基于闭塞区间的高速列车运行时间与节能协同优化方法

提出了一种高速列车运行时间与节能协同优化方法.针对由动态调度层、优化控制层、跟踪控制层组成的列车运行控制与动态调度一体化结构,设计了面向动态调度层和优化控制层的列车运行时间调整策略和节能速度位置曲线.基于高速铁路闭塞区间,建立了列车区间模型和列车速度曲线节能优化模型.利用模型预测控制方法对列车区间运行时间进行调整,优化列车总延误时间;根据调整后的区间运行时间设计列车运行优化速度位置曲线,减少列车运行能耗.仿真算例验证了设计的运行时间与节能协同优化策略的有效性.     

单线列车调度问题的双向阻塞车间调度模型及其粒子群求解算法

针对单线列车调度问题的特点,以线路中列车的总运行时间最小为目标,建立了可以直观描述问题解空间的双向阻塞车间调度模型,并提出了一种有效的离散粒子群优化算法进行求解。该算法基于双向阻塞车间调度模型设计了排列编码形式,从而可确定列车的运行顺序,同时利用随机策略和运行时间最短优先策略选择列车运行轨道;算法在求解过程中,提出了列车冲突的检测和化解方法,并按照“调度-检测冲突-化解冲突”的步骤逐区段调度列车运行;最后,利用离散粒子群优化算法进行全局优化,得到问题的最优解。仿真实例表明,所得模型和算法能够高效地求解单线列车调度问题。     

基于免疫萤火虫算法的RFID仓储车辆动态调度

针对物流配送实时仓储车辆调度问题,提出了一种基于RFID技术的免疫萤火虫车辆动态调度框架。建立了基于配送成本的带约束条件车辆路径问题数学模型,运用免疫萤火虫优化算法求解该模型,免疫萤火虫优化算法将萤火虫优化及免疫克隆技术融合,采用多层进化模式,在低层萤火虫操作中及高层免疫操作中分别引入多态子种群自适应机制和全局极值筛选策略,以提高算法全局收敛效率,在此基础上设计了仓储车辆动态调度框架,将车辆动态调度过程分为车辆调度任务控制和路径优化两个阶段,给出了车辆动态调度任务处理流程。实验仿真表明,该车辆动态调度算法能够有效地解决大规模动态物流车辆调度问题。     

列车运行调度方法的研究与分析

将列车运行调度的相关研究按列车运行图编制和列车运行调整两方面进行分类,介绍列车单双线区段和客运专线的运行图编制方案,分析列车运行实时调整、列车插入(取消)服务及冲突预测方法,探讨研究列车运行自动调度系统的实时性和智能性、突发条件下列车运行的疏解方案、节假日高峰情况下列车运行调度等问题的重要性。     

基于混合逻辑动态的列车运行调度模型

通过对列车运行调度问题的分析,指出列车运行过程中存在正常事件、客观事件和主观事件三种类型事件,调度的本质是主观事件通过改变列车的运行速度来克服客观事件对列车造成的影响,是典型的混杂系统.在考虑车站股道约束和区间容量两个约束条件的基础上,采用混合逻辑动态理论建立了列车运行调度模型,仿真结果表明了模型的有效性.     

城轨列车精确停车智能控制算法研究与仿真

随着我国城轨交通的大力发展,列车自动驾驶（ATO）系统在城轨列车中的应用越来越广泛,精确停车是衡量城轨列车自动驾驶性能重要指标之一。以精确停车、舒适性及能耗为指标,利用列车牵引计算知识建立列车运行多目标模型;利用遗传算法对该模型进行优化,结合MATLAB仿真软件得出列车运行理想目标曲线;运用SIMULINK模块分别搭建基于PID控制的仿真模型、基于模糊PID控制的仿真模型以及基于预测模糊PID控制的仿真模型,得出相应的列车运行跟踪曲线,并进行比较。结果表明：较前两者,预测模糊PID控制能最大程度地提高列车的停车精度。     

