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.     

Digital twin-based job shop anomaly detection and dynamic scheduling

Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.     

Human performance in industrial scheduling: A framework for understanding

Planning and scheduling activities have a significant impact on the performance of manufacturing enterprises. Throughout the 1980s there was a belief that computer‐led solutions would “solve” complex industrial planning and scheduling problems. However, in most manufacturing organizations, planning and scheduling still require significant human support to ensure effective performance. Although the contribution of these human resources is often highly valued, we are only beginning to develop a coherent body of knowledge that can contribute toward the successful integration of human and computer‐based planning and scheduling systems. Here we examine the state of knowledge in this domain and identify the need for field investigations. We present a framework to facilitate research in human and organizational issues in planning and scheduling in manufacturing. A structured and detailed set of research questions is developed to underpin field studies. The framework focuses on understanding the scheduling environment, the process of scheduling, and related performance issues. The application of the framework is illustrated using our own field studies, where a number of specific research questions of practical importance have been identified: what scheduling is, who carries it out, what influences scheduling practice and performance, how schedulers actually schedule, what makes a good scheduler and schedule, and what support is needed. The framework makes a valuable contribution to advancing knowledge in an area of real practical benefit to contemporary manufacturing industry. © 2001 John Wiley & Sons, Inc.     

A formally verified application-level framework for real-time scheduling on POSIX real-time operating systems

We present a framework, called meta scheduler, for implementing real-time scheduling algorithms. The meta scheduler is a portable middleware layer component designed for implementations over POSIX-compliant operating systems. It accommodates pluggable real-time scheduling algorithms while offering the flexibility of platform independence - the singular underlying OS requirement is the now nearly ubiquitous POSIX compliance. The versatility of pluggable schedulers positions the meta scheduler for deployment in an interoperable heterogeneous real-time environment. We present the design of the meta scheduler and outline its implementation. Furthermore, we present a mechanized correctness verification using the UPPAAL model checker. Prototype implementation of the meta scheduler over QNX Neutrino real-time operating system demonstrates high performance and a small footprint.     

可重配置实时任务的快速动态调度算法

要提出一种快速动态定位和实时任务调度算法,采用最早最迟开始时间优先、最优化空白区域管理和配置重用的调度原则,能够反映实时任务的紧迫度,快速地调度实时可配置硬件任务,同时有效地管理可重构资源．实验结果表明,该算法可以有效地提高系统的总体性能．     

基于Linux内核的实时调度机制研究及应用

在实时系统中,任务调度策略是内核设计的关键部分,如何进行任务调度,保证各个任务能按要求完成是实时操作系统研究的一个重要领域。针对RT-LINUX调度器在系统负载较重或过载时调度性能急剧下降的缺点,提出了一个基于价值调度的可适应算法以较少的最后期限错失来达到一个高的系统价值。根据它的两个执行标准“价值比率”和“成功比率”评估该调度程序的仿真和应用。     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

实时控制任务的模糊反馈调度系统研究

分析了实时控制任务的控制性能在不同控制阶段与处理器利用率需求间的关系,提出一种实时控制任务的模糊反馈调度系统.模糊控制器通过监测实时控制任务的误差及其变化率,查询模糊决策表,动态决定任务的优先级,反馈调度器根据优先级分配任务的利用率.仿真结果表明，在计算资源有限时，该方法能有效改善实时控制任务的控制性能.     

Operations planning and scheduling problems in an FMS: An integrated approach

Operations planning and scheduling (OPS) problems in advanced manufacturing systems, such as flexible manufacturing systems, are composed of a set of interrelated problems, such as part-type batching, machine grouping, tool loading, routing, part input sequencing and on-line scheduling. In this paper, an integrated, simulation-based approach to the OPS problems is discussed. A detailed simulation model is developed using FORTRAN and SLAM II which integrates loading, part inputting, routeing and dispatching issues of the OPS. An experimental frame is developed which provides statistical analysis of the simulation output by developing an experimental design. Statistical analyses concerning a number of system parameters (e.g. loading strategies and scheduling rules) are performed on a set of performance measures.     

A simulation-based approach for dynamic process management at web service platforms

Web services technology is being adopted as a viable deployment approach for future distributed software systems that enable business-to-business and business-to-consumer interactions across the open and dynamic internet environment. Recent research is focused on developing support technologies for web service discovery, on-demand service composition, and robust execution to facilitate web services based deployment of business processes. Developing techniques to cope with the volatile and open nature of the web during execution of composite services at the service platform is essential for delivering reliable and acceptable performance in this new process delivery framework. In this paper, we propose a simulation-based framework to guide scheduling of composite service execution. Online simulation of the dynamics of the open environment is used for scheduling service requests at the service platform. Comparison of the look-ahead simulation for different scheduling policies with the current execution state provides guidelines for service execution in order to cope with system volatility. We have implemented a prototype of the proposed framework and illustrate the feasibility of our approach with experimental studies.     

基于滚动变时间窗的重组批处理机调度研究

针对具有等待时间限制和工件动态到达的重组批处理机调度问题,以拖延时间和最小为目标,提出基于滚动变时间窗的三层混合调度算法。该调度算法是应用滚动时域策略,将重组批处理机调度问题分解为许多变时间窗的子问题;每个子问题调度分三层执行：即产生触发并传递参数、重组批及排序、派工并更新参数。通过实时调度仿真平台和CPLEX平台进行实例验证,结果表明基于滚动变时间窗的三层混合调度算法能够在较短计算时间内获得满意优化解。     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

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.     

