A hybrid heuristic strategy for liquefied natural gas inventory routing

As liquefied natural gas (LNG) steadily grows to be a common mode for commercializing natural gas, LNG supply chain optimization is becoming a key technology for gas companies to maintain competitiveness. This paper develops methods for improving the solutions for a previously stated form of an LNG inventory routing problem (LNG-IRP). Motivated by the poor performance of a Dantzig-Wolfe-based decomposition approach for exact solutions, we develop a suite of advanced heuristic techniques and propose a hybrid heuristic strategy aiming to achieve improved solutions in shorter computational time. The heuristics include two phases: the advanced construction phase is based on a rolling time algorithm and a greedy randomized adaptive search procedure (GRASP); and the solution improvement phase is a series of novel MIP-based neighborhood search techniques. The proposed algorithms are evaluated based on a set of realistic large-scale instances seen in recent literature. Extensive computational results indicate that the hybrid heuristic strategy is able to obtain optimal or near optimal feasible solutions substantially faster than commercial optimization software and also the previously proposed heuristic methods.     

Dynamic airspace sectorisation for flight-centric operations

Today’s air traffic operations follow the paradigm of ‘flow follows structure’, which already limits the operational efficiency and punctuality of current air traffic movements. Therefore, we introduce the dynamic airspace sectorisation and consequently change this paradigm to the more appropriate approach of ‘structure follows flow’. The dynamic airspace sectorisation allows an efficient allocation of scarce resources considering operational, economic and ecological constraints in both nominal and variable air traffic conditions. Our approach clusters traffic patterns and uses evolutionary algorithms for optimisation of the airspace, focusing on high capacity utilisation through flexible use of airspace, appropriate distribution of task load for air traffic controllers and fast adaptation to changed operational constraints. We thereby offer a solution for handling non-convex airspace boundaries and provide a proof of concept using current operational airspace structures and enabling a flight-centric air traffic management. We are confident that our developed dynamic airspace sectorisation significantly contributes to the challenges of future airspace by providing appropriate structures for future 4D aircraft trajectories taking into account various operational aspects of air traffic such as temporally restricted areas, limited capacities, zones of convective weather or urban air mobility. Dynamic sectorisation is a key enabling technology in the achievement of the ambitious goals of Single European Sky and Flightpath 2050 through a reduction in coordination efforts, efficient resource allocation, reduced aircraft emissions, fewer detours, and minimisation of air traffic delays.     

人机混驾环境下混行车辆雾模型研究

目前搭载高级驾驶辅助系统和车联网（Vehicular Ad Hoc Network，VANET）技术的智能网联车（Intelligent Connected Vehicles，ICV）正大量涌入人工驾驶车（Manual Vehicle，MV）流之中，ICV与MV共存的异构车辆混行交通态势逐步形成，异构车辆之间的交互产生壁垒。混行之下单个ICV虽可依托单车硬件传感与单车计算单元实现与MV的交互意图识别，但其受有限算力与有限传感的影响，资源负载增大，时效性与安全性方面存在一定的误差与风险，而混行之下的VANET技术也不能够提供全局性车路资源用以高度匹配ICV与MV的交互场景，而且越来越多的ICV计算需求也在激增VANET的负载压力。对此，结合边缘计算概念中的雾计算理论，提出混行车辆雾模型（Mixed Vehicle Fog，MVF），充分发挥车联网络边缘节点能力，通过合理整合调度ICV资源的方法，解决对MV正常交互意图计算的时效性与安全性问题。该模型首先通过各感知单元响应混行交通环境下ICV与MV的正常交互事件，然后利用基于容错节点分簇的资源调度算法（Fault-tolerant Node Clustering Resource Scheduling Algorithm，FNC-RSA），动态划分局部路段内对交互事件具有相关意图感知与计算需求的ICV为一组"协同雾群"，再评估雾内ICV节点自身资源与路由代价，定向定量调度资源，最终实现雾群内部MV交互信息共享与驾驶意图协同计算。试验借助Prescan和MATLAB搭建联合仿真平台，与低能耗自适应分簇型路由算法（Low Energy Adaptive Clustering Hierarchy，LEACH）模型对比，验证MVF模型的运行效率与模型鲁棒性。研究结果表明：MVF模型通过交互事件细分协同雾群，保证了计算负载均衡，提高了ICV定向资源计算与传输效率，比LEACH模型降低了55.17%的平均跳数，缩短了45.40%的平均任务完成时间，抗时延干扰能力强，鲁棒性能优异。该模型对于打破混行环境异构车辆交互壁垒，提高混行道路交通行车安全，创造车联网络良性发展空间具有积极作用。     