灾后动态环境下基于MPC的应急运输实时调度

应急资源运输是灾后应急响应中的一类重要行动.针对强地震后灾区物资供应和伤员转运需求,建立一个多周期应急运输调度模型,基于模型预测控制(MPC)的多周期滚动优化框架,提出相应的运输计划动态调整策略,以适应供应与需求等决策参数的预计不准确性和动态演变性.数值实验验证了所建运输调度模型及所提出动态调整框架的有效性.与传统基于多周期应急运输调度方法相比,所提出方法能够有效减少运输调度的不满足量,并能显著消除预测结果不准确性对不满足量的影响.     

基于SSA-BP的铁路沿线风速预测方法

精确的铁路沿线风速预测对提升列车运行控制和调度智能化水平起着关键作用。提出在BP神经网络的基础上采用麻雀搜索算法(SSA)对其进行优化,构建SSA-BPNN模型,新的模型优化了BP神经网络的阈值和权值,将SSA-BPNN模型与GA-BPNN模型的预测值进行对比。结果表明,两种模型均可对铁路沿线时间序列的风速进行预测,但SSA-BP算法的预测精度更高,预测性能更加良好,RMSE从1.3743减小到0.7803,R²则从0.8901增大到0.9363。     

基于两阶段算法的列车调度问题

考虑突发铁路损坏对列车运行的影响,在列车运行调度理论的基础上,建立了单线铁路调度模型,设计了一种带有突发事件处理能力的两阶段列车调度算法,第1阶段对列车区间运行速度进行调整,第2阶段对列车的停站时间进行调整.将3种有效的搜索算子、一种自适应更新规则与粒子群算法相结合,以列车延迟率作为优化目标,求解单线铁路列车调度问题.通过将所提算法与其他算法在相同实验条件下进行测试对比,并进行突发事件测试,验证了所提算法的有效性.     

基于粒子群算法的列车运行调度研究

列车运行调度是铁路行车调度指挥工作的重要内容,其计算机自动计算求解问题是我国铁路信息化建设的核心技术和难点。建立了高速列车运行调度的数学模型,采用粒子群算法求解。设计的算法步骤是,将列车运行调整问题描述成适合粒子群算法的形式,确定该问题的适应度函数,对基本问题模型进行改进,进而确定运动方程。以京广高速铁路线路内具有6个车站的下行方向,运行14列列车为例,利用设计的粒子群算法求解了某干扰条件下的列车运行调度问题,为列车运行调整提供了新的方法。     

Application of the analytical hierarchy process for real-time scheduling and part routing in advanced manufacturing systems

This paper presents a framework that employs the analytical hierarchy process (AHP) in advanced manufacturing systems for real-time scheduling and part routing. The proposed multicriteria decision-making framework brings a new perspective to real-time scheduling and part routing decisions, by implementing pairwise comparison of possible future states of a manufacturing system. The framework includes an extended finite state machine and a scheduler model to facilitate dynamic, short-term decision making. The scheduler model, which is developed on the basis of control theory, uses AHP to assess possible future states in a limited look-ahead horizon by comparing the performance measures of each state. The multicriteria decision-making framework developed in this study is implemented in a simulation environment to validate it for real-time manufacturing system control and investigate its performance under a range of look-ahead horizons. Simulation results indicate that the proposed framework performs better for a mid-range horizon for most of the commonly used performance measures.     

A feasible timetable generator simulation modelling framework for train scheduling problem

An important problem in management of railway systems is the train scheduling/timetabling problem. This is the problem of determining a timetable for a set of trains that do not violate track capacities and satisfy some operational constraints. In this study, a feasible timetable generator framework for stochastic simulation modelling is developed. The objective is to obtain a feasible train timetable for all trains in the system. The feasible train timetable includes train arrival and departure times at all visited stations and calculated average train travel time. Although this study focuses on train scheduling/timetabling problem, the developed simulation framework can also be used for train rescheduling/dispatching problem if this framework can be fed by real time data. The developed simulation model includes stochastic events, and can easily cope with the disturbances that occur in the railway system.     