晶圆生产线瓶颈区并行机半在线调度方法

为提高晶圆生产线瓶颈区生产率,提出一种瓶颈区并行机半在线调度方法.该方法基于晶圆制造的"多重入"特点,充分利用生产历史信息、可预知信息及实时信息,分析了瓶颈区短期内的重入流来源,给出了瓶颈区任务集的预测方法.针对非零初始状态的生产线,设计了以最小化瓶颈区平均加权拖期时间为优化目标的蚁群优化算法,定义了并行机任务集析取图,并给出了半在线调度方法的应用算例,为实际生产管理提供了可行的优化方法.     

柔性制造系统的改进粒子群无死锁调度算法

基于柔性制造系统的Petri网模型,以制造期最小为优化目标,将死锁避免策略嵌入粒子群算法中,提出一种无死锁改进粒子群调度算法.该算法将粒子与工件的工序序列相对应,以位置数值的大小表示对应工件工序在执行顺序中的优先级.采用一步向前看的死锁避免策略方法对序列的可行性进行验证,提出一种跳出局部极值的策略.实例仿真结果表明了粒子群调度算法的可行性和有效性,以及改进粒子群调度算法的优越性.     

大规模短时间任务的低延迟集群调度框架

大规模数据分析环境中,经常存在一些持续时间较短、并行度较大的任务。如何调度这些低延迟要求的并发作业是目前研究的一个热点。现有的一些集群资源管理框架中,集中式调度器由于主节点的瓶颈无法达到低延迟的要求,而一些分布式调度器虽然达成了低延迟的任务调度,但在最优资源分配以及资源分配冲突方面存在一定的不足。从大规模实时作业的需求出发,设计和实现了一个分布式的集群资源调度框架,以满足大规模数据处理的低延迟要求。首先提出了两阶段调度框架以及优化后的两阶段多路调度框架;然后针对两阶段多路调度过程中存在的一些资源冲突问题,提出了基于负载平衡的任务转移机制,从而解决了各个计算节点的负载不平衡问题;最后使用实际负载以及一个模拟调度器对大规模集群中的任务调度框架进行了模拟和验证。对于实际负载,所提框架的调度延迟控制在理想调度的12%以内;在模拟环境下,该框架与集中式调度器相比在短时间任务的延迟上能够减少40%以上。     

基于Petri网的分布式实时调度模型研究

Petri网作为一种可视化的规格语言,越来越多地用于实时系统的评估和分析。该文提出了一种基于Petri网的分布式实时系统模型,并对该模型中的局部调度器和消息调度器进行了描述。根据该模型可以开发相应的分布式实时调度模拟器,这样就可以在系统的开发初期,利用模拟器来验证在给定的局部调度策略和消息调度策略下,系统任务的时间约束是否能够得到有效的保障。同时该模型还可以很容易地转化为系统的快速原型。     

Pragmatic integrated scheduling for clustered VLIW architectures

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Scheduling for clustered architectures involves spatial concerns (where to schedule) as well as temporal concerns (when to schedule). Various clustered VLIW configurations, connectivity types, and inter‐cluster communication models present different performance trade‐offs to a scheduler. The scheduler is responsible for resolving the conflicting requirements of exploiting the parallelism offered by the hardware and limiting the communication among clusters to achieve better performance. In this paper, we describe our experience with developing a pragmatic scheme and also a generic graph‐matching‐based framework for cluster scheduling based on a generic and realistic clustered machine model. The proposed scheme effectively utilizes the exact knowledge of available communication slots, functional units, and load on different clusters as well as future resource and communication requirements known only at schedule time. The proposed graph‐matching‐based framework for cluster scheduling resolves the phase‐ordering and fixed‐ordering problem associated with earlier schemes for scheduling clustered VLIW architectures. The experimental evaluation in the context of a state‐of‐art commercial clustered architecture (using real‐world benchmark programs) reveals a significant performance improvement over the earlier proposals, which were mostly evaluated using compiled simulation of hypothetical clustered architectures. Our results clearly highlight the importance of considering the peculiarities of commercial clustered architectures and the hard‐nosed performance measurement. Copyright © 2007 John Wiley & Sons, Ltd.     

面向安全关键实时应用的分层防危调度算法研究

针对现有防危调度算法在软硬件失效情况下防危能力不足的问题,具体进行了以下工作:构建了一种分层防危实时调度模型,该模型从功能组件和安全分区两方面描述了安全关键实时应用的防危性需求,并给出一种基于分层调度思想的三级防危调度器框架。以该模型和框架为基础,提出了一种新的分层防危调度算法(HSS),该算法对安全关键实时应用中不同关键度的功能组件采用空间隔离机制,对同一功能组件内的不同分区采用时间隔离机制,兼顾实现了时空隔离的防危效果。仿真实验结果表明,HSS算法与其他同类算法相比,在防危效果和应用负载承受能力方面具有较好的表现。     

An integrated scheduler for manufacturing planning and control (INSCH)

The most basic problem in a manufacturing process is to create valid scheduling system which determines the sequence of jobs to be processed at each of the series of machine centers. An integrated scheduler (INSCH) is developed for small job shop manufacturing systems while considering high machine utilization, low work-in-process, and reduced job lateness.

As an and to understand the interaction of live jobs with the shop, sequence scheduler is developed to complement production scheduler such as Gantt bar chart. INSCH can achieve better performance than simple static models as shown in an example. It is desirable for INSCH to be applied to small job shop manufacturing companies using micro personal computer with relevant modifications discussed.