基于货物时间价值的物流网络最优配送路径模型研究

本文构建了物流配送网络中货物时间价值相关的最小配送费用路径模型,并研究了其算法。模型把整个配送过程分为运输过程和装卸过程,并且考虑了由于运输过程和装卸过程时间延迟造成的货物价值损失,这也是和其它类似成本路径模型最大的区别。该模型算法可以利用计算机自动完成,不受物流配送网络大小和节点的限制,并保证算法的正确性。     

Online Prediction of Travel Time: Experience From a Pilot Trial

Abstract

This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.     

Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.     

基于韧性最优的灾后公路网修复调度研究

为有效修复灾后公路网的受损路段从而加快恢复路网畅通,研究以韧性最优为目标的灾后公路网修复调度问题。首先,采用交通需求满足率表示路网性能,并构建路网性能韧性和恢复速度韧性评价指标。其次,构建基于韧性最优的修复调度双层规划模型,其中上层模型是多目标混合整数规划模型,用以确定修复路段的选择和修复先后顺序,下层模型为日变交通流分配模型,模拟修复期间的路网交通流动态演变。然后,采用禁忌搜索算法和Frank-Wolfe算法分别求解上层模型和下层模型,并通过循环迭代得到模型最优解。最后,通过案例分析验证模型和算法的有效性。研究结果表明:在一定数量的修复资金和抢修队约束下,该模型得到的最优修复调度方案能最大限度地提升路网韧性,并且在修复过程中,只有当受灾区域内某线路中的所有受损路段均完成修复后,路网性能才开始呈阶梯式上升,并且路网性能的提升速度表现为先慢后快。对不同参数的敏感性分析表明:修复预算的增加使路网性能韧性和恢复速度韧性分别以15.65%和17.72%的平均速度增长和降低,但当修复预算超过1 800万元后,只增加修复预算不一定能获取更优的修复调度方案;当决策者偏好系数变化时,路网的性能韧性和恢复速度韧性具有相反的变化趋势,且2个韧性指标的平均变化率分别为5.96%和4.48%;增加抢修队数量可提升路网的性能韧性和恢复速度韧性,但增加抢修队数量所产生的边际效益逐步降低,分别由0.11和0.43降至0.01和0.02。     

水下传感器网络覆盖优化算法

针对水下传感器网络随机部署时节点分布不均匀、网络覆盖率不高问题,提出一种基于改进全局人工鱼群算法的网络覆盖优化算法。优化算法以覆盖率为目标函数自适应调整人工鱼移动步长,同时在向最优人工鱼靠拢过程中引入权重系数,提高算法寻优精度和收敛速度。实验结果表明,与遗传算法和鱼群算法相比较,该改进算法优化了网络覆盖率,加快了算法收敛速度,提升了网络性能。     

无线传感器网络中基于势能场的能量均衡模型

在无线传感器网中,现有的节能路由以找到能量消耗最小的路径为目标,导致围绕汇聚节点的能量空洞．网络能量均衡可以有效的利用和管理整个网络的节点能量,通过有效的分配网络负载,实现网络寿命的延长,网络效率的提高．本论文中提出了基于势能场的模型,建立了基于势能的路由,并且通过相位切换技术,达到局部和全局的能量均衡,从而延长无线传感器网络的使用寿命．     

高负荷路网微循环交通组织实证研究

微循环交通组织是区域交通优化组织的重要方法.首先,在分析高负荷路网交通特征的基础上,归纳了微循环交通组织的常见问题及相应的改善措施;然后,结合中关村西区微循环交通组织实例,从OD划分、有效车流路径规划及信息诱导原则和方法三个方面,构建了有效路径下的微循环交通组织仿真模型及仿真流程;最后,利用VISSIM软件仿真,比较了...     

A coupled multi-agent microsimulation of social interactions and transportation behavior

Choice set formation, location and mode preferences, coordinated scheduling, alternative utility valuations, and shared mobility resources are among the many activity-travel issues hypothesized to be significantly influenced by traveler interdependencies. Empirical evidence lags theory, particularly about the geography of social networks. A simulation tool is presented to let the experimenter construct and test hypothetical interdependencies between geography, socially-linked travelers, and activity-travel choices. The exploratory tool is integrated in the Multi-Agent Transportation Simulation Toolbox (MatSim-T). Initially, any social network can be constructed and embedded in geography. It can remain static, or be adapted to the travel patterns of the agents. The interactions and exchanges between agents influencing socializing and/or travel behavior can be defined in substance and in time/space. The reward for socializing or being socially linked can be varied. Finally, the co-dependence of social factors and travel behavior can be studied. This paper introduces the model and presents verification results which illustrate the coupling of extremely simplified socializing assumptions and travel behavior.     

一种基于WSN和GPS融合定位的 浮动车路况优化研究与验证

为解决浮动车数据在城市范围内存在的“信号漂移”和“信号盲区”问题,提高地图道路 适配准确率和路况信息准确率,研究应用WSN无线定位技术以及地图匹配技术,设计数据融合 框架,提出基于WSN和GPS的融合数据路况优化算法及计算流程,监测车辆在路网上的行驶状 况和各路段的拥堵程度。通过选取广州市800 个车载传感器节点、20 个路侧传感器节点以及3 个路段进行现场数据验证,结果显示在加入WSN定位数据并采用上述算法处理后,地图道路适配准确率提高约4%,路况信息准确率提高约5%,同时路况信息的处理速度也得以提高。可见,基于该优化算法,WSN和GPS融合定位数据能有效提高浮动车路况的准确性和有效性。     

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针对无容量约束的单分配轴-辐式物流网络设计问题的特点,为其建立了单分配p-枢纽中位模型,并提出了一种基于蚁群算法的启发式求解算法. 该算法分两步实现：首先利用蚁群算法来确定网络中枢纽节点的位置,然后用另一种启发式算法将非枢纽节点分配给枢纽节点,同时,将一种基于6种邻域结构的变邻域搜索算法作为蚁群算法的局域搜索策略以提升算法的全局搜索能力,并加快收敛速度. 最后结合澳大利亚邮政数据进行了算例仿真实验,并对蚁群算法中参数的合理设置进行了测试分析,实验结果表明,该算法在求解此问题时有着良好的有效性和较快的求解效率.