基于因素空间的规则调度决策模型

为解决在开发生产调度仿真系统时所遇到的复合调度规则仿真和复合规则决策问题,提出了一种引入决策者偏好信息的生产调度决策模型.在模型中,采用了基于因素空间的知识表示方法,并在此基础上提出了一种基于变权综合函数的调度决策方法,使得该模型不但可以表达模糊调度规则中的模糊概念,而且适用于复杂调度环境下的复合规则决策过程.偏好信息的引入,使得决策结果体现了决策者的意愿和兴趣,调度仿真的结果更加贴近真实情况,从而为决策者提供了令人满意的决策支持.最后,通过一个实际调度决策问题,对模型进行了验证与评价,结果表明了模型的有效性和可行性.     

基于损益云模型的制造车间重调度决策方法

针对制造车间重调度触发机制问题,建立了制造车间重调度损益函数,揭示了生产车间重调度过程损失及增益的变化规律.引入云理论测度重调度损益的不确定性,使用逆向云算法计算重调度增益云和损失云的数字特征,根据云形态预测重调度损益变化趋势.提出一种基于损益云模型的重调度决策方法以判断是否需要重调度,并利用最佳损益比甄选预调度方案以兼顾生产系统的稳定性和有效性.最后,通过实例验证了该方法的合理性和实用性.     

一类复杂生产调度问题的基于符号演绎的调度方法

针对带有时问不确定件的复杂生产过程调度问题,提出一种基于符号演绎的调度方法.首先将时间的不确定性信息看作符号型数据,并提出一种用于处理这些符号型数据的基于不确定区间的符号演绎方法;然后将此符号演绎方法与遗传算法相结合,提出一种预排调度计划与实时调度规则相结合的调度方法来求解上述复杂生产调度问题.实验表明,将基于符号演绎的调度方法用于求解带有时间不确定性的复杂生产过程调度问题,能够取得较好的调度效果.     

基于MAS的动态生产调度与控制及系统开发

提出基于MAS的面向敏捷制造的生产过程动态调度与控制的层次结构．1）以任务分解与分配层为中心，建立各层之间的协调工作及协同决策机制；2）引入协商式招／投标方法实现任务的分解与分配；3）采用能力匹配与动态调度相结合的方法实现任务分配与调度控制的有效集成；4）面向生产任务需求动态确定Agent粒度、组建MAS模型；5）适应制造系统状态变化的需要，进行任务的动态重构．讨论基于MAS的采用分级递阶和并行处理相结合的自治组织结构和运作模式，以及利用与组织结构相对应的层次黑板结构实现各Agent之间信息与数据共享．在支持生产过程动态调度与控制基础设施建设的基础上，结合奏川机床集团有限公司车间生产实际，研究开发了基于MAS的车间动态调度系统．     

基于超网络的集成工艺决策与调度问题研究

针对多工艺产品的加工路线决策与车间调度方案不能同步制定的问题,在制造车间数字化背景下,提出集成车间不同要素信息的特征—工序—机器—工人的超网络结构,建立基于超网络的加工路线决策与车间调度模型,设计一种集成工艺决策与车间调度的两阶段混合遗传算法求解模型。在工艺决策阶段,设计特征—工序双层矩阵编码染色体保持加工路线的多样性,并在遗传算法的执行过程中使用变邻域搜索方法增强算法的局部搜索能力;在车间调度阶段,采用NSGA-Ⅱ算法优化调度模型,将得到的调度方案多目标值返回至工艺决策阶段用于加工路线的适应度评价。最后通过仿真实验验证了该算法的可行性与有效性。     

弹性资源约束的动态调度决策

针对产品开发过程中由于活动变化导致原调度需要重新调整的问题,提出一种利用弹性资源特性进行动态测度决策的方法.以活动添加的变化为对象,根据活动间时序约束、资源约束等特点,分别构建时序冲突消解模型以及基于资源推拉技术的资源冲突消解模型.为提高活动冲突消解效率,提出一种虚拟前置活动和虚拟后续活动的构建方法,并确定动态调度决策指标值.最后通过船舶分段制造实例,验证了弹性资源约束的动态调度决策方法的可行性和有效性.     

基于新型制造资源组织模型的生产调度系统研究

为了适应航天制造企业对生产调度系统的要求,提出了新型的制造资源组织模型—–基于虚拟制造单元的制造资源组织模型,探讨了此模式的理论思想以及基于此模型构建的生产调度系统.首先构建了基于虚拟制造单元的资源组织模型结构,并给出虚拟制造单元构建的详细过程;然后,建立了基于此制造资源组织模型的生产调度系统模型;最后,通过应用实例表明了该系统能够有效地简化生产调度过程,提高生产效率.     

炼钢MES计划调度一体化

阐述了计划层、执行层到控制层一体化,时间到空间一体化,时间和空间综合一体化,即计划调度"三个一体化"理念.针对炼钢MES计划调度的复杂性,提出了基于数据、知识、模型和人机协调的四维一体技术和方法.进而分析了一体化计划与调度协调框架多代理技术、基于区间多目标决策的工序组批建模技术、基于资源优化的计划模型协调技术等关键技术,对于实际应用具有一定指导意义.     

智能制造系统基于数据驱动的车间实时调度

智能制造系统采用大量先进的信息技术,为车间实时调度提供技术基础.各类信息技术在生产制造过程中的广泛应用使得制造系统积累了大量与生产调度相关的数据,因此,通过利用历史生产调度数据和智能装备收集到的实时生产数据,建立基于数据驱动的生产实时调度方法成为新型制造环境下实现高效调度的新思路.针对智能制造环境下的混合流水车间实时调度问题,提出基于BP神经网络的数据驱动的实时调度方法,从历史近优的调度方案中提取用于调度知识挖掘的样本数据,通过BP神经网络训练学习获取生产系统状态与调度规则的映射关系网络,并将其应用于生产在线实时调度.数值实验表明,所提出的方法优于固定单一调度规则,在不同的调度性能指标下其效果均稳定且良好.     

