试论电力系统调度自动化

电力调度自动化系统有着安全、稳定的优势,同时又能够及时的对电网进行实效性监控。能够对整个系统的调度自动化起到健制作用,同时调度自动化在其中发挥了不可替代的重要作用,他全程跟踪电网的运行情况,为的是能够使网络得到及时的维护,能够使系统顺利的运行,保持良好的状态。本文从电力调度自动化系统的基本功能、现状、技术优化、发展趋势方面进行了分析。     

浅析电力调度自动化系统稳定运行出现的问题及解决措施

随着技术的进步,计算机网络系统推动了电力调度自动化系统的广泛应用。本文简述了电力调度自动化系统的应用现状以及在实际使用中所存在的问题,并对电力调度自动化系统存在的问题进行了分析,并提出了相应的解决策略。     

基于我国电网调度特点的电网自动化及时性通信的探讨

本文对我国电网调度自动化专业发展现状进行简要介绍,同时分析了以自身调度为特点的自动化及时通信系统的通信网管、通信协议集成、运行模式等。以调度自动化系统为前提,根据具体需要将多个应用实时通信子系统有效集成。     

自动化立体仓库出入库调度优化

针对自动化立体仓库的出入库调度优化问题,提出一种基于遗传算法/层次分析法(GA/AHP)的调度规则优化方法.遗传算法中的每个染色体都代表一组可用于描述具体调度方案的规则组合,并对染色体采用分段整数编码;为获得适应度函数值,利用一种集成层次分析法的决策优化方法求取相应的适应度值.优化调度仿真结果表明,该方法能有效地应用在自动化立体仓库的出入库调度规则的优化问题上.     

刍议调度自动化系统中常见问题的解决策略

随着国家电力系统迅速发展,电力调度自动化工作也相应的快速发展起来。电力调度自动化通过对电力系统中的运行参数和信息进行及时监视和控制,为电力系统的正常运行提供了保障。本文就电力调度自动化系统中常见问题的解决策略进行分析及研究。     

基于分析的空调系统运行控制的多目标优化

针对空调系统运行效率低下,且运行过程中子系统之间相互影响、相互制约的问题,本文基于分析方法提出一种空调系统多目标运行优化方法,以提高空调系统整体的运行效率。以某机场航站楼空调系统为研究对象,采用分析方法建立了空调系统的生产结构图,并根据生产结构将空调系统划分为3个子系统。在建立子系统分析模型的基础上,以3个子系统的最小损为优化目标,采用多目标粒子群优化算法(MOPSO)对空调系统的运行参数进行优化。针对某一典型日的运行工况对空调系统进行多目标优化,结果表明:相对于空调系统的原始控制方式,采用多目标运行优化方法可以有效减小空调各个子系统以及整个系统的损,使空调系统的运行效率明显提高,达到了节能的目的。     

浅析电网自动化调度的无功电压管理

无功电压管理在电力系统的运行过程中起着至关重要的作用,是对电力系统自动化调度进行合理优化的重要方式。特别是随着我国经济的蓬勃发展,人们的生活水平日益提高,对电力资源的需求量以及利用量都大幅度增加,这就加重了电力系统的负荷。因此,加强对电网自动化调度的无功电压管理极其重要。对无功电压管理工作中存在的问题进行分析研究,提出切实可行的优化策略也极具现实意义。     

电力调度自动化系统的一体化技术研究

电力调度自动化系统主要为电网调度管理人员提供电网各种实时的信息,并对电网进行调度决策管理和控制,保证电网安全运行,提高电网质量和改善电网运行的经济性。本论文主要针对目前调度自动化系统所面临的几个主要问题,探讨一体化技术在电力调度自动化系统中的应用,并从平台一体化、图模库一体化、应用功能一体化和接口一体化多个方面进行应用研究分析。     

电网调度自动化系统通信网络防护措施

通过对电网调度自动化系统通信网络防护措施的分析,并结合电网调度自动化系统的结构特点,提出相应的信息安全策略及应采取的安全管理措施,希望对今后电网调度自动化系统通信网络防护措施研究能够有所帮助。     

Selection of dispatching rules on multiple dispatching decision points in real-time scheduling of a semiconductor wafer fabrication system

Semiconductor wafer fabrication involves one of the most complex manufacturing processes ever used. To control such complex systems, it is a challenge to determine appropriate dispatching strategies under various system conditions. Dispatching strategies are classified into two categories: a vehicle-initiated dispatching policy and a machine-initiated dispatching policy. Both policies are important to improve the system performance, especially for the real time control of the system. However, there has been little research focusing on combining them under various situations for the semiconductor manufacturing system. In addition, it is shown that no single dispatching strategy consistently dominates others in all situations. Therefore, the goal of this study is to develop a scheduler for selection of dispatching rules for dispatching decision variables in order to obtain the desired performance measures given by a user for each production interval. For the proposed methodology, simulation and competitive neural network approaches are used. The results of the study indicate that applying our methodology to obtaining a dispatching strategy is an effective method considering the complexity of semiconductor wafer fabrication systems.     

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.     

