基于扩展随机高级判断Petri网的FMS调度和仿真

本文提出一种新的更适合FMS调度仿真的扩展随机高级E-net,简称ESHLEP-N(Extended stochastic High Level Evluation Petri Net)。以ESHLEP-N模型为基础,设计并建立了FMS调度仿真专家系统。借助于该系统提出并研究了新的旨在减少空闲(no-inputs)和阻塞的实时动态再调度规则。经过仿真,证明新规则的产率(throughput)比传统规则平均提高6.48％。本文的部分成果已经用于航空航天部159厂的高级DNC系统的设计之中。     

FMS的实时调度与控制方法研究

本文以 CIMS 中的基本单元 FMS 为对象,从离散事件动态系统角度出发,研究了 FMS 的实时调度与控制.首先,从递阶控制角度出发讨论了 FMS 的调度与控制,把一般的 FMS 的调度与控制系统划分成三层:调度层、事件轨迹控制层和单元逻辑控制层,提出了 FMS 调度与控制的一体化结构——调度驱动监控结构.在此基础上,建立了 FMS 基于 Petri 网的调度驱动监控的形式化理论.     

柔性制造系统实时调度的有色Petri网和状态方程模型

柔性制造系统(FMS)的实时调度问题是一个典型的并发系统(CA),亦称为离散事件动态系统(DEDS).这是一类很复杂的动态系统,本文尝试用Petri网和有色Petri网对FMS的实时调度问题建立动态模型并同时给出了代数形式的状态方程模型.     

基于专家系统的入侵检测技术

提出了一种基于专家系统的入侵检测系统基本框架,该系统综合了基于主机和网络的两种结构特点,利用专家系统进行规则匹配技术来实现入侵检测.该技术有利于系统准确、实时地检测出攻击行为,并可给出详细的解释说明信息,而基于规则又具有模块化特征,使得知识容易封装并不断扩充,并且使系统可方便地实现对新的攻击特征的检测.     

FMS物流系统的调度与控制

本文针对FMS物流系统，提出了一种基于任务分类的逐层细化调度与控制策略。并在上海第四机床厂箱体零件柔性制造系统（SJ－FMS）中加以具体应用和软件实现。结果表明，这种调度与控制策略能够有机地将调度的计划性和随意性相结合，保证控制的实时性，具有实用价值。     

基于规则的专家调度法在镀锡生产中的应用

流程工业的生产调度是流程工业CIMS中的一类重要问题,对其实现具有重要意义.在叙述了流程工业CIMS的生产调度方法的基础上,针对上海宝钢宜昌薄板有限公司的镀锡生产调度的实际问题,提出使用基于规则的专家系统对其镀锡生产进行调度,并讨论了基于规则的专家系统的调度方法在镀锡生产调度实现过程中规则获取、知识形成、推理方法设计的实现方法.最后,根据实际生产调度数据给出了使用该调度方法的调度系统在镀锡生产调度中的应用结果.     

基于Petri网的柔性制造系统作业调度研究与实现

选择面向对象的有色Petri网对FMS进行建模,研究FMS调度控制问题.本文针对柔性制造系统的特点,采用面向对象的着色Petri网建立了由工件、机床、运送和仓贮对象等组成的模型.模型是一个包括加工系统、物流系统、管理系统在内的一个完整的FMS模型.该模型由于采用了面向对象技术,具有可移植性、可扩展性的特点,并且易于用软件去实现,本文使用"映射"法,把库所映射为软件数据、变迁映射为程序函数和方法、子系统映射为软件系统基本类,从而把FMS Petri网模型转化为FMS控制管理软件程序.本文采用简易的工艺文件数据,对FMS控制管理软件运行进行了测试.通过对测试结果的分析,验证了本文所建立的FMS物流系统模型和设计的FMS柔性生产线控制管理软件的正确性和有效性.     

基于动态通信调度的网络化系统最优控制

针对存在介质访问约束的网络化控制系统,研究其中的控制和通信协同设计问题.将网络化控制系统建模为输入矩阵随介质约束而变化的切换系统,运用最优控制理论和切换系统分析方法,设计了可动态调度的最优控制器及实时切换规则,并分析了动态调度下闭环网络化控制系统的稳定性,最后通过仿真验证了该方法的有效性.     

多核环境下基于图模型的实时规则调度方法

安全攸关反应式系统的核心要求是：必须在指定时间期限内完成对外部事件的检测和目标事件的响应,否则会产生灾难性的后果.随着安全攸关反应式系统对智能化需求的日益增加,将规则推理应用于这类系统成为必然趋势.规则调度是保证规则推理硬实时约束的关键.为此,提出了一种基于图模型的实时规则调度方法（graph-based real-time rule scheduling,简称GBRRS）.该方法对基于事件图的实时规则推理过程进行建模,提出了基于图的端到端推理任务模型,并给出了端到端推理任务的调度算法,保证了规则调度的安全性.采用模拟实验对GBRRS方法进行了验证,实验结果表明,与DM-EDF方法（通过直接映射把规则上的推理操作转成推理任务后,用全局EDF算法对其进行调度的方法）相比,GBRRS方法在规则调度成功率上平均高出13%~15%,且在规则集的平均负载较高时,仍保持着80%以上的调度成功率.     