开放式实时环境中的集成调度框架

开放式实时环境中的多类型实时任务并存的情况给实时调度机制带来了新的需求和挑战．提出了一种适用于开放式实时环境的3层集成调度框架，它基于服务器方式的实时调度理论，把5种既有相关性又有功能互补特征的服务器有机的集成到统一的调度机制中．可支持多种类型的实时任务及非实时任务共存于同一系统的混合调度，并且根据它们的属性自动选择最合适的服务器与之搭配．相对于现有方法，该调度框架可支持的调度对象类型更广泛，易于进行服务质量控制，并且有良好的可扩展性．因此，更适合于开放式实时环境．     

Train scheduling and circulation planning in urban rail transit lines

This paper proposes a two-stage optimization approach to optimize the train schedule and circulation plan with consideration of passenger demand for an urban rail transit line. A train scheduling model is based on the operation of train services, which results a mixed integer nonlinear programming problem. Moreover, a train circulation model is formulated to adjust the departure and arrival times obtained by the train scheduling model to reduce the number of trains required, which results in a mixed integer linear programming problem. The case study based on the Beijing Yizhuang line illustrates the effectiveness of the proposed model and solution approach.     

基于UML和CPN的列控系统等级转换建模与分析

针对列控系统的安全性和实时性要求,基于CTCS-3级列控系统需求规范中等级转换场景建立C2级向C3级转换的UML(统一建模语言)模型和有色Petri网(CPN)模型,分析了影响列车安全运行和行车效率的因素,即转换时长和转换成功率,验证了该建模方法的有效性。验证结果表明,UML和CPN模型相结合的方法适合于列控系统需求规范的验证。搭建的等级转换模型能够满足系统实时性要求。在保证切换成功率的前提下,列车运行速度与切换时间成反比,速度越高,切换时间越短;列车速度越高,对系统实时性要求也越高。     

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.     

改进WFQ算法在列车网络调度中的应用研究

随着我国轨道交通迅猛地发展,基于TCN的列车网络已经不能满足列车数据传输业务的需求,列车控制与服务网络TCSN应运而生。针对列车数据传输业务研究了基于综合服务模型的列车通信网络,建立了四类列车业务流量模型,分析了网络分组的加权公平队列调度算法（WFQ）,设计了基于服务分类和带宽分配的加权公平队列调度算法（SCBAWFQ）,并且利用仿真软件对恒定数据流和突发数据流分别在WFQ与SCBAWFQ算法下的时延进行了仿真与分析,仿真结果证明改进算法在降低实时业务流延时方面有很好的效果,SCBAWFQ算法既保持了WFQ算法的公平性又满足实时业务服务质量的需求。     

Feedback Control Real-Time Scheduling: Framework,Modeling, and Algorithms*

This paper presents a feedback control real-time scheduling (FCS) framework for adaptive real-time systems. An advantage of the FCS framework is its use of feedback control theory (rather than ad hoc solutions) as a scientific underpinning. We apply a control theory based methodology to systematically design FCS algorithms to satisfy the transient and steady state performance specifications of real-time systems. In particular, we establish dynamic models of real-time systems and develop performance analyses of FCS algorithms, which are major challenges and key steps for the design of control theory based adaptive real-time systems. We also present a FCS architecture that allows plug-ins of different real-time scheduling policies and QoS optimization algorithms. Based on our framework, we identify different categories of real-time applications where different FCS algorithms should be applied. Performance evaluation results demonstrate that our analytically tuned FCS algorithms provide robust transient and steady state performance guarantees for periodic and aperiodic tasks even when the task execution times vary by as much as 100% from the initial estimate.