多组合设备的调度控制研究综述

在半导体芯片制造中越来越广泛使用多组合设备.本文介绍了多组合设备的结构配置、生产运行过程、调度控制问题的一般特征条件、应该满足的约束及周期性调度策略;分析了问题的复杂性因素.从多组合设备的结构特征、运行过程特征两方面分类综述了调度问题的建模、分析方法、调度算法及存在的问题,最后指出了未来的研究方向.     

基于时间约束集的集束型设备群调度方法

随着300mm晶圆的加工技术问世,工业界开始采用一种全新的晶圆制造设备——集束型设备群(Multi-cluster tools).对于单个集束型设备(Single-cluster tools)调度研究已比较成熟,并提出了多种调度方法,然而对于集束型设备群调度研究尚处在一个起步阶段. 本文对带有驻留约束且具有多种晶圆类型的集束型设备群的调度问题进行了研究,在引入时间约束集概念的基础上建立了调度模型, 同时,提出了一种逐级回溯的调度方法,并对调度算法进行了仿真实验分析. 仿真结果表明本文提出的算法是有效且可行的.     

半导体制造系统仿真调度中的优化方法

基于仿真的调度方法通常需要进行大量的仿真或者采用好的规则以优化调度结果. 本文建立了以减小平均在制品为优化目标的半导体制造系统的调度模型, 对模型进行分解和简化. 把结论作为一个调度规则直接应用于仿真调度方法. 由于充分利用了系统全局的状态信息, 可以有效地减少仿真的次数, 提高了仿真调度的优化能力.     

光刻设备TRACK系统的无死锁调度算法

为使半导体生产线光刻设备的TRACK系统正常运行,有效的避免死锁,提高设备利用率,提出一种无死锁调度算法,在不会引发死锁的条件下为需要调度的工件确定加工组件,同时考虑到组件发生故障的情况,及时排除故障的影响,并通过仿真证明了算法的有效性。     

网格技术在半导体生产调度中的应用研究

网格技术是一种新型的分布计算技术,致力于解决复杂度很高的新应用问题.随着全球半导体生产规模的日益扩大,半导体生产线的优化调度问题成为学术界及工程界研究的热点.半导体生产线具有许多特殊的特点,诸如生产规模大、工件数量多、随机性大、加工成本高、高度的可重入性等,这些特点决定了原有的调度策略已不能满足半导体生产线的要求.鉴于网格技术在处理设备可扩展性和资源平衡性上的优势,主要研究将网格技术的思想用于半导体生产线的调度中.利用网格计算中的负载向量和失衡因子的概念,来控制半导体生产线上各加工机器处工件块的规模以及投料规模.通过优化算法的调度,使得半导体生产线的各加工设备负载得到平衡,设备的生产效率提高,缩短加工周期,从而达到优化生产线的目的.     

基于多目标的半导体生产线满意调度

在分析生产线性能指标的基础上,选择出用于优化的目标,并针对基于单目标的调度策略所存在的局限性,采用满意的概念,根据模糊规则与比较法提出了基于多目标的满意调度,实验表明该调度方法较单目标调度策略有很大的优越性,从而为实现生产线的整体优化提供了解决的途径。     

Using scheduling in flexible manufacturing systems

In this paper the concept of recursive Allen temporal algebra is fully described, along with its relationship to the smart scheduler program and the concept of smart scheduling in general. Flexible manufacturing systems (FMS) are described, and their relationship with smart scheduling is explored. A case study that shows the utility of SSP in an FMS environment is presented in detail. Conclusions about the concept of smart scheduling are presented in closing.     

FLIP: Prototyping multi-robot systems

The main objective of this article is to promote the use of inexpensive and realistic prototypes to verify the applicability of state-of-the-art technologies in the area of mobile robotics and intelligent manufacturing systems. We propose a toy prototype based on a LEGO^® Mindstorms™ RCX brick extended with a PDA and wireless LAN. We describe and evaluate the application of an agent-oriented approach for a prototype of an autonomous transportation system. This transportation system is an integral part of the FLIP project in which intelligent collaboration during transportation and processing of LEGO^® bricks is investigated.     

