高速铁路分段垂直矩形天窗设置的探讨

目前,我国高速铁路日常综合维修主要采用分段垂直矩形天窗的形式,天窗对长途跨线列车的影响远远大于本线列车。通过对相邻天窗分段交错时间采用图解法进行分析,研究发现：天窗分段的长度和交错时间是影响顺向跨线列车运行的主要因素,列车运行时分最长的天窗分段限制了分段垂直矩形天窗对顺向跨线列车的影响范围,其影响范围等于天窗限制区段的天窗开设时长与运行时间之和。在对三个相邻天窗分段开设的情况进行研究的基础上,得出了最小天窗影响时段的天窗交错时间取值范围。     

面向船舶动力定位模拟器的推力分配仿真子系统开发

船舶动力定位(Dynamic positioning, DP)模拟器主要依赖进口,国内尚未研制具有自主知识产权的DP模拟器。DP模拟器由控制子系统、推力分配子系统、测量子系统和船舶作业三维显示等多个子系统组成。在消化吸收DP模拟器相关技术的基础上,通过研究推力分配算法,提出采用C#中WPF技术完成DP推力分配仿真子系统的设计与实现。仿真结果表明,该仿真子系统能够有效地模拟多个推进器的推力和方位角变化情况。     

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对逆向物流车辆路径问题进行了概述和分类,构建了以VRPPDTW为基础的带回程取货的逆向物流车辆路径数学模型,设计了求解该模型的最大—最小蚁群算法,对设计要素进行了详细介绍,包括初始蚁群分布,状态转移策略,以及信息素更新策略等,并给出了具体的算法步骤. 最后,以Solomon中的R101、R102、R103、R104和R105等5项示例为背景,分别取前25节点和50节点,以取货点的取货量比例分别占全部客户节点需求量的10％、30％、50％取货,得到30个算例的计算结果,并将其与Tangian和模拟退火等计算结果进行了比较,结果表明最大—最小蚁群算法在某种程度上优于其他算法     

基于改进粒子群算法的车辆路径问题研究

针对标准粒子群算法在解决多维复杂优化问题中存在的“早熟”现象,以及算法后期出现的搜索精度下降、收敛速度降低等不足,对算法做出改进：引入微生物行为机制中的趋化、繁殖、迁移算子。最后,通过实例验证对比,表明改进粒子群算法在搜索效率和解的质量方面均优于遗传算法和基本粒子群算法。     

载人深潜器观察窗的力学性能

载人球是深海载人潜水器最为关键的部件.在设计过程中,要准确了解观察窗在使用工况下的蠕变变形过程和强度变化过程,据此开展窗座设计.否则可能出现观察窗挤出窗座或者在蠕变过程中变形不协调使得密封失效,或者载人球整体抗压能力的降低.文章通过计算分析和试验两种手段,对深海观察窗的强度、蠕变、边界条件的影响进行了研究.观察窗与窗座之间的相对位移,文中认为由两部分构成:一部分是观察窗玻璃随时间的蠕变变形,另一部分为观察窗与窗座在海水压力作用下发生的挤压变形.文中采用有限元方法进行接触分析,了解在不同边界摩擦系数下,观察窗因海水压力产生的挤压变形.从加工出来的观察窗产品中,任意抽取了两套侧观察窗和两套主观察窗进行试验研究.对理论计算与试验结果进行了对比分析,相关结果可供工程设计参考.     

Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty

Experiments studying the behavior of agent-based methods over varying levels of uncertainty in comparison to traditional optimization methods are generally absent from the literature. In this paper we apply two structurally distinct solution approaches, an on-line optimization and an agent-based approach, to a drayage problem with time windows under two types of uncertainty. Both solution approaches are able to respond to dynamic events. The on-line optimization approach utilizes a mixed integer program to obtain a feasible route at 30-s intervals. The second solution approach deploys agents that engage in auctions to satisfy their own objectives based on the information they perceive and maintain locally. Our results reveal that the agent-based system can outperform the on-line optimization when service time duration is highly uncertain. The on-line optimization approach, on the other hand, performs competitively with the agent-based system under conditions of job-arrival uncertainty. When both moderate service time and job-arrival uncertainties are combined, the agent system outperforms the on-line optimization; however, in the case of extremely high combined uncertainty, the on-line optimization outperforms the agent-based approach.     

深海载人平台观察系统研究与设计

介绍水下摄像与照明装备以及观察窗的技术现状,围绕深海载人平台观察系统开展设计,阐述深海载人平台高清摄像与水下照明系统的布置及体系架构,给出深海载人平台观察窗的类型和材质,分析深海载人平台观察窗的照明条件和厚度,为深海载人平台观察系统的设计提供参考。     

Just-in-time delivery for green fleets: A feedback control approach

With increasing attention being paid to greenhouse gas (GHG) emissions, the transportation industry has become an important focus of approaches to reduce GHG emissions, especially carbon dioxide equivalent (CO₂e) emissions. In this competitive industry, of course, any new emissions reduction technique must be economically attractive and contribute to good operational performance. In this paper, a continuous-variable feedback control algorithm called GEET (Greening via Energy and Emissions in Transportation) is developed; customer deliveries are assigned to a fleet of vehicles with the objective function of Just-in-Time (JIT) delivery and fuel performance metrics akin to the vehicle routing problem with soft time windows (VRPSTW). GEET simultaneously determines vehicle routing and sets cruising speeds that can be either fixed for the entire trip or varied dynamically based on anticipated performance. Dynamic models for controlling vehicle cruising speed and departure times are proposed, and the impact of cruising speed on JIT performance and fuel performance are evaluated. Allowing GEET to vary cruising speed is found to produce an average of 12.0–16.0% better performance in fuel cost, and −36.0% to +16.0% discrepancy in the overall transportation cost as compared to the Adaptive Large Neighborhood Search (ALNS) heuristic for a set of benchmark problems. GEET offers the advantage of extremely fast computational times, which is a substantial strength, especially in a dynamic transportation environment.