基于Matlab油船意外泄油性能的程序设计

基于概率方法的意外泄油性能参数计算是2010年1月1日及以后新造船破舱稳性需要评估的性能之一,满足一定的衡准要求方可。根据油船的分舱分别进行船底破损概率和船侧破损概率的插值,然后按照一定的公式求出船底破损后与船侧破损后的泄油量的加权之和,即意外泄油性能参数。由于手工计算费时费力,文章基于Matlab平台,开发了意外泄油性能参数的自动计算程序模块。计算结果与文献结果相比较,证明了本程序的计算精度,具有较好的工程实用价值。     

Dynamic traffic metering in urban street networks: Formulation and solution algorithm

Traffic metering offers great potential to reduce congestion and enhance network performance in oversaturated urban street networks. This paper presents an optimization program for dynamic traffic metering in urban street networks based on the Cell Transmission Model (CTM). We have formulated the problem as a Mixed-Integer Linear Program (MILP) capable of metering traffic at network gates with given signal timing parameters at signalized intersections. Due to the complexities of the MILP model, we have developed a novel and efficient solution approach that solves the problem by converting the MILP to a linear program and several CTM simulation runs. The solution algorithm is applied to two case studies under different conditions. The proposed solution technique finds solutions that have a maximum gap of 1% of the true optimal solution and guarantee the maximum throughput by keeping some vehicles at network gates and only allowing enough vehicles to enter the network to prevent gridlocks. This is confirmed by comparing the case studies with and without traffic metering. The results in an adapted real-world case study network show that traffic metering can increase network throughput by 4.9–38.9% and enhance network performance.     

考虑移步需求的无桩型共享单车动态调度研究

共享单车系统自发的不均衡性导致单车数量分布与用户需求分布之间产生偏移，降低了系统服务能力，需要调度实现再平衡. 现有动态调度算法缺乏考虑起点车辆供给不足，用户在出行过程中“再次”取车的移步需求，难以准确识别用户真实的出行需求分布，降低了调度效果. 本文提出以用户出行选择行为为下层，以调度车辆路径规划为上层的双层规划模型框架，设计结合仿真系统的启发式求解算法. 算例场景基于上海市虹口、杨浦区共享单车历史出行数据搭建，并进行网格化处理. 算例结果表明，模型能有效识别移步需求，提高共享单车的供需匹配能力. 针对各类调度资源配置情况，共享单车的供需匹配率提升18.07%~ 19.89%，提高了共享单车系统的管理效率.     

共享自动驾驶汽车经营策略优化分析

研究共享自动驾驶汽车(SAV)与普通汽车共存背景下，SAV公司如何根据运营目标优化经营策略并影响通勤者出行方式选择. 假设高速公路上存在一定数量的独驾通勤者，无车通勤者在SAV和地铁之间进行权衡. 分析固定需求和弹性需求下，SAV公司追求系统总成本最小或系统净收益最大和利润最大时的最优经营策略，即SAV票价和容量，进而得到均衡时模式划分情况，SAV最优发车数，系统总成本或系统净收益，以及公司利润等指标. 算例对均衡结果进一步验证发现，SAV公司垄断经营会收取较高的票价，提供容量较小的车型. 在系统最优情形下，SAV公司无法获得正利润，需要政府补贴运营.     

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.     

Integrating capacity analysis with high-speed railway timetabling: A minimum cycle time calculation model with flexible overtaking constraints and intelligent enumeration

Compared with most optimization methods for capacity evaluation, integrating capacity analysis with timetabling can reveal the types of train line plans and operating rules that have a positive influence on improving capacity utilization as well as yielding more accurate analyses. For most capacity analyses and cyclic timetabling methods, the cycle time is a constant (e.g., one or two hours). In this paper, we propose a minimum cycle time calculation (MCTC) model based on the periodic event scheduling problem (PESP) for a given train line plan, which is promising for macroscopic train timetabling and capacity analysis. In accordance with train operating rules, a non-collision constraint and a series of flexible overtaking constraints (FOCs) are constructed based on variations of the original binary variables in the PESP. Because of the complexity of the PESP, an iterative approximation (IA) method for integration with the CPLEX solver is proposed. Finally, two hypothetical cases are considered to analyze railway capacity, and several influencing factors are studied, including train regularity, train speed, line plan specifications (train stops), overtaking and train heterogeneity. The MCTC model and IA method are used to test a real-world case involving the timetable of the Beijing–Shanghai high-speed railway in China.     

Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem

In this paper, we study two closely related airline planning problems: the robust weekly aircraft maintenance routing problem (RWAMRP) and the tail assignment problem (TAP). In real life operations, the RWAMRP solution is used in tactical planning whereas the TAP solution is implemented in operational planning. The main objective of these two problems is to minimize the total expected propagated delay (EPD) of the aircraft routes. To formulate the RWAMRP, we propose a novel weekly line-of-flights (LOF) network model that can handle complex and nonlinear cost functions of EPD. Because the number of LOFs grows exponentially with the number of flights to be scheduled, we propose a two-stage column generation approach to efficiently solve large-scale real-life RWAMRPs. Because the EPD of an LOF is highly nonlinear and can be very time-consuming to accurately compute, we propose three lower bounds on the EPD to solve the pricing subproblem of the column generation. Our approach is tested on eight real-life test instances. The computational results show that the proposed approach provides very tight LP relaxation (within 0.6% of optimal solutions) and solves the test case with more than 6000 flights per week in less than three hours. We also investigate the solutions obtained by our approach over 500 simulated realizations. The simulation results demonstrate that, in all eight test instances, our solutions result in less EPDs than those obtained from traditional methods. We then extend our model and solution approach to solve realistically simulated TAP instances.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

大城市中小学周边地区交通拥堵问题分析与解决策略

交通拥堵已经成为我国大城市的一种社会问题,它与每一个人都息息相关,如何更好地处理这种问题,使其减少对人们出行的影响,成为值得每一个人思考的问题。针对大中型城市中的交通拥堵其中的一种现象进行深入的剖析,系统介绍交通拥堵的成因,有针对性地提出如何改善中小学周边地区交通拥堵现象的一些观点和做法。     

高速路网不停车收费车道优化布设方法

合理设置高速公路收费站ETC (Electronic Toll Collection)车道数量，对高速公路通行效率至关重要。针对目前路网中ETC与MTC (Manual Toll Collection)车辆混行的情况，考虑ETC的普及率，结合多用户路网均衡模型和排队论方法，建立基于双层规划模型的高速路网ETC车道优化布设方法。上层模型以车辆总通行时间最小为目标，优化设置进出收费站的ETC车道数量；下层模型为多用户路网均衡模型，反映ETC和MTC车辆的路径和收费车道选择行为。下层模型通过设计收费站的等价拓扑结构，表征收费站的车道使用规则及车辆的收费车道选择行为，并采用排队论方法估计ETC和MTC车道的收费排队时间。根据模型的特点设计了基于主动集的启发式算法，利用参数二进制与拉格朗日函数法确定迭代下降方向，解决了下降方向与步长难以计算的问题；通过内嵌优化函数的方式，保证在主动集转化过程中上层约束均不会失效，且避免了迭代过程中的模型解退化问题。基于上海市绕城高速进行实证分析，结果表明：随着ETC普及率的提升，收费排队时长按照负指数趋势下降；与按比例布设ETC车道的方法相比，所提方法最高可降低57.4%的收费排队时间，且该方法可以避免ETC车道布设过多对于MTC车道通行能力挤压造成的负面效果。研究成果可以有效指导高速路网ETC车道的布设，提高路网通行效率。