钢铁企业集成化生产调度模型的研究

为了解决钢铁企业生产因缺乏整体生产调度计划造成的宏观调控和实时监控等方面次序混乱的问题,提出了分阶段、以关键生产阶段调度为核心、基于规则的分型生产调度模型,并对如何运用该模型实现集成调度进行了分析,最后对该模型在企业实际项目中的应用进行了概要介绍。应用结果表明,该调度模型对协调分厂间的生产、平衡生产物流、缩短生产周期起到了重要作用,实现了钢铁企业生产调度的整体优化。     

实时容错调度算法的自动模拟系统模型与设计

实时容错技术是实时系统中的关键技术之一,而实时容错调度算法则是实时容错研究领域中需要重点研究的问题.传统的实时容错调度算法的性能模拟是通过手工完成的,如果采用自动模拟方法,可以提高模拟实验的效率和准确性.首先给出一个实时容错调度算法的自动模拟系统模型,该模型定义了自动模拟系统中的关键参数.然后,提出了实时容错调度自动模拟算法.最后,研究了该自动模拟系统的软件结构.文章研究的自动模拟系统具重要的指导意义,可以参考该系统设计其他类型的调度算法的自动模拟系统.该系统的实现可大大节省用于研究实时容错调度算法的时间和费用.     

分时计算机随机服务系统的模拟

模拟技术可用于构造系统模型以预测系统在给定环境下的性能。本文基于排队系统的系统模拟之原理，提出了一个分时和前－后台优先级调度方式相结合的计算机随机服务系统的系统模型，产研制了模拟软件。     

网络控制系统的性能优化研究

网络控制系统(NCS)是基于网络的分布式控制系统。文章结合网络调度和系统控制,以网络的可调度性和系统稳定性为约束条件,给出了NCS的性能优化模型,并利用基于Matlab/Simulink的实时控制系统仿真软件包True Time对其进行了仿真。仿真结果表明:该方法既提高了控制系统的性能,又优化了网络调度性能,提高了网络资源的利用率。     

Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment

This paper proposes a fuzzy rule-based system for an adaptive scheduling, which dynamically selects and applies the most suitable strategy according to the current state of the scheduling environment. The adaptive scheduling problem is generally considered as a classification task since the performance of the adaptive scheduling system depends on the effectiveness of the mapping knowledge between system states and the best rules for the states. A rule base for this mapping is built and evolved by the proposed fuzzy dynamic learning classifier based on the training data cumulated by a simulation method. Distributed fuzzy sets approach, which uses multiple fuzzy numbers simultaneously, is adopted to recognize the system states. The developed fuzzy rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop more effective and robust rules than the traditional job-dispatching rules and a neural network approach.     

多核架构下的多线程负载平衡

首先指出了在多核CPU时代的软件应用所面临的一个方面的问题:负栽平衡,接着给出了在原架构下的解决方案,然后结合一个实时数据库下的任务调度问题,根据多核CPU的多线程优势,设计出了一个调度应用方案,并为证明其有效性进行了测试.最后给出了需要改进的问题.     

Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations

One of the most challenging problems when facing the implementation of computational grids is the system resources effective management commonly referred as to grid scheduling. A rule-based scheduling system is presented here to schedule computationally intensive Bag-of-Tasks applications on grids for virtual organizations. There exist diverse techniques to develop rule-base scheduling systems. In this work, we suggest the joining of a gathering and sorting criteria for tasks and a fuzzy scheduling strategy. Moreover, in order to allow the system to learn and thus to improve its performance, two different off-line optimization procedures based on Michigan and Pittsburgh approaches are incorporated to apply Genetic Algorithms to the fuzzy scheduler rules. A complex objective function considering users differentiation is followed as a performance metric. It not only provides the conducted system evaluation process a comparison with other classical approaches in terms of accuracy and convergence behaviour characterization, but it also analyzes the variation of a wide set of evolution parameters in the learning process to achieve the best performance.     