基于专家系统的入侵检测技术

提出了一种基于专家系统的入侵检测系统基本框架，该系统综合了基于主机和网络的两种结构特点．利用专家系统进行规则匹配技术来实现入侵检测。该技术有利于系统准确、实时地检测出攻击行为，并可给出详细的解释说明信息．而基于规则又具有模块化特征，使得知识容易封装并不断扩充，并且使系统可方便地实现对新的攻击特征的检测。     

Real time fuzzy scheduling rules in FMS

This paper presents a real-time fuzzy expert system to scheduling parts for a flexible manufacturing system (FMS). First, some vagueness and uncertainties in scheduling rules are indicated and then a fuzzy-logic approach is proposed to improve the system performance by considering multiple performance measures. This approach focuses on characteristics of the system's status, instead of parts, to assign priorities to the parts waiting to be processed. Secondly, a simulation model is developed and it has shown that the proposed fuzzy logic-based decision making process keeps all performance measures at a good level. The proposed approach provides a promising alternative framework in solving scheduling problems in FMSs, in contrast to traditional rules, by making use of intelligent tools.     

The effect of responsiveness of the control-decision system to the performance of FMS

In Flexible Manufacturing System (FMS), decision-making process is one of the key aspects for its performance enhancement, particularly for shop-floor control, where operation managers need to make a large number of control decisions. A term called ‘response-time’ in decision-making process has been defined in this study, which refers to a lead time in decision-making and its implementation. This paper contributes a methodology for decision-maker to study the decision-making process and identify a suitable decision-making approach, while considering critical factors such as decision automation levels, routing flexibility levels, and control strategies. Considering the complexity in modelling an FMS with routing flexibility levels, control strategies (sequencing and dispatching rules), and decision-making process with information system, computer simulation modelling has been employed to study the makespan performance. The results show that decision-making process with response-time for FMS control performs as good as with the real-time control when routing flexibility level is low. Furthermore, under some specific situations, it even outperforms the real-time control. This research gives insight to decision-maker to identify whether a decision system with response-time will be more suitable and economically justified, or real-time decision-making system is more appropriate.     

Trace-driven knowledge acquisition (TDKA) for rule-based real time scheduling systems

Expert scheduling systems, which develop the schedule automatically on a real time basis, are able to respond to the changes of product demand in Flexible Manufacturing Systems (FMS). While developing an expert scheduling system, the most time-consuming and difficult step is knowledge acquisition, the process that elicits the knowledge from experts and transfers it into the knowledge base. A trace-driven knowledge acquisition (TDKA) method is proposed to extract the expertise from the schedules produced by expert schedulers. Three phases are involved in the TDKA process: data collection, data analysis, and rule evaluation. In data collection, the expert schedulers are identified and decisions made during the scheduling process are recorded as a trace. In data analysis, a set of scheduling rules is developed based on the trace. The rules are then evaluated in the last phase. If the resulting rules do not perform as well as the expert schedulers, the process returns to phase two and refines the rules. The whole process stops whenever the resulting rules perform at least as well as the expert schedulers. A circuit board production line is used to demonstrate the feasibility of the TDKA methodology. The scheduling rules perform much better than the expert schedulers from whom the rules are extracted.     

仿真调度中的辅助决策系统

仿真与人工智能相结合是解决调度问题的重要手段，本文重点讨论了一体化仿真软件辅助决策系统的设计与实现，该辅助决策系统采用仿真、优先规则和专家系统技术实现了对ＦＭＳ日生产计划和调度的决策支持。本文还对三个实例进行了分析。     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

混合遗传算法在柔性系统动态调度中的应用研究

本文研究了柔性制造系统实时生产环境下的动态调度问题.提出了基于动态数据库技术的动态调 度系统的框架结构.动态数据库中存储着问题的数据结构,包含工件相关类与机器相关类信息.动态数据库能 够随着生产的进行及时进行更新.扰动发生后,遗传算法根据动态数据库所提供的更新后的调度任务数据,快 速产生新的优化调度方案.通过在遗传算法中嵌入约束解决机制确保遗传算法适应约束的能力,从而提高算 法的收敛速度与精度.仿真实验证实了方案的有效性.     

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.     

A simulation analysis of part launching and order collection decisions for a flexible manufacturing system

In a dynamic flexible manufacturing system (FMS) environment jobs arrive randomly, and in most of the existing studies the due date for a single part is set individually. However, when the due date is set for an order that consists of multiple parts, some completed parts may have to wait for the rest of the order to be completed. This paper studied the scheduling problem in the FMS in which orders require the completion of different parts in various quantities. The orders arrive randomly and continuously, and all have predetermined due dates. Two scheduling decisions were considered in this study: launching parts into the system for production, and determining the order sequence for collecting the completed parts. A new part-launching rule, named the Tardiness Estimating Method (TEM) was proposed. A discrete-event simulation model of the FMS was developed and used as a test-bed for experiments under various system conditions. The proposed part launch rule was capable of providing good performance regarding minimum mean tardiness and maximum service level, but provided only a moderate flow time when compared with the other five rules commonly used in the literature. In addition, three order collection rules were tested in the experiments. Collecting parts for the order with the earliest due date (EDD) was found better than the other rules for tardiness related measures.