CATIA-V5的知识工程优化功能在汽车产品开发上的应用

知识工程优化是通过捕捉、优化设计意图，使用户可以按目标进行优化设计的一种优化方法。介绍了与知识工程优化有关的概念和采用CATIA—V5软件知识工程优化功能进行产品优化设计的流程，并以油底壳体积优化设计和刮雨器的结构功能优化设计为例，提出了CATIA—V5知识工程优化的方法。     

地铁架空刚性接触网平面设计软件的优化

当前地铁设计任务的不断增加对接触网辅助设计软件提出更高的要求。介绍架空刚性接触网平面设计软件的优化内容,采用先进的编程语言实现分布式软件架构,对架空刚性接触网平面设计中的底图处理、锚段智能划分、悬挂点智能布置及冲突检测和调整、拉出值自动计算、汇流排和渡线设计、工程量统计及图幅处理等算法进行优化设计。实际应用表明,本软件提高了地铁架空刚性接触网平面设计的成图质量及设计效率,可满足高质、高效的出图需求。     

基于遗传算法的集装箱单箱三维装载优化问题

研究应用改进遗传算法优化铁路集装箱单箱三维装箱问题,达到充分利用集装箱装载质量或装载容积的目的。改进遗传算法中考虑货物放置方向、装载容积、装载质量及装载重心等约束条件,并结合方向及顺序约束采用巧妙的个体编码方法,结合装载容积、装载质量及重心约束构造合理的适应值函数。以16件货物装入20英尺箱为实例,应用改进遗传算法进行计算,计算结果为集装箱装载容积利用率为63 18%,并且可以缩短计算时间一半以上。     

基于蚁群算法和群决策理论的动态路径优化研究

文章针对城市交通信号控制中的动态路径优化问题,综合考虑相邻交叉口间距和路段交通饱和度两个参数的影响,提出了一种基于蚁群算法和群决策理论的动态路径优化算法模型,并通过仿真实验,对比分析了该算法模型的有效性。     

杭州公共自行车交通系统发展优化建议与思考

文章通过大量的信息调研,综合分析了杭州市公共自行车交通系统(PBS)的运营现状及存在的问题,提出了拓宽非机动车道、服务点规模等级化、站点设施结构智能化、换乘系统综合化的公共自行车交通系统发展优化建议。     

尼铁现代化项目中矩形空心桥台的优化设计

当前常用的时速200 km的铁路矩形空心桥台参考图中的桥台截面尺寸偏厚,安全富余量很大。为节约混凝土圬工量,希望在保证安全的前提下减少桥台截面尺寸。首先提出专门针对铁路矩形空心桥台台身截面计算的荷载图示,然后利用这一新的图示对拉伊铁路项目中使用的常规矩形空心桥台进行有限元分析,经过不断试算提出最优的截面尺寸。在钢筋用量基本不变的情况下,新尺寸桥台较原参考图中桥台的混凝土用量减少10%以上。对于项目所在地尼日利亚国水泥价格高昂的情况而言,桥台尺寸的这一优化将产生较大的经济效益。     

高铁站功能区布局优化模型研究

为使高铁站功能区的布局更加科学合理,论文以旅客在高铁站房内的平均走行时间最少和高铁站建造成本最低为目标,综合考虑功能区的面积、长宽比例、功能区禁止重叠、流线等约束条件,构建基于多目标混合规划的高铁站功能区布局优化模型,通过算例验证了模型的有效性。该方法得出的结论可以为高铁站功能区布局的规划设计提供参考。     

Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

Rapid motor vehicle crash detection and characterization is possible through the use of Intelligent Transportation Systems (ITS) and sensors are an integral part of any ITS system. The major focus of this paper is on developing optimal placement of accident detecting omnidirectional sensors to maximize incident detection capabilities and provide ample opportunities for data fusion and crash characterization. Both omnidirectional sensors (placed in suitable infrastructure locations) and mobile sensors are part of our analysis. The surrogates used are acoustic sensors (omnidirectional) and Advanced Automated Crash Notification (AACN) sensors (mobile). This data fusion rich placement is achieved through a hybrid optimization model comprising of an explicit–implicit coverage model followed by an evaluation and local search optimization using simulation. The compound explicit–implicit model delivers good initial solutions and improves the detection and data fusion capabilities compared to the explicit model alone. The results of the studies conducted quantify the use of a data fusion capable environment in crash detection scenarios, and the simulation tool developed helps a decision maker evaluate sensor placement strategy.     

Structural Optimization and Strength Analysis of the Lifting Arm of a Wet Shotcrete Spraying Unit北大核心CSCD

Wet shotcrete spraying units are widely used in underground engineering, for railways, highways, water conservancy and hydropower stations, municipal works, mining and military and other industries. Structural analysis and mechanical behavior optimization are conducted regarding the lifting arm of a TKJ series shotcrete spraying unit, and optimization of the hinge point position and working scope of the lifting arm is realized. The optimal layout scheme for the hinge force is given based on the Monte Carlo method, the hinge force of the lifting arm is improved for the mean and maximum values, and the maximum and average hinge force of the lifting cylinder decrease by 23.14% and 7.70%, respectively, compared with that of the original scheme. The static strength is checked using Ansys-Workbench for the optimized scheme, and the results show that the optimized scheme has a larger safety re-serve and that the structural design is more reasonable than the original scheme. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved.     

Prioritizing road extension projects with interdependent benefits under time constraint

Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: “I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects?” To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on “gradient maximization” is developed. To address the projects’ interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.