航班快速恢复建模与仿真研究

随着民用航空的发展与竞争,航班延误不仅影响航空飞行的安全与正常,更与航空公司的运营效率、运营成本及乘客利益息息相关。针对某一恶劣天气影响,对某公司受影响航班进行重新调配,考虑到航班的备降、盘旋等待、延误、取消等多种状态,以总成本最小为目标函数,建立航班快速恢复模型,通过MATLAB运用遗传算法设计航班恢复算法进行求解,得出最经济的航班恢复方案。     

斜腿刚构桥几何布局的优化设计

采用数学规划的方法从静力和动力两方面对斜腿刚构桥的几何布局进行优化设计。静力优化设计的优化目标是截面截面应力平方均值最小，动力优化设计的优化目标是结构自振周期平方和最小。采用了直接搜索法寻优。通过算例可知，这两种优化设计方法均可行，且均为刚性设计。     

基于红外搜索系统的被动测距技术研究

红外搜索系统是一种被动探测系统，量测数据中无目标的距离量；而评判来袭目标的威胁程度离不开其距离量。介绍了基于红外搜索系统的被动测距技术测量目标距离的算法、原理框图以及仿真试验与结果。     

基于排序二叉树的摘挂列车编组钩计划自动编制方法

根据摘挂列车编组调车作业原理,将摘挂列车下落问题抽象为排序问题,提出一种基于排序二叉树的编组钩计划自动编制方法.根据待编列车序列构造排序二叉树;利用排序二叉树的有序性快速搜索出有序车组序列,将其作为下落方案的可选集.考虑邻组、暂合列内收编固定组组别和空闲组别、端组等因素,从可选集中筛选出较优的下落方案.通过定义收编固定组简化列车收编过程,实现列车收编过程的计算机自动编制.通过实例验证,采用该方法降低了选择下落方案的复杂性,减少了列车编组钩计划的调车钩数,而且可根据实际调车线数灵活调整方案.     

平行调试方法的应用及安全风险分析

为了避免综合监控系统延伸线接入主线时功能调试对主线运营造成的影响,通过在主线停运期间用增设的临时中央级设备进行调试,在主线运营期间则恢复至原有中央级设备,当调试已完成且相应问题已基本整改时将延伸线正式接入主线。将其中搭建的临时中央级设备、延伸线临时接入主线、开展平行调试、骨干网接入和设备升级等重要步骤进行归纳总结,并针对项目实施前的主线通信网络发生功能性损害的安全风险,以及项目实施过程中的系统数据库出现紊乱、临时设备上电后超载跳闸、非调试期间人员误操作等的安全风险提出相应管控措施,进一步提高后续类似项目的施工效率和安全性。     

Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible.Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles. We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs.Computational experiments were carried out on the 1 Commodity Pickup and Delivery Traveling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demonstrate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.     

变邻域搜索求解公共交通乘务调度问题

公共交通乘务调度问题是一个将车辆工作切分为一组合法班次的过程,它是NP难问题,许多求解方法的效率都与班次评价密不可分,本文通过裁剪TOPSIS方法(Technique for Order Preference by Similarity to an Ideal Solution)设计了TOPSIS班次评价方法.此外,通过裁剪变邻域搜索算法使之适合求解乘务调度问题,提出了基于变邻域搜索的乘务调度方法(Crew Scheduling Approach Based on Variable Neighbourhood Search,VNS),其中,并入了TOPSIS班次评价方法在调度过程中进行班次评价,设计了两种带概率的复合邻域结构以增加搜索的多样性,帮助跳出局部最优,在VNS中利用模拟退火算法进行局部搜索.利用中国公共交通中的11组实例进行了测试,测试结果表明,VNS优于两种新近提出的乘务调度方法,且其结果关于班次数接近于下界.     

Large-scale dynamic transportation network simulation: A space-time-event parallel computing approach

This paper describes a computationally efficient parallel-computing framework for mesoscopic transportation simulation on large-scale networks. By introducing an overall data structure for mesoscopic dynamic transportation simulation, we discuss a set of implementation issues for enabling flexible parallel computing on a multi-core shared memory architecture. First, we embed an event-based simulation logic to implement a simplified kinematic wave model and reduce simulation overhead. Second, we present a space-time-event computing framework to decompose simulation steps to reduce communication overhead in parallel execution and an OpenMP-based space-time-processor implementation method that is used to automate task partition tasks. According to the spatial and temporal attributes, various types of simulation events are mapped to independent logical processes that can concurrently execute their procedures while maintaining good load balance. We propose a synchronous space-parallel simulation strategy to dynamically assign the logical processes to different threads. The proposed method is then applied to simulate large-scale, real-world networks to examine the computational efficiency under different numbers of CPU threads. Numerical experiments demonstrate that the implemented parallel computing algorithm can significantly improve the computational efficiency and it can reach up to a speedup of 10 on a workstation with 32 computing threads.     

The load-dependent vehicle routing problem and its pick-up and delivery extension

The present paper examines a Vehicle Routing Problem (VRP) of major practical importance which is referred to as the Load-Dependent VRP (LDVRP). LDVRP is applicable for transportation activities where the weight of the transported cargo accounts for a significant part of the vehicle gross weight. Contrary to the basic VRP which calls for the minimization of the distance travelled, the LDVRP objective is aimed at minimizing the total product of the distance travelled and the gross weight carried along this distance. Thus, it is capable of producing sensible routing plans which take into account the variation of the cargo weight along the vehicle trips. The LDVRP objective is closely related to the total energy requirements of the vehicle fleet, making it a credible alternative when the environmental aspects of transportation activities are examined and optimized. A novel LDVRP extension which considers simultaneous pick-up and delivery service is introduced, formulated and solved for the first time. To deal with large-scale instances of the examined problems, we propose a local-search algorithm. Towards an efficient implementation, the local-search algorithm employs a computational scheme which calculates the complex weighted-distance objective changes in constant time. Solution results are presented for both problems on a variety of well-known test cases demonstrating the effectiveness of the proposed solution approach. The structure of the obtained LDVRP and VRP solutions is compared in pursuit of interesting conclusions on the relative suitability of the two routing models, when the decision maker must deal with the weighted distance objective. In addition, results of a branch-and-cut procedure for small-scale instances of the LDVRP with simultaneous pick-ups and deliveries are reported. Finally, extensive computational experiments have been performed to explore the managerial implications of three key problem characteristics, namely the deviation of customer demands, the cargo to tare weight ratio, as well as the size of the available vehicle fleet.     

Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.