汽车车窗的改良

随着现代汽车迅速发展,汽车的车窗越来越多样化与人性化,但仍存在一些不足之处.本文通过采用双层车窗以及可调风向多孔车窗的形式,对汽车的车窗进行了一些改良,使其在保证基本性能的同时,能满足乘客的更多需求.     

求解带硬时间窗车辆路径问题的改进UMDA 算法

针对带硬时间窗的车辆路径问题(VRPHTW)求解,提出了一种混合单变量边 缘分布算法(hybrid UDMA,hUDMA),改进了基本UMDA的概率模型.统计节点按路径分 布的概率,使其能够在解空间上找到节点-路径的分布关系,提高了UMDA的全局搜索 能力.采用两阶段插入法进行最佳节点搜索和路径分配完成UMDA采样操作,通过种群 进化来获取最优解.计算Solomon 100 客户的6 类问题56 个算例的实验结果表明：在最优 解的取得方面,C类算例能够全部取得最优解,R、RC类算例能以50%左右概率取得最优 解;在平均误差方面,C类算例计算结果与已知最优解一致,R、RC类算例计算误差率与 已知最优解比较接近,平均误差率为1.03%.     

求解双目标带时间窗车辆路径问题的蚁群算法

针对运输网络为多重图的双目标带时间窗车辆路径问题设计了蚁群算法.首先,建立了多重图的双目标带时间窗车辆路径问题的数学模型,提出了针对该问题解的搜索空间构建方法,定义了一种综合考虑各优化目标、时间窗和信息素等启发信息的状态转移概率公式. 为了对比说明该算法的有效性,同时设计基于NSGA-II的多目标遗传算法.针对本文算例,对蚁群算法中的各参数进行了敏感性分析,根据分析结果设定算法参数,获得了算例的Pareto最优路径集,同时与NSGA-II算法及相关文献算法针对运行时间、收敛性和群体多样性进行比较.结果显示,本文设计的蚁群算法在这3个指标上均明显优于NSGA-II算法;在相同蚂蚁数量情况下,本文的算法在收敛性和群体多样性方面优于相关文献算法.     

带时间窗的地铁配送网络路径优化问题

为应对人们日益增加的货物需求与货车进城难题,提出整合地铁网和道路交通网,形成以地铁列车和城市配送车辆为载体的地铁配送网络.考虑列车开行时间表、客户服务时间窗、城市配送车辆容量等限制条件,构建带时间窗的地铁配送网络路径优化模型,综合优化地铁列车班次的客户分配、出站点的客户分配及末端配送路径.设计随机变邻域的迭代搜索算法(ILS-RVND)进行求解,以成都市地铁3号线运输货物为例,验证了模型和算法的实用性和有效性.结果表明,地铁配送网络配送成本低,准时性高,配送车辆行驶距离短,能满足比货车单独配送更精准的服务需求.     

混合车型需求响应公交服务定制问题研究

针对现存需求响应公交服务中服务质量与服务效率的矛盾,本文提出一种使用混合车型的需求响应服务定制方法.首先采用基于k-means的算法,获得具有时空特性的服务单元.在此基础上,将载运车辆的分配与路径规划问题,视为一个带时间窗的取送问题,通过Dantzig-Wolfe分解,以最小化走行距离和使用车辆数为目标,构建需求响应公交服务定制等价分解模型.在MapReduce框架下,设计了分布式列生成算法,提出了解的可行性保障机制.最后,求解以北京市为背景的案例,验证了模型和方法的可行性和正确性.     

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创新性地考虑了多车型车辆路径问题中不同车型具有不同的边际费用和行驶费用的问题,并同时考虑车型与任务的相容性,对带时间窗约束的多车型多费用非满载车辆路径问题,以最小化总费用为目标建立了数学模型。由于该模型的NP-hard性质,基于高费用车型的边际费用和单位行驶费用比低费用车型的相应费用都要高以及低费用车型的边际费用远大于高费用车型的单位行驶费用的思想,对该模型设计了一个启发式算法。     

带时间窗空车调整问题的遗传算法研究

结合实际科研项目的研究成果,根据空车调整问题的实际要求,通过引入"到达时间窗"的概念,建立以减少空车总走行公里和广义运输成本最小化为目标的空车调整优化模型;运用遗传算法的交叉和变异算子自适应调整技术,以及染色体的矩阵编码结构设计基于矩阵编码的遗传算法,并利用C++语言进行原码程序设计和实例运算分析,取得较好的效果.     

公交车安全气压窗及火灾隐患监测预警器设计

针对紧急情况下公交乘客的逃生需求,设计了一种气压式安全公交车窗,当事故发生时,使车窗在3s内打开,为乘客提供及时有效的逃生通道。通过制作模型和力学分析计算,此设计既能在常规状态下保证车窗的强度和密封性,又能在紧急情况下使其迅速开启。此外,为预防事故,以AT89S52单片机为核心,采用2M007型可燃气体传感器和MQ-2型烟雾传感器建立公交车火灾监测报警系统,实时检测发动机舱及车体内可燃气体浓度和烟雾情况,具有声光报警功能。结合GSM通信模块,向公交车指挥中心传输报警信息,以便进行救援指挥。     

Designing a home-to-work bus service in a metropolitan area

This paper presents a model and an algorithm for the design of a home-to-work bus service in a metropolitan area. This type of service must display an equilibrium between conflicting criteria such as efficiency, effectiveness, and equity. To this end, we introduce a multi-objective model in which, among other aspects, equity is considered by time windows on the arrival time of a bus at a stop. Time windows can have other uses such as, for example, guaranteeing synchronization of the service with other transportation modes. This is one of the guiding principles of the proposed model which is based on concepts that simultaneously tackle several issues at once. Along this line, we propose a cluster routing approach to model both bus stop location and routing in urban road networks where turn restrictions exist. The resulting multi-objective location-routing model is solved by a tabu search algorithm. As an application, we analyze a home-to-work bus service for a large research center located in Rome, Italy. This case study provides a benchmark for the algorithmic results, and shows the practical relevance of the proposed methodology.     

Partial recharge strategies for the electric vehicle routing problem with time windows

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW) where the fleet consists of electric vehicles (EVs). Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR), which is more practical in the real world due to shorter recharging duration. We formulate this problem as a 0–1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently. We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTW-PR. These new mechanisms include the removal of the stations independently or along with the preceding or succeeding customers and the insertion of the stations with determining the charge amount based on the recharging decisions. We test the performance of ALNS by using benchmark instances from the recent literature. The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions.