A model and optimization-based heuristic for the operational aircraft maintenance routing problem

This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.     

Assessing carbon emissions from road transport through traffic flow estimators

Carbon emissions from road transport are one of the main issues related to modern transport planning. To address them adequately, the acquisition of reliable data about traffic flow is an essential prerequisite. However, the large quantity and the heterogeneity of available information often cause problems; missing or incomplete data are one of the most critical aspects. This paper discusses how technology handles imperfect information in order to obtain more accurate quantification of CO₂ emissions. First, an analysis of single estimators and combination models is provided, highlighting their main characteristics. Then, the TANINO model (Tool for the Analysis of Non-conservative Carbon Emissions In TraNspOrt) is presented, jointly developed at the University of Seville and at the IUAV University of Venice. It consists of two different modules: the first is a combination model that optimizes the results of three traffic flow single estimators, while the second is a macro-model of carbon evaluation, which takes into account road infrastructure, vehicle type and traffic conditions. TANINO is then tested to calculate CO₂ emissions along the ring road of the Spanish city of Seville, showing its more efficient performance, compared to the single estimators normally adopted for such aims. Transport planning can benefit from the adequate knowledge of traffic flows and related CO₂ emissions, since it allows a more reliable monitoring of the progresses granted by specific carbon policies.     

Methods using belief functions to manage imperfect information concerning events on the road in VANETs

Different models using belief functions are proposed and compared in this article to share and manage imperfect information about events on the road in vehicular networks. In an environment without infrastructure, the goal is to provide to driver the synthesis of the situation on the road from all acquired information. Different strategies are considered: discount or reinforce towards the absence of the event to take into account messages agings, keep the original messages or only the fusion results in vehicles databases, consider the world update, manage the spatiality of traffic jams by taking into account neighborhood. Methods are tested and compared using a Matlab™ simulator. Two strategies are introduced to tackle fog blankets spatiality; they are compared through an example.     

基于时间卷积自编码网络的实时交通事件自动检测方法

为了提高道路异常交通事件检测效率并降低误报率,提出了一种基于时间卷积自编码网络的实时交通事件自动检测方法。首先设计了基于波动相似性度量的交通模式搜索算法用来筛选具有相同交通规律的样本数据;并构造了交通流模式矩阵作为网络模型输入,以避免样本不均衡与单一样本数据随机性对交通模式学习的干扰;同时设计了新的时间卷积自编码网络对交通模式特征进行无监督提取并对未来交通参数进行合理预测;为了降低交通流参数随机波动性带来的事件判别的干扰,设计了异常状态评估方法,通过对模型预测误差分布的学习,结合当前检测数据给出最终的事件判定结果。采用美国西雅图I90公路与I405公路2015年全年的交通流检测数据与历史事故数据进行实证研究,并与6种典型交通事件检测算法进行性能对比。研究结果表明:基于时间卷积自编码网络的实时交通事件自动检测算法具有较高的检测率、较低的误报率以及更快的平均检测时间;综合各种交通运行情况下,可接受误检率分别为5%、10%时,平均检测率可分别达到93%、98%;同时算法能够自适应学习交通状态的动态变化,对不同交通运行环境具有较强适应性与稳定性。     

基于深度特征聚类的高排放移动污染源自动识别

传统的高排放移动源识别方式是将采集的尾气数据与预先设定的排放阈值进行比较判 定，但是，排放阈值的设定很大程度上取决于人为标准，并且忽视了外部因素对尾气排放的影响， 无法真正反映移动源排放水平。针对此问题，本文结合机器学习算法，提出一种基于深度特征聚 类的高排放移动源识别方法。首先，利用随机森林算法筛选出不同污染物(CO、HC、NO)排放的 主要影响特征；其次，对多维影响特征进行聚类分析，获取高排放类别标签；最后，训练得到基于 深度森林的移动污染源分类模型，自动识别高排放目标源。通过对比实验，在合肥市机动车污染 遥测数据集上验证了所提方法的有效性。     

基于Autodesk的堤坝BIM模型构建与信息化框架开发与应用

为了解决现有技术下的堤坝BIM三维线框模型缺陷,根据堤坝工程的特点及其模型构建需求,建立了堤坝BIM模型构建与信息化框架。该框架包含堤坝工程的编码标准、命名体系、断面提取功能与三维实体生成功能等内容,可将几何模型与属性信息自动融合为实体BIM模型。该框架能在不同BIM软件平台下实施,选取Autodesk平台的Civil 3D与Revit进行. Net二次开发,在相关工程中进行了实践,成功构建了BIM模型,提高了工作效率,对堤坝工程的BIM全流程应用具有参考意义。     

舰船电力系统智能化发展趋势探究

随着舰船电力系统朝着下一代发展,其系统规模将越来越大、结构越来越复杂并且地位也越来越高。利用现代信息技术及智能理论全面提升舰船电力系统的发展水平成为了目前的迫切需求,而信息化、智能化也必将成为舰船电力系统未来的发展方向。该文从统一的数据交互标准、智能诊断与重构、三维可视化和信息安全等角度对舰船电力系统的智能化发展趋势进行分析和论述,对于其未来的发展具有一定的借鉴意义。     

RBAC在铁路客票网络安全系统中的应用

基于角色访问控制(RBAC)是近几年访问控制领域的研究热点,本文详细描述了基于角色的访问控制模型,探讨了其实现机制,并将之应用于铁路客票安全系统。在已提出的各种模型中,对角色的定义大都没有进行明确分类,往往造成角色权限的冲突,成为安全隐患。本系统根据一般安全系统的具备的特征对角色进行了分类,对各个子类定义了通用的角色约束机制,以避免冲突。客票系统是个复杂的分布式系统,在其访问控制的设计中,按照客票系统的实际情况进行了扩充和修改,从安全性和可用性给出了具体的解决方案。     

船舶安全管理决策的多目标信息效用模型研究

根据船舶安全管理决策的复杂性,运用多目标效用理论,构建了船舶安全管理决策的多目标信息效用模型,并以船舶适航性和经济效益为目标,对所建立的模型进行验证,表明其科学性和合理性。该模型的建立对于理顺安全管理决策多目标之间的关系,实现船舶安全管理决策的科学化,具有十分重要的现实意义。