%、64.5%和69.3%,通过能力占用率分别为78.7%、65.9%和25%.     

一位全加器优化设计算法的研究

二十世纪九十年代以来,演化硬件技术通过演化算法自动生成数字电路已成为研究热点之一。人们希望通过实现较大规模数字电路的自动设计及优化,来提高设计效率、降低能耗,从而节约现实成本。该文以扩展矩阵编码规则为基础,基于多目标优化技术,结合轮盘赌选择、子电路交叉、自适应变异以及跨时代精英机制组成电路演化优化算法。