TLA: Temporal look-ahead processor allocation method for heterogeneous multi-cluster systems

In a heterogeneous multi-cluster (HMC) system, processor allocation is responsible for choosing available processors among clusters for job execution. Traditionally, processor allocation in HMC considers only resource fragmentation or processor heterogeneity, which leads to heuristics such as Best-Fit (BF) and Fastest-First (FF). However, those heuristics only favor certain types of workloads and cannot be changed adaptively. In this paper, a temporal look-ahead (TLA) method is proposed, which uses an allocation simulation process to guide the decision of processor allocation. Thus, the allocation decision is made dynamically according to the current workload and system configurations. We evaluate the performance of TLA by simulations, with different workloads and system configurations, in terms of average turnaround time. Simulation results indicate that, with precise runtime information, TLA outperforms traditional processor allocation methods and has up to an 87% performance improvement.     

Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields

This paper presents a reactive scheduling approach for flexible manufacturing systems, which integrates the overall energy consumption of the production. This work is justified by the growing needs of manufacturers for energy-aware control, due to new important environmental criteria, which holds true in the context of high reactivity. It makes production hard to predict. The proposed reactive scheduling model is based on potential fields. In this model, resources that sense the intentions from products are able to switch to standby mode to avoid useless energy consumption and emit fields to attract products. Simulations are provided, featuring three indicators: makespan, overall energy consumption and the number of resource switches. Real experiments were carried out to illustrate the feasibility of the approach on a real system and validate the simulation results.     

A decentralized model for flow shop production with flexible transportation system

The recent advances in technology sectors often clash with traditional organizational paradigms which can limit or make difficult an efficient implementation in the real world. In this paper we show how it is possible to exploit the advantages of innovative technologies in manufacturing when these are supported by new and efficient methods for production management. More in details, we face a flow shop scheduling problem in a shoe manufacturing system in which overtaking of jobs is allowed thanks to an innovative transportation system. Overtaking means that a job can be put in waiting state and another job can surpass it, allowing the change of the scheduling sequence. Preemption is not allowed. The objective function of the problem is the minimization of the maximum lateness. We propose a decentralized model, based on multi-agent system theory, to represent the production cells of the plant and to include the potentiality offered by overtaking of jobs at decisional level. The adoption of a decentralized approach increases the system flexibility since each machine is able to solve its local scheduling problem. Adding or removing machines to the plant will not imply a change in the scheduling algorithms. The outcomes of this work are reached firstly through a formulation of the problem with three flow shop scheduling models, secondly through a comparison of the models with respect to different performance indicators. The results highlight as the decentralized approach is able to reach comparable performances with the centralized one for a relevant number of instances. Moreover sensitivity analysis shows as in the decentralized model the computational time required to solve bigger instances increases less quickly than in the case of centralized ones. Finally, simulations of the decentralized approach clarify as the correlation of the local solution procedure is effected by the number of machines of the flow shop and the coordination mechanism is effected by the number of the jobs to be scheduled.     

Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method

This paper proposes an ant colony optimisation-based software system for solving FMS scheduling in a job-shop environment with routing flexibility, sequence-dependent setup and transportation time. In particular, the optimisation problem for a real environment, including parallel machines and operation lag times, has been approached by means of an effective pheromone trail coding and tailored ant colony operators for improving solution quality. The method used to tune the system parameters is also described. The algorithm has been tested by using standard benchmarks and problems, properly designed for a typical FMS layout. The effectiveness of the proposed system has been verified in comparison with alternative approaches.     

A holonic approach to dynamic manufacturing scheduling

Manufacturing scheduling is a complex combinatorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, where the scheduling functions are distributed by several entities, combining their calculation power and local optimization capability. In this scheduling and control approach, the objective is to achieve fast and dynamic re-scheduling using a scheduling mechanism that evolves dynamically to combine centralized and distributed strategies, improving its responsiveness to emergence, instead of the complex and optimized scheduling algorithms found in traditional approaches.