土坡三维最危险滑动面搜索方法

文章基于均质土坡旋转椭球形滑动面的假设,提出了一种用遗传算法确定均质土坡三维最危险滑动面及其对应的最小稳定系数的方法。该方法模拟了生物遗传进化的过程,克服了传统方法容易陷入局部极小值的缺点,是一种全局优化算法。     

基于混沌优化的港口集装箱运输网络优化

随着集装箱船舶大型化的发展,航运公司势必会尽可能的选择科学、合理的航线,以实现大型集装箱船的规模效益,降低单箱运输成本,从而获得更大的效益。这样集装箱港口出现了枢纽港与支线港的分离。本文以体现大型集装箱船的规模效益为前提,尝试将混沌优化这一理论应用到港口运输网络的优化中,寻找合理的港口集装箱运输线路及服务频率,为我国港口集装箱运输提供决策依据。     

Sequencing twin automated stacking cranes in a block at automated container terminal

In the considered automated container terminal (ACT) that is designed for Shanghai Yangsha Terminal, two automated stacking cranes (ASCs) are configured for each block and they interact with automated lifting vehicles (ALVs) at the two ends of a block individually. To increase the capacity, container yards with multiple rows of blocks perpendicular to the terminal’s shoreline are considered. To utilize the yard spaces, the twin ASCs are devised to share the same tracks installed at the two sides of a block, while interferences between the ASCs challenge the routing and sequencing operations. To isolate the control and simplify the coordination of the two ASCs, the interference between ASCs is formulated by analyzing the minimal temporal intervals between any two tasks. Three models are then established to sequence the container handling tasks under the minimization of the makespan. An exact algorithm and a genetic algorithm are designed to solve the problem. Numerical experiments show that the algorithms are competitive comparing to on-the-shelf solvers. Practical implications are investigated based on the formulations and experimental results. The managerial implications and technological aspects of applying the formulations and algorithms to practical situations to real-world ACTs are discussed.     

Connectivity-based optimization of vehicle route and speed for improved fuel economy

Traditionally, vehicle route planning problem focuses on route optimization based on traffic data and surrounding environment. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP) problem, to optimize vehicle route and speed simultaneously using both traffic data and vehicle characteristics to improve fuel economy for a given expected trip time. The required traffic data and neighbouring vehicle dynamic parameters can be collected through the vehicle connectivity (e.g. vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, etc.) developed rapidly in recent years. A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem. It is able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) with a Simulink powertrain model, is developed to validate the proposed VMMP method. Four simulation studies, based on a real traffic network, are conducted for validating the proposed VMMP: (1) ideal traffic environment without traffic light and jam for studying the fuel economy improvement, (2) traffic environment with traffic light for validating the proposed traffic light penalty model, (3) traffic environment with traffic light and jam for validating the proposed adaptive real-time optimization strategy, and (4) investigating the effect of different powertrain platforms to fuel economy using two different vehicle platforms. Simulation results show that the proposed VMMP method is able to improve vehicle fuel economy significantly. For instance, comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%.     

Optimising courier routes in central city areas

Determining efficient routes for couriers in central city areas is becoming more challenging due to growing levels of congestion. New technologies allow on-street loading zones to be booked to reduce waiting times and circulation.Couriers when planning deliveries need to consider both driving and walking routes to optimise their activities. A two-layer optimisation model has been developed for determining the best routes for minimising operating and environmental costs. A genetic algorithm is proposed to find (near-)optimal solutions. The output can provide guidance for couriers as well as planners who are involved in determining duration limits at loading zones.     

求解交通配流问题的一种新算法

路径选择是交通配流的主要问题,本文采用遗传算法设计了一个新模型以求解交通配流问题。实例计算表明,这一算法能迅速求出问题的全局最优解。     

提高船舶操纵模拟器视景成像效率的算法设计

本文介绍了在船舶模拟器开发设计中采用的提高视景成像效率的算法。特殊代码设置使得物标和图素与视域交界面的判断简便易行,利用物标裁剪预处理减轻了图素裁剪的计算量;通过简化的面方向判断计算和深度排序法改进了隐藏面消除的处理速度     

城市轨道交通列车开行方案的优化理论及方法

根据城市轨道交通的特点,分析客流需求的时变特征、乘客在车站的等待行为和列车与运行线的匹配关系。以轨道列车运行要求、动车组数量和运营企业利益为约束条件,以最小化乘客的等待时间为目标函数,构造拥挤环境下城市轨道交通列车开行方案的数学模型。采用遗传算法求解模型,设计基于二进制结构的特殊编码方法。以南京市地铁1号线为背景,应用本文的模型和算法进行了仿真测算。计算结果表明,利用本文模型得到的非均衡列车开行方案能够更好地满足城市轨道交通乘客和运营企业双方的利益。     

Optimal Genetic View Selection Algorithm for Data Warehouse

To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.     

Kernel Factor Analysis Algorithm with Varimax

Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.