基于物联网大数据节能调度框架研究

当今社会,物联网的快速发展离不开数据的支撑,数据的持久化存储与检索离不开物联网的边缘服务器,边缘服务器相当于物联网的数据服务中心,同时也是云环境重要组成部分,因此数据中心的能耗问题成为核心问题。现今在边缘侧常用的大数据资源调度框架为YARN,该框架并不能对能耗进行有效控制,针对此问题,提出一种基于YARN的物联网大数据节能调度框架,该框架是在YARN基础上进行优化,增加三个关键性的功能模块,分别对系统任务进行分析、计算、调度、分配,实现了细粒化管理。通过模拟实验验证该框架能在保证系统任务性能的同时减少能量损耗。     

GA-based discrete dynamic programming approach for scheduling inFMS environments

The paper presents a new genetic algorithm (GA)-based discrete dynamic programming (DDP) approach for generating static schedules in a flexible manufacturing system (FMS) environment. This GA-DDP approach adopts a sequence-dependent schedule generation strategy, where a GA is employed to generate feasible job sequences and a series of discrete dynamic programs are constructed to generate legal schedules for a given sequence of jobs. In formulating the GA, different performance criteria could be easily included. The developed DDF algorithm is capable of identifying locally optimized partial schedules and shares the computation efficiency of dynamic programming. The algorithm is designed In such a way that it does not suffer from the state explosion problem inherent in pure dynamic programming approaches in FMS scheduling. Numerical examples are reported to illustrate the approach.     

视频点播系统中基于流行度的MBGC策略

视频节目的调度策略是视频点播系统中有效使用存储资源和网络资源的关键技术,优化的节目调度和管理可以极大的提高视频服务器的性能。根据流行度来划分视频节目的优先级,提出了一种视频节目调度算法——可移动边界保护信道法(MBGC),并设计了一个二维的Markov链对算法的性能进行了理论上的分析,最后的数值分析结果表明,提出的算法提高了系统的性能。     

遗传算法在短期发电优化调度中的研究与应用

将遗传算法用于解决梯级水电系统短期发电优化调度问题。建立了短期发电优化调度模型，详细阐述了用遗传算法解决问题的步骤。实例证明，该方法能够求解具有复杂约束条件下的非线性优化问题，算法编程简洁，易于实现，从而为梯级水电站短期发电优化调度问题提供了一种有效的解决方法。     

强化学习和仿真相结合的车间作业排序系统

设计了一个强化学习和仿真相结合的动态实时车间作业排序系统．首先引入多个随机变量，将车间作业排序问题转换成序贯决策问题；然后通过仿真手段构建车间作业排序问题的模型环境，求取系统性能指标并保证解的可行性；接着设计了一个多智能体Q学习算法和仿真集成解决作业排序问题；最后通过仿真优化实验验证了该系统的有效性．     

Performance evaluation of bag of gangs scheduling in a heterogeneous distributed system

Distributed systems deliver a cost-effective and scalable solution to the increasing performance intensive applications by utilizing several shared resources. Gang scheduling is considered to be an efficient time-space sharing scheduling algorithm for parallel and distributed systems. In this paper we examine the performance of scheduling strategies of jobs which are bags of independent gangs in a heterogeneous system. A simulation model is used to evaluate the performance of bag of gangs scheduling in the presence of high priority jobs implementing migrations. The simulation results reveal the significant role of the implemented migration scheme as a load balancing factor in a heterogeneous environment. Another significant aspect of implementing migrations presented in this paper is the reduction of the fragmentation caused in the schedule by gang scheduled jobs and the alleviation of the performance impact of the high priority jobs.     

Development of a simulation-based decision support system for controlling stochastic flexible job shop manufacturing systems

This paper describes a simulation-based decision support system (DSS) to production control of a stochastic flexible job shop (SFJS) manufacturing system. The controller design approach is built around the theory of supervisory control based on discrete-event simulation with an event–condition–action (ECA) real-time rule-based system. The proposed controller constitutes the framework of an adaptive controller supporting the co-ordination and co-operation relations by integrating a real-time simulator and a rule-based DSS. For implementing SFJS controller, the proposed DSS receives online results from simulator and identifies opportunities for incremental improvement of performance criteria within real-time simulation data exchange (SDX). A bilateral method for multi-performance criteria optimization combines a gradient based method and the DSS to control dynamic state variables of SFJS concurrently. The model is validated by some benchmark test problems.     

