双链遗传算法在作业车间调度中的应用

针对简单遗传算法在解决作业车间生产调度问题时存在收敛效率低与过早收敛的局限，将一种改进的遗传算法——“双链遗传算法”应用于求解作业车间生产调度问题．与简单遗传算法相比，双链遗传算法在解决作业车间调度问题上，显著提高了搜索效率，还增强了避免早熟的能力．对双链遗传算法求解作业车间调度问题的某些策略和基本步骤作了简要的归纳和总结，编制了双链遗传算法求解作业车间调度问题的程序，通过调度例子测试表明了算法的有效性和可行性。     

短波自组网路由协议驱动方式研究

自组网路由协议可依驱动方式分为表驱动和按需驱动,这两种驱动方式的路由协议各有其优缺点.对这两类路由协议的路由发现延迟、路由开销、网络吞吐量等性能进行了对比分析,并结合短波自组网在网络中心战体系中所处的层次和主要应用对性能的需求,分析了适应于短波自组网的路由驱动方式.     

宁波-舟山港核心港区深水航路船舶定线制设计方法

在设计原则、设计内容和交通实态的调查基础上分析船舶定线制的设计要素,结合宁波-舟山港的船舶定线制系统实践分析船舶定线制的设计方法及模式等。     

A competitive, charter air-service planning model for student athlete travel

This paper presents a model for planning an air charter service for pre-scheduled group travel. This model is used to investigate the competitiveness of such an enterprise for student athlete travel in conference sports. The relevant demand subset to be served by a limited charter fleet is identified through a comparison with existing scheduled travel options. Further, the routing and scheduling of the charter aircraft is performed within the same framework. Through this modeling a method for formulating and accommodating continuous time windows and competitive market dynamics in strategic planning for a charter service is developed. Computational improvements to the basic model are also presented and tested. The model is applied to the Big Sky Conference for the 2006-2007 season, quantifying the benefits to the students from such a service and the change in expenditure associated with such a benefit for various assumptions about operations and value of time. The findings indicate the lack of spatial or sport based patterns for maximizing benefit, indicating the absence of simplistic “rules of thumb” for operating such a service, and validating the need for the model.     

国内外低路堤应用研究

进入21世纪后,公路建设达到了顶峰,公路占地成为首要问题。通过对国内外高低路堤的比较发现,高速公路建设采用低路堤方案可节约不可再生的土地资源,走可持续发展的道路。     

无线传感网络中QoS约束多播路由算法

分析了无线传感网络中具有时延、带宽和剩余能量等多QoS约束的多播路由问题.描述了一种适应于研究QoS多播路由的网络模型.提出了一种具有多QoS约束的能量有效的多播路由算法(EMRA),该算法成功地解决了无线传感网络中多播节点动态变化时的QoS路由选择问题.仿真实验证明了EMRA的有效性和优越性.     

用改进的人工鱼群算法求解TSP问题

针对人工鱼群算法在寻优过程中存在的不足,结合嗅觉在自然界鱼类捕食过程中的重要作用,在基本人工鱼群算法的基础上,提出了具有嗅觉特征的人工鱼群算法。最后,利用改进的人工鱼群算法成功解决了旅行商问题,并且通过比较基本人工鱼群算法与改进人工鱼群算法的实验结果,得出结论,改进后的人工鱼群算法在算法搜索时间、全局最优值精确度方面都有了显著的提高。     

蚂蚁记忆系统结合邻域搜寻法应用於车辆途程问题

以蚂蚁记忆系统为基础搭配不同的邻域搜寻法做区域搜寻来应用於车辆途程问题。先以2-opt、根据2-opt增加交换节线的or—opt及改良交换法的Lin—Kernighan做途程内交换,选择和已知最佳解误差百分比最少的2-opt做改善,再计算记忆盒中较佳路线之重心,并从运量最少路线开始做区域改善,以其重心为基准对附近路线做途程内2-opt及途程间Swap的交换,如此将能有效降低区域改善的时间,进而提升求解效率。利用国际标竿例题来验证AMS之求解效率,在随机问题C1-C10。求解平均误差为0．98％,在丛聚问题C11-C14求解平均误差为0．55％。     

Application of El Farol model for managing marine terminal gate congestion

Truck queuing at marine terminal gates has long been recognized as a source of emissions problem due to the large number of trucks idling. For this reason, there is a great deal of interest among the different stakeholders to lessen the severity of the problem. An approach being experimented by some terminals to reduce truck queuing at the terminal is to provide live views of their gates via webcams. An assumption made by the terminals in this method is that truck dispatchers and drivers will make rational decisions regarding their departure times such that there will be less fluctuations in truck arrivals at the terminal based on the live information. However, it is clear that if dispatchers send trucks to the terminal whenever the truck queues are short and not send trucks when the truck queues are long, it could lead to a perpetual whip lash effect. This study explores the predictive strategies that need to be made by the various dispatchers to achieve the desired effects (i.e. steady arrival of trucks and hence less queuing at the seaport terminal gates). This problem is studied with the use of an agent-based simulation model and the solution to the well known El Farol Bar problem. Results demonstrate that truck depots can manage (without any collaboration with one another) to minimize congestion at seaport terminal gates by using the provided real-time gate congestion information and some simple logics for estimating the expected truck wait time.     

基于广义回归神经网络的船舶交通量预测模型

船舶交通量受多种环境与社会因素的影响,使得船舶交通量预测存在复杂性与非线性的特点.在分析现有预测模型和方法不足的基础上,介绍了广义回归神经网络GRNN的基本原理与拓扑结构.不同类型船舶受各类因素影响的程度不同,根据天津港VTS(Vessel Traffic Services)中心提供的船舶交通量数据,按船舶种类将船舶交通量分为六类,利用GRNN神经网络分别进行预测.预测结果表明GRNN神经网络具有很强的非线性拟合能力,有效解决了天津港船舶交通量预测中的小样本问题,提高了整个预测系统的精度与稳定性.