复杂情况下大规模船队规划模型与求解方法

以某大型航运企业船队为例进行实证分析,利用Benders分解算法对在多航线复杂情况下,由多种类型船舶构成的大规模船队规划问题进行求解,验证所提出的模型及算法对于大规模实际问题的应用效果.结果表明,该方法实现了多航线、多型船、大规模混合整数船队规划问题的优化求解,能够为大型航运企业进行船队规划决策提供支持.     

考虑路段失效的军事物流配送中心选址模型

针对战场保障网络常常遭受敌人打击的现象,建立了考虑路段失效的军事配送中心可靠选址模型.模型为双层规划形式,上层规划的目标是优化系统在无路段失效和出现路段失效时所覆盖的总的需求量,下层规划的目标是最小化某一路段失效时系统覆盖的需求量.采用启发式算法求解下层规划,运用遗传算法求解整个模型.通过一个算例验证模型的优越性和算法的有效性.算例结果表明,尽管模型所得选址方案在无路段失效时覆盖的需求量,小于不考虑路段失效的最大覆盖模型选址方案覆盖的需求量,但在最佳路段失效时模型选址方案的覆盖效果优于后者.     

船舶汽轮机组优化设计研究

汽轮机组是船舶动力装置的重要组成部分,其重量和尺寸是影响船舶动力装置重量体积及布置的重要因素.建立包括汽轮机、冷凝器和齿轮减速器设计三部分的船舶汽轮机组数学模型,以汽轮机组重量最小为目标函数和在给定的约束条件下,采用不同的优化算法对机组进行了优化设计.与原方案相比,采用优化方案后汽轮机组重量明显减小,改进的自适应遗传算法有更好的优化性能,优化效果显著.     

高速公路场景下的电子稳像技术研究

为了去除高速公路监控视频中自身位置不稳定的事物给全局运动矢量估计带来误差,先通过Canny算子检测图像中的直线,并根据车道边界的特征筛选出道路边界,确定道路区域。再检测道路区域内的Harris角点信息,并与背景图中道路区域内的角点位置进行匹配,从而得到全局运动矢量,并计算出稳定的图像。实验表明,该算法能够满足高速公路场景下的稳像需求。     

基于动态时间调整的时空图卷积路网交通流量预测

为深入挖掘交通流数据的复杂时空特征并建立其依赖关系,提高交通流参数的预测精度, 本文提出一种新的交通流量预测模型——基于注意力机制和残差网络的时空关系图卷积网络 (TSARGCN)。TSARGCN对输入数据进行切片,实现多分支建模,挖掘数据的时间周期性特征; 引入残差网络保证网络中信息传递的完整性;利用DTW (Dynamic Time Warping)算法计算路网 中节点之间交通流量序列在时间维度的相似程度大小,提出时间图的概念,结合路网结构中各节 点的邻近关系,提出时空关系图的概念;基于时空关系图,在每个分支结合注意力机制分别进行图卷积和时间维度卷积,捕获交通流的时空特征及其依赖关系,实现对路网交通流量数据时空关系的建模。经过在公开数据集PEMSD4上进行实验,结果表明：TSARGCN在交通流量预测中的平均绝对误差 (MAE) 达 到 19.24,均方根误差 (RMSE) 达到 27.09,比 ARIMA(Autoregressive Integrated Moving Average model),Conv-LSTM(Convolution Long short-term memory)及 ASTGCN (Attention based Spatial-temporal Graph Convolutional Network)等知名交通流量预测算法具有更高的预测精度。     

基于路网可达性的城市交通离散网络设计模型及算法

根据城市交通路网分区理论,把分成的子区看成一个节点,考虑所有节点的可达性,以此度量整个路网的可达性,设计了基于路网可达性最大为目标的城市交通离散网络设计模型。采用粒子群算法,并给出一个简单的算例,算例表明,合理的添加路段,能使城市路网可达性达到最大。     

Camera calibration and vehicle tracking: Highway traffic video analytics

We describe a real-time highway surveillance system (RHSS), which operates autonomously to collect statistics (speed and volume) and generates incident alerts (e.g., stopped vehicles). The system is designed to optimize long-term real-time performance accuracy. It also provides convenient integration to an existing surveillance infrastructure with different levels of service. Innovations include a novel 3-D Hungarian algorithm which is utilized for object tracking and a practical, hands-off mechanism for camera calibration. Speed is estimated based on trajectories after mapping/alignment with respect to dominant paths learned based on an evolutionary dynamics model. The system, RHSS, is intensively evaluated under different scenarios such as rain, low-contrast and high-contrast lightings. Performance is presented in comparison to a current commercial product. The contribution is innovation of new technologies that enable hands-off calibration (i.e., automatic detection of vanishing points) and improved accuracy (i.e., illumination balancing, tracking via a new 3-D Hungarian algorithm, and re-initialization of background detection on-the-fly). Results indicate the capability and applicability of the proposed system in real-time and real-world settings.     

Train rescheduling model with train delay and passenger impatience time in urban subway network

This paper considers the train rescheduling problem with train delay in urban subway network. With the objective of minimizing the negative effect of train delay to passengers, which is quantified with a weighted combination of travel time cost and the cost of giving up the planned trips, train rescheduling model is proposed to jointly synchronize both train delay operation constraints and passenger behavior choices. Space–time network is proposed to describe passenger schedule‐based path choices and obtain the shortest travel times. Impatience time is defined to describe the intolerance of passengers to train delay. By comparing the increased travel time due to train delay with the passenger impatience time, a binary variable is defined to represent whether the passenger will give up their planned trips or not. The proposed train rescheduling model is implemented using genetic algorithm, and the model effectiveness is further examined through numerical experiments of real‐world urban subway train timetabling test. Duration effects of the train delay to the optimization results are analyzed. Copyright © 2017 John Wiley & Sons, Ltd.     

Multi-objective re-synchronizing of bus timetable: Model,complexity and solution

This work is originally motived by the re-planning of a bus network timetable. The existing timetable with even headways for the network is generated using line by line timetabling approach without considering the interactions between lines. Decision-makers (i.e., schedulers) intend to synchronize vehicle timetable of lines at transfer nodes to facilitate passenger transfers while being concerned with the impacts of re-designed timetable on the regularity of existing timetable and the accustomed trip plans of passengers. Regarding this situation, we investigate a multi-objective re-synchronizing of bus timetable (MSBT) problem, which is characterized by headway-sensitive passenger demand, uneven headways, service regularity, flexible synchronization and involvement of existing bus timetable. A multi-objective optimization model for the MSBT is proposed to make a trade-off between the total number of passengers benefited by smooth transfers and the maximal deviation from the departure times of the existing timetable. By clarifying the mathematical properties and solution space of the model, we prove that the MSBT problem is NP-hard, and its Pareto-optimal front is non-convex. Therefore, we design a non-dominated sorting genetic (NSGA-II) based algorithm to solve this problem. Numerical experiments show that the designed algorithm, compared with enumeration method, can generate high-quality Pareto solutions within reasonable times. We also find that the timetable allowing larger flexibility of headways can obtain more and better Pareto-optimal solutions, which can provide decision-makers more choice.     

大型客运站调机运用优化模型及算法

对铁路客运站调机运用进行科学合理的优化分析,对于提高客运站工作效率及保证旅客列车正点到发具有非常重要的意义。通过对客运站调机作业分析,建立客运站调机作业运用优化决策模型,将调机作业顺序模型转化为车辆数固定的、带时间窗口的车辆路径问题。采用模拟退火算法,实现调机运用的合理优化,并对模型和算法进行验证,得出优化效果明显的结论。