基于人工蜂群算法的油船舯剖面优化设计

人工蜂群算法(ABC)是模仿蜜蜂行为提出的一种优化方法,通过各人工蜂个体的局部寻优行为,最终在群体中使全局最优值凸显出来,有着较快的收敛速度[1]。本文基于HCSR规范,以中剖面净面积最小为优化目标,以区域纵骨间距个数、板厚、型材尺寸、板缝位置为设计变量,采用ABC算法,建立了适用于油船的中剖面优化设计模型。以一艘32000DWT油船为例,对其进行了优化设计,优化结果验证了人工蜂群算法用于船舶中剖面结构优化的可行性和高效性。     

Mitigation of disruptions in public transit by Bee Colony Optimization

Dispatchers in many public transit companies face the daily problem of assigning available buses to bus routes under conditions of bus shortages. In addition to this, weather conditions, crew absenteeism, traffic accidents, traffic congestion and other factors lead to disturbances of the planned schedule. We propose the Bee Colony Optimization (BCO) algorithm for mitigation of bus schedule disturbances. The developed model takes care of interests of the transit operator and passengers. The model reassigns available buses to bus routes and, if it is allowed, the model simultaneously changes the transportation network topology (it shortens some of the planned bus routes) and reassigns available buses to a new set of bus routes. The model is tested on the network of Rivera (Uruguay). Results obtained show that the proposed algorithm can significantly mitigate disruptions.     

A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability

This paper introduces a new double standard model (DSM), along with a genetic algorithm (GA), for solving the emergency medical service (EMS) vehicle allocation problem that ensures acceptable service reliability with limited vehicle resources. Without loss of generality, the model is formulated to address emergency services to human injuries caused by vehicle crashes at intersections within an urban street network. The EMS fleet consists of basic life support (BLS) and advanced life support (ALS) vehicles suited for treating crashes with different severity levels within primary and secondary service coverage standards corresponding to extended response times. The model ensures that all demand sites are covered by at least one EMS vehicle within the secondary standard and a portion of which also meets the service reliability requirement. In addition, a portion of demand sites can be covered by at least one of each type of EMS vehicles within the primary standard. Meanwhile, it aims to achieve maximized coverage of demand sites within the primary standard that complies with the required service reliability. A computational experiment is conducted using 2004–2010 data on top two hundred high crash intersections in the city of Chicago as demand sites for model application. With an EMS fleet size of 15 BLS and 60 ALS ambulances maintained by the Chicago Fire Department, at best 92.4–95.5% of demand could be covered within the secondary standard at 90% of service reliability; and 65.5–68.4% of high severity demand and 50.2–54.5 low severity demand could be covered within the primary standard at 90% of service reliability. The model can help optimize EMS vehicle allocation in urban areas.     

基于成对组合的遗传算法生成联锁测试用例

计算机联锁自动测试的质量往往取决于测试用例的质量.本文提出一种新的设计思想:将基于成对组合覆盖的遗传算法用于计算机联锁测试用例的生成和优化,并以道岔测试为例对该算法的应用过程进行详细分析,重点介绍成对组合覆盖方法、算法的整体框架和遗传操作设计.采用遗传算法对成对组合的启发式算法AETG进行改进,有效地减少了冗余的测试用例,提高测试用例质量和联锁测试效率.     

基于再生能量利用的城市轨道交通节能时刻表的研究

再生能量在城市轨道交通系统中同一供电区间内相邻列车之间的可传递性已经在实践中被证实,为了提高再生能量利用率,针对城市轨道交通列车“行车密度高、站间距离短、日周期性强”的特点,本文提出了一种时刻表的优化方法.首先,分析再生能量在相邻列车间的传递过程,构建时刻表优化模型,通过调整列车在车站的停站时间使得制动列车产生的再生能量能够更多的供给牵引列车使用,从而提高再生能量的利用率.其次,设计遗传算法求解时刻表优化模型,得到优化的时刻表.最后,基于北京地铁亦庄线的线路数据做算例分析,结果表明该方法使得再生能量的利用量提高了48％.     

改进的K-means算法及其在铁路客户细分中的应用

提出使用距离均和识别孤立点,并引入方差对孤立点进行判断处理,对传统的K-means算法进行改进并将改进后的K-means算法应用到铁路客户细分领域,实验结果表明,改进后的K-means算法能更为准确地对铁路货运客户进行聚类分析,从多维的角度较为全面、深入地细分客户消费行为特征,从而辅助铁路货运营销部门制定有针对性的营销策略,进行高效的客户关系管理,提高市场竞争力.     

铁路客票系统中基于Paxos算法的数据一致性模型研究

文章介绍分布式系统中解决一致性问题的Paxos算法,分析铁路客票发售和预定系统(简称客票系统)的数据架构,建立了基于Paxos算法的数据一致性模型.该模型可以解决铁路客票系统中数据的一致性和完整性问题.     

高模量沥青面层合理结构形式的有限元研究

本文根据以往的经验提出了三种高模量沥青路面的结构形式,并从应力、应变和弯沉值的角度对高模量沥青面层结构进行分析,从而得出了高模量沥青混凝土路面的合理结构形式,为相关的沥青路面设计提供了一个新的分析方法。     

多线路公交行车计划优化研究

本文主要讨论多线路公交行车计划优化问题,通过引进逆差函数理论对其车辆进行多个场站间的协调调度,以达到各线路资源优化配置的目的。在具体研究中,首先基于给定的行车计划,应用逆差函数理论绘制出各场站的逆差函数图,再通过插入空驶车次（URDHC）与调整发车时间（SDT）的方法缩减车队总规模,进而编制出一套协同联动的优化行车计划。