P2P流媒体系统层次关系模型及其优化

当前流媒体系统的研究主要从数据调度层及覆盖网层内部各自展开,缺少对于层次间关系的建模描述及优化.为解决此问题,建立P2P流媒体层次间闭环控制模型,具体分析覆盖网层所提供信息的误差对数据调度层及系统整体性能的影响.并在此基础上提出基于数据调度反馈的增强型层次间交互模型,用以优化覆盖网维护.     

Multicycle Polling Scheduling Algorithms for FieldBus Networks

The paper deals with the scheduling of periodic information flow in a FieldBus environment. The scheduling problem is defined from an analytical point of view, giving a brief survey of the most well-known solutions. One of these is called multicycle polling scheduling, which is based on the hypothesis that all the production periods of the periodic processes to be scheduled are harmonic. Although in some process control or manufacturing scenarios, this hypothesis may be acceptable, there are many real industrial processes to which it cannot be applied. The aim of the paper is to make a contribution towards solving the scheduling problem. It essentially concerns extension of the theory on which multicycle polling scheduling is based to a much more realistic and general scenario, where the periods of all the processes to be scheduled have arbitrary values. The authors present a new formulation of multicycle polling scheduling, called extended multicycle polling scheduling, and demonstrate that it comprises the scenario currently considered in the literature. Two algorithmic solutions for extended multicycle polling scheduling are then proposed, giving a computational complexity analysis which will highlight the capability of the algorithmic scheduling solutions to be performed on-line. The paper concludes by comparing the multicycle polling scheduling approach known in literature and the one presented in the paper. Comparison is performed by evaluating the use of available bandwidth to serve both periodic and asynchronous traffic in the two approaches.     

P2P工作流系统的调度框架设计和实现

为了把参与者信息用于业务流程调度,同时给出基于流程实例迁移服务的一个可扩展性强的调度框架,提出了一个在P2P工作流系统上的流程实例调度框架.通过在框架中加入一个根据参与者位置调度的算法和一个负载均衡算法,证实了该框架的确能够针对业务流程的不同特点进行决策从而提高流程实例的执行效率.通过网页绑定服务和参与者绑定服务保证流程迁移后能继续和业务系统交互,表明了调度框架的实用性.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.     

On Task Scheduling Accuracy: Evaluation Methodology and Results

Many heuristics based on the directed acyclic graph (DAG) have been proposed for the static scheduling problem. Most of these algorithms apply a simple model of the target system that assumes fully connected processors, a dedicated communication sub-system and no contention for the communication resources. Only a few algorithms consider the network topology and the contention for the communication resources. This article evaluates the accuracy of task scheduling algorithms and thus the appropriateness of the applied models. An evaluation methodology is proposed and applied to a representative set of scheduling algorithms. The obtained results show a significant inaccuracy of the produced schedules. Analyzing these results is important for the development of more appropriate models and more accurate scheduling algorithms.     

基于制造网格的虚拟企业资源调度问题研究*

为支持虚拟企业间资源的高效调度,在制造网格的基础上,提出了虚拟企业的资源调度平台。研究了基于GlobusToolkit 4和agent技术的平台实现方法和工作流程。在此基础上,提出了以T、C、Q、S为目标的多目标资源优化调度模型,结合制造网格与计算网格的差异,将网格底层的跨地域实体物流交互对资源调度的影响纳入优化调度模型,使虚拟企业资源调度方案更加合理。最后,以一个具体的实例,验证了多目标优化调度模型的有效性。     

生产调度理论和方法研究综述

生产调度理论与方法研究是非常困难的课题，但是对企业或行业提高生产效率和效益是至关重要的，特别是在当前市场经济时代，生产调度理论和方法的研究已经有50多年的历史，但是经典调度理论和实际调度问题之间仍然存在着鸿沟。针对这一特点，指出了当前实际生产调度存在的一些问题和需要考虑的各种因素，例举了主要的生产调度方法和典型应用，指出了各种方法的优缺点。最后，总结并提出了生产调度理论和方法的未来研究方向和今后工作的建议。     

制造车间自动导引车调度新进展

随着制造企业生产自动化程度加深,自动导引车（AGV）成为运输和搬运环节的主角。近年来,制造车间AGV调度主要是建立双目标或多目标函数的优化模型,采用智能优化方法进行求解,其中遗传算法以广度搜索能力强的优势成为当今最常用的算法框架。另外,当今主流的还有混合算法,它使各种算法和算子的优势集中在一起,以得到更好的优化表现。就最新的制造车间AGV调度优化所研究的问题模型进行了归纳和总结,给出了主流的优化结果表现形式,并将求解优化模型主要采用的研究方法分为基于遗传算法框架的算法、其他智能优化方法和其他优化方法三大类进行讨论,在每一大类中提取重要的关键字以及交叉学科词汇进行汇总。在此基础之上总结出当今AGV调度研究中的两点不足之处,并结合当今的热点（大数据、人工智能等）对未来的研究方向提出了几条建议。     

动态生产调度管理系统的研究与实现

本文基于知识发现提出了一类动态调度算法的框架，在此基础上，基于数据仓库建立 了动态调度管理系统的体系结构，并采用面向对象（Object-Oriented，O-O）技术，分析 和设计了动态调度管理系统，最后阐明了所提方法的优点．