A simulation study of new multi-objective composite dispatching rules,CONWIP, and push lot release in semiconductor fabrication

This paper evaluates dispatching rules and order release policies in two wafer fabrication facilities (thereafter referred to as ‘fab’) representing ASIC (application specific integrated circuit) and low-mix high-volume production. Order release policies were fixed-interval (push) release, and constant work-in-process (CONWIP) (pull) policy. Following rigorous fab modelling and statistical analysis, new composite dispatching rules were found to be robust for average and variance of flow time, as well as due-date adherence measures, in both production modes.     

Learning effective dispatching rules for batch processor scheduling

Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.     

Using in-line equipment condition and yield information for maintenance scheduling and dispatching in semiconductor wafer fabs

Yield is one of the most important measures of manufacturing performance in the semiconductor industry, and equipment condition plays a critical role in determining yield. Researchers and practitioners alike have traditionally treated the problems of equipment maintenance scheduling and production dispatching independently, ignoring how equipment condition may affect different product types or families in different ways. This paper addresses the problem of how to schedule maintenance and production for a multiple-product, multiple-stage production system. The problem is based on the situation found in semiconductor wafer fabrication where the equipment condition deteriorates over time, and this condition affects the yield of the production process. We extend a recently developed Markov decision process model of a single-stage system to account for the fact that semiconductor wafers have multiple layers and thus make repeated visits to each workstation. We then propose a methodology by which the single-stage results can be applied in a multi-stage system. Using a simulation model of a four-station wafer fab, we test the policies generated by the model against a variety of other maintenance and dispatching policy combinations. The results indicate that our method provides substantial improvements over traditional methods and performs better as the diversity of the product set increases. In the scenarios examined, the reward earned using the policies from the combined production and maintenance scheduling method was an average of more than 70° higher than the reward earned using other policy combinations such as a fixed-state maintenance policy and a first-come, first-serve dispatching policy.     

Effect of load,processing time and due date variation on the effectiveness of scheduling rules

In real manufacturing environments, variations in production factors (i.e. processing time, demand, due-dates) are inevitable facts. All these dynamic changes, together with random disturbances (e.g. machine breakdowns) can seriously affect the system performance. In this paper we focus on load, processing time and due date variation and analyse their impacts on a scheduling system. Specifically, we investigate the impact of variation on dispatching policies in a job shop environment via simulation. The statistical analysis of the results leads to two major conclusions: first, the relative performance of rules is not threatened much by PV (processing time variation), LV (load variation) or DDV (due date variation) -- a result that can be a consolation for practitioners in the field. Secondly, the performance of the rules deteriorates, in particular at high levels of PV, LV and DDV -- a result that can provide new insights into the problem and produces useful information for researchers in their continuous effort to develop better dispatching rules.     

Aligning workload control theory and practice: lot splitting and operation overlapping issues

This paper addresses the problem of lot splitting in the context of workload control (WLC). Past studies on WLC assumed that jobs released to the shop floor proceed through the different stages of processing without being split. However, in practice, large jobs are often split into smaller transfer sublots so that they can move more quickly and independently through the production process and allow operations overlapping relating to the same job. This paper assesses the performance of different lot splitting policies for job release and dispatching strategies under lot splitting. A new dispatching rule was designed to specifically take advantage of lot splitting and operations overlapping in the context of WLC. Discrete-event simulation is used to assess system performance in relation to the ability to provide shorter delivery times and on time deliveries. Results highlight the importance of releasing the sublots of the same job together and demonstrate that combining an effective lot splitting policy with an appropriate dispatching rule can enhance the performance of production systems.     

Analysis of storage assignment policies in less than unit load warehousing systems

Analytical models for the analysis of item location policies in unit load storage and retrieval systems are extended to less than unit load warehousing systems. The models are used to compare dedicated storage using cube per order index item dispatching and randomized storage using closest open location item dispatching with respect to total item space requirements, orderpicking cycle times and system responsiveness. Despite a number of computational limitations, it is argued that the models provide practical tools for analysis of less than unit load systems prior to development of simulation-based models or for validation of simulation-generated model results. The application of the models are demonstrated for a sample problem.     

Heuristic dispatching rule to maximize TDD and IDD performance

Although mean flow time and tardiness have been used for a long time as indicators in both manufacturing plants and academic research on dispatching rules, according to Theory of Constraints (TOC), neither indicator properly measures deviation from production plans. TOC claims that using throughput dollar-day (TDD) and inventory dollar-day (IDD) can induce the factory to take appropriate actions for the organization as a whole, and that these can be applied to replace various key performance indices used by most factories. However, no one has studied dispatching rules based on TDD and IDD performance indicators. The study addresses two interesting issues. (1) If TDD and IDD are used as performance indicators, do those dispatching rules that yield a better performance in tardiness and mean flow time still yield satisfactory results in terms of TDD and IDD performance? (2) Does a dispatching rule exist to outperform the current dispatching rules in terms of TDD and IDD performance? First, a TDD/IDD-based heuristic dispatching rule is developed to answer these questions. Second, a computational experiment is performed, involving six simulation examples, to compare the proposed TDD/IDD-based heuristic-dispatching rule with the currently used dispatching rules. Five dispatching rules, shortest processing time, earliest due date, total profit, minimum slack and apparent tardiness cost, are adopted herein. The results demonstrate that the developed TDD/IDD-based heuristic dispatching rule is feasible and outperforms the selected dispatching rules in terms of TDD and IDD.