Solving a discrete multimodal transportation network design problem

This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level mathematical program with complementarity constraints (MPCC). The decision variables, including the expanded capacity of auto links, the layout of bus routes, the fare levels and the route frequencies, are transformed into multiple sets of binary variables. The layout of transit routes is explicitly modeled using an alternative approach by introducing a set of complementarity constraints. The congestion interaction among different travel modes is captured by an asymmetric multimodal user equilibrium problem (MUE). An active-set algorithm is employed to deal with the MPCC, by sequentially solving a relaxed MMNDP and a scheme updating problem. Numerical tests on nine-node and Sioux Falls networks are performed to demonstrate the proposed model and algorithm.     

车道偏离预警算法研究

为提高车辆行驶的主动安全性,文章根据车辆偏离速度将TLC算法和FOD算法相结合,针对车道偏离预警系统提出了多模式车道偏离预警算法,并搭建Simulink模型,结合Carsim进行联合仿真实验。仿真结果表明,提出的算法能更好的进行车道偏离预警。     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

基于迷宫问题遗传算法的复合材料铺层数量优化

研究使用最少铺层数量,使产品达到性能指标的复合材料铺层优化问题。使用遗传算法,参考迷宫问题的解决方法,保留普通遗传算法的框架,改变求染色体的适应度值的计算过程,找出满足要求的最少铺层数量的解。在求适应度值过程中增加逐位解释环节,以得不同铺层数量的解。在求适应度值过程中增加解码、剔除无效项、对称环节,以满足复合材料铺层角角度离散、铺层角要少于4层连续相同、对称这些常见要求。结合示例,指出将逐位解释范围分段,可以提高计算效率。该方法实现了对复合材料产品的铺层数量和角度的同时优化,可以实现产品的经济性和轻量化。     

An integrated programming model for storage management and vehicle scheduling at container terminals

In this paper, we study the optimization of yard operations, which are critical for the terminal efficiency. A linear mixed integer programming (MIP) model is proposed for scheduling different types of equipment and planning the storage strategy in an integrated way. We also investigate a nonlinear mixed integer programming (NLMIP) model to reduce the number of constraints and the computational time. A set of numerical results are carried out for the comparison between the linear model and the nonlinear model. Finally, we propose a genetic algorithm for the MIP model to illustrate how large scale problems can be solved and to show the effect of different factors on the performances of the optimization model.     

Future electricity demand from battery‐powered vehicles

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.     

喷射机械手智能控制系统与喷射路径规划设计研究

为提高隧道施工中混凝土喷射的效率与质量,减轻劳动强度并使作业人员远离喷射区域,在机械化操作的基础上,研发喷射机械手智能化控制系统,对智能化控制系统的构成、各子系统的原理与作用进行介绍。喷射机械手智能化控制系统采用全站仪作为测量仪器,采用定位算法计算隧道大地坐标系与湿喷机基坐标系之间的转换关系; 采用三维激光扫描仪获取待喷面的点云空间坐标,计算待喷面的超欠挖信息; 利用机器人学建立喷射机械手运动学模型,设计正解算法与逆解算法计算喷射机械手的空间位姿,并根据目标空间位姿反算喷射机械手各关节运动值的路径规划系统。工程实践结果表明： 智能化喷射控制系统可以对周边环境进行识别,根据识别结果规划喷射路径,并根据不同区域的超欠挖情况动态控制喷射参量。