分布式系统TTCAN调度优化算法研究

总线技术的发展给线缆测试仪带来了分布式、信息化、网络化的新需求,且在分布式线缆测试仪工作过程中,测试线路的数目增加也对总线数据通讯的稳定性和通讯效率提出了更高的要求。针对分布式系统在线缆测试中的应用需要,设计并优化了分布式线缆测试仪工作的TTCAN应用层协议和其系统矩阵。对于分布式系统通信中的周期性消息形成的系统矩阵先后采用遗传算法、改进型差分进化算法进行优化,对于其中的非周期性消息采用基于松弛度的动态优先级算法。在MATLAB仿真环境中进行实验,实验结果表明,改进型差分算法比遗传算法能够更快、更稳定地计算出优化矩阵,经调度优化后的TTCAN总线工作时数据传输效率有显著提高。论文通过智能优化算法,有效提高了系统总线的通讯效率和稳定性。     

云计算中基于排队系统的任务调度模型研究

为缩短云计算中任务调度过程任务等待时间及提高虚拟机任务调度系统的执行效率,提出一种云环境下基于 排队系统的任务调度模型。对该模型中系统稳态分布和条件随机分解结果进行了分析,给出该模型的稳态队长的随机分解和稳态等待时间,结合数值例子,准确的找到服务率与期望队长、期望等待时间及其它性能指标之间的关系。通过云任务调度系统的仿真,实验结果验证了该模型能够快速地完成云任务的调度,提高了虚拟机资源的平均利用率。     

An adaptive scheme for predicting the usage of grid resources

In order to manage the grid resources more effectively and provide a more suitable job scheduling strategy, the prediction information is needed for applications in the Grid computing system, such as the high performance computing and sharing computational resources, etc. In this paper, we propose a prediction system that can predict most information in the grid environment. Whether the repetitive time series pattern of the information exists or not, the proposed system can provide prediction results. We label the environment information in the grid and use the periodicity detector to detect the iterative patterns. The detected patterns can be used to predict several future values. Before the repetitive patterns have been found, a simple scheme that does not require a lot of resource has been used to generate prediction values. A prototype of this model is developed and tested with several test cases. The experimental results by the simulation show that our prediction system is able to capture different kinds of time series patterns and provide accurate prediction for the grid environment.     

Job-Shop调度问题的仿真研究

Job-Shop是离散事件系统中典型的调度问题，通过计算机仿真能够动态地展现Job-Shop车间的状态，分析在不同调度方法下的系统性能，并运用知识和经验去选择合适的调度方法，从而改善调度性能。本文构造了Job-Shop的离散事件仿真模型，设计了基于事件驱动的仿真策略，并讨论了优先调度准则，为解决这一问题提供了一种现实可行的思路与方法。     

Modeling, scheduling and simulation of product development process by extended stochastic high-level evaluation Petri nets

This paper presents a methodology for modeling and simulating product development process-based on the extended stochastic high-level evaluation Petri nets (ESHLEP-N). A product development process is composed of many design activities and the ESHLEP-N model can describe some special features of design activity in detail, such as randomness of its duration, uncertainty of its interruption and complexity of design iteration. Therefore, the ESHLEP-N model is employed to simulate a product development process. The initial product development plan obtained by a mathematical method beforehand is taken as the input of the simulation. Then the simulation procedure is proposed, along with four types of rules, i.e. activity-sequencing rules, resource-assigning rules, state-changing rules and the simulation-terminating rule, for scheduling the design activities. An example of the development process of an automobile drive system in concurrent engineering environment is presented to illustrate the method of the ESHLEP-N-based modeling, simulation procedure and scheduling rules. The simulation results show that the simulation procedure and the scheduling rules are effective.     

Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model

Due to the dynamic nature, complexity, and interactivity of production scheduling in an actual business environment, suitable combined and hybrid methods are necessary. This paper takes prefabricated concrete components as an example and develops the dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) to optimize and simulate the production scheduling. First, a multiobjective genetic algorithm is integrated into the MAS for preliminary optimization and a series of near‐optimal solutions are obtained. Subsequently, considering the resource constraints and uncertainties, the MAS is used to simulate complex real‐world production environments. Considering the different types of uncertainty factors, the paper proposes the corresponding dynamic scheduling method and uses MAS to generate the optimal production schedule. Finally, a practical prefabricated construction case is used to validate the proposed model. The results show that the model can effectively address the occurrence of uncertain events and can provide dynamic decision support for production scheduling.