International experience with speed limits during and prior to the energy crisis of 1973–4

This paper presents an artificial neural network (ANN) based method for estimating route travel times between individual locations in an urban traffic network. Fast and accurate estimation of route travel times is required by the vehicle routing and scheduling process involved in many fleet vehicle operation systems such as dial‐a‐ride paratransit, school bus, and private delivery services. The methodology developed in this paper assumes that route travel times are time‐dependent and stochastic and their means and standard deviations need to be estimated. Three feed‐forward neural networks are developed to model the travel time behaviour during different time periods of the day‐the AM peak, the PM peak, and the off‐peak. These models are subsequently trained and tested using data simulated on the road network for the City of Edmonton, Alberta. A comparison of the ANN model with a traditional distance‐based model and a shortest path algorithm is then presented. The practical implication of the ANN method is subsequently demonstrated within a dial‐a‐ride paratransit vehicle routing and scheduling problem. The computational results show that the ANN‐based route travel time estimation model is appropriate, with respect to accuracy and speed, for use in real applications.     

适于物流配送车辆导航路径规划的路网阻抗确定方法研究

路径规划是物流配送车辆导航系统的核心功能之一。路网阻抗是路径规划的基础。由于物流配送涉及多方面利益，因此确定路网阻抗需综合考虑各种道路与非道路的影响因素。文中就各影响因素的特点对其进行量化和量纲一的量化处理，利用层次分析法计算各影响因素组合权重，得出路网阻抗．为物流配送导航路径规划提供依据。     

Heterogeneous fixed fleet vehicle routing considering carbon emission

The paper considers heterogeneous fixed fleet vehicle routing with carbon emission to minimizing the sum of variable operation costs. A cost-benefit assessment of the value of purchasing or selling of carbon emission rights, using a mixed integer-programming model to reflect heterogeneous vehicle routing, is incorporated. Essentially, the use of a carbon market as a means of introducing more flexibility into an environmentally constrained network is considered. Tabu search algorithms are used to obtain solutions within a reasonable amount of computation time. In particular, we show the possibility that the amount of carbon emission can be reduced significantly without sacrificing the cost due to the benefit obtained from carbon trading.     

楼宇三维路径模型的自动生成算法

提出了一种楼宇三维路径的自动生成算法.该算法首先使用图像细化算法及特征点提取算法建立单层楼的通道路径,并将房间等楼层实体作为结点连接到通道路径上,从而建立好单层楼的路径图;然后利用楼梯结点和电梯结点将相邻的楼层路径图相互连接起来,生成完整的楼宇三维路径模型.该路径模型是由弧和结点结构组成的带权图结构,可以应用于智能导航、楼宇自动控制、紧急情况的人员疏散及楼宇安全等领域.     

基于计算机网络路由行为方案的春运旅客区域划分探讨

根据网络路由协议的特性,利用网络路由选择最优路径的原理,把全国春运铁路网络仿真成计算机网络并划分成不同的区域运行网络路由协议,寻找一种基于网络路由行为的铁路运输方式来提高运输旅客的效率,达到缓解春运压力的目的。     

改进混合遗传算法在车辆路线问题中的应用研究

物流配送车辆路线问题，是物流配送优化中不可缺少的环节。针对传统的遗传算法存在收敛速度慢，局部搜索能力差，易早熟的缺点，采用混合遗传算法进行优化求解。即采用二重结构编码，可以使问题变得更简洁，提高遗传法的搜索效率。用个体数量控制选择策略，以保证群体的多样性，用改进的顺序交叉算子避免优良基因片断在顺序交叉时被破坏，保证算法能够收敛到全局最优。结合具体实例，通过实验计算证明了该改进算法的良好性能。     

考虑疫情影响的卡车无人机协同配送路径优化

在新型冠状病毒感染的肺炎（新冠肺炎）疫情蔓延的背景下，提出利用“卡车-无人机”协同配送新模式对疫情相对严重的地区进行物流配送，进一步研究该模式对疫情影响下物流配送的价值。考虑无人机最大飞行时间、载重以及道路条件等因素，将客户分为只能由卡车服务的客户、只能由无人机服务的客户以及卡车与无人机均能提供服务的客户3类，以车辆总服务时间最小为目标，建立车辆与无人机协同为客户提供配送服务的数学模型。设计嵌入简单启发式算法的混合邻域搜索算法，通过不同规模算例运算时间及多次运算解的波动性验证算法的有效性；通过对TSP算法、邻域搜索算子的不同组合的分析，找出最优的组合，进而对无人机最大飞行时间、无人机飞行速度载重影响因子进行了灵敏度分析。研究结果表明：计算结果验证了算法的有效性；TSP算法质量直接影响邻域搜索操作得到解的质量，可以通过高效TSP算法设计寻找更好的配送方案；无人机续航能力越强，目标函数就越小，无人机飞行速度载重影响因子越大，导致配送所需的服务时间越多，可以通过选用续航能力强、飞行速度受载重影响小的无人机提升配送服务效率。研究成果可以为城市发生重大灾难或特殊情况时，城市物流配送系统中无人机的应用提供指导和参考价值。     

IRP问题多Agent模型调度策略研究

针对存贮路径问题的复杂特性,运用多Agent技术构建智能模型框架,利用模型中Agent间的协调机制实现存贮路径问题补货策略的求解,同时阐述了调度流程的任务优先级、冲突消除等问题.与集中式数学方法相比,该求解策略更能体现出问题分布式特性,并以一个实例分析验证方法的有效性.     

Distributed coordinated in-vehicle online routing using mixed-strategy congestion game

This study proposes a coordinated online in-vehicle routing mechanism for smart vehicles with real-time information exchange and portable computation capabilities. The proposed coordinated routing mechanism incorporates a discrete choice model to account for drivers’ behavior, and is implemented by a simultaneously-updating distributed algorithm. This study shows the existence of an equilibrium coordinated routing decision for the mixed-strategy routing game and the convergence of the distributed algorithm to the equilibrium routing decision, assuming individual smart vehicles are selfish players seeking to minimize their own travel time. Numerical experiments conducted based on Sioux Falls city network indicate that the proposed distributed algorithm converges quickly under different smart vehicle penetrations, thus it possesses a great potential for online applications. Moreover, the proposed coordinated routing mechanism outperforms traditional independent selfish-routing mechanism; it reduces travel time for both overall system and individual vehicles, which represents the core idea of Intelligent Transportation Systems (ITS).     

The Green Ship Routing and Scheduling Problem (GSRSP): A conceptual approach

Recent reviews of the literature on ship routing and scheduling note the increased attention to environmental issues. This is an area of paramount importance for international shipping and will be even more so in the future. This short communication is motivated by the increasing attention to ‘green’ routing and scheduling and outlines some possible ways to incorporate the air emissions dimension into maritime transportation OR. The main contribution of this note vis-a-vis the state of the art is that it conceptualizes the formulation of the ‘Green Ship Routing and Scheduling Problem’ (GSRSP) based on existing formulations and highlights all the important parameters of the problem.