无缝钢管生产的多规则调度仿真优化研究

无缝钢管生产由热加工与冷加工组成,热加工有很强的连续性,而冷加工过程具有可重入现象,单一规则或局部规则调度很难解决此类问题。提出多规则组合仿真优化方法,从无缝钢管的生产特点入手,寻找到关键决策点,在Arena仿真环境下设计调度规则模块库和系统参数水平集合;采用柔性化仿真建模技术,将仿真模型分为功能模块、控制模块,构建无缝钢管生产系统的仿真模型;以平均生产周期为目标,利用TS算法优化无缝钢管生产系统的调度规则组合及相关参数水平,实例证明方法有效.     

基于规则的柔性作业车间机床与AGV联合调度优化

利用离散事件系统建模与仿真技术和规则调度方法,对柔性作业车间机床与AGV(automatic guided vehicle)联合调度问题进行研究。基于离散事件建模仿真方法建立了柔性作业车间仿真模型,以平均流动时间、完工时间、AGV平均利用率与完工数量为评价指标,首先将不同路由规则与调度规则组合结合AGV数量进行仿真测试,确定了最佳规则组合与AGV最佳数量。在确定最佳规则基础上,研究车间任务到达模式与AGV数量对系统性能的影响,不同工况下的仿真结果表明,在最优规则组合、任务到达模式与AGV数量下,柔性作业车间联合调度问题能得到有效解决且系统性能将达到最优。     

一类规则调度系统的Petri网研究方法

研究了规则调度系统的性能分析问题,与传统的仿真研究方法比较,文中采用了基于Ｐｅｔｒｉ网的研究思想,首先运用Ｐｅｔｒｉ网给规则调度系统建模,得到调度规则的形式化表示;然后基于该模型作稳定性及其它性能分析,最后针对一类切换式规则调度系统,介绍了运用混合动态系统理论开展研究的具体思路。     

工作流知识规则仿真的研究与实现

工作流仿真是目前工作流系统研究的主流方向之一。针对利用知识规则建模的工作流系统,如何确保自定义的知识规则的可靠性,从工作流模型建模、仿真平台的构建和仿真引擎3个方面,研究了工作流知识规则的仿真,通过可视化的实例显示以及统计技术等手段,精确地找出知识规则的错误。     

基于仿真和正交试验的优化调度研究

针对生产系统多点协调调度问题,提出了一种基于仿真和正交试验的优化调度方法。在动态生产环境中,为了支持快速仿真调度分析,以模型元素和控制逻辑分离的原则为指导,采用面向对象方法,实现了基于模块化控制逻辑的快速仿真建模。以仿真模型作为系统目标求解函数,各种决策参数作为系统输入变量,通过正交试验优化仿真实验,确定最佳调度决策参数组合,将其应用于生产系统的调度控制。以某研究所的数控车间为例,验证了该调度方法的可行性和有效性。     

柔性作业车间在线调度问题的仿真建模与分析

利用离散事件系统建模与仿真技术和规则调度方法,对柔性作业车间在线调度问题进行研究。将优先级调度规则和路径选择规则结合,形成针对柔性作业车间的调度规则组合。在建立仿真模型时,定义了车间结构,并对车间结构参数设定不同的水平值。针对平均流经时间最小、最大拖期时间最小、机器最大负荷最小、拖期工件比最小这4个不同的调度目标,从备选规则集中选出了每个目标下最优的调度规则组合及相应的车间结构参数。仿真结果表明,工件交货期、车间利用率、机器平均有效率、机器平均故障水平及车间柔性程度对调度规则组合的选取有很大影响。     

基于ADAMS的三维虚拟道路的再现

针对ADAMS仿真软件,提出了三维虚拟道路模型的再现方法和适用于Ftire模型的规则栅格的组合道路的建模方法.利用采集的实际道路不平度的高程数据,结合ADAMS路面文件的编制格式,应用MATLAB语言编写了3D等效容积路面、3D样条道路及细合道路文件,真实的再现了三维虚拟道路.通过对车辆模型在这三种不同道路上的仿真,结果验证了三种道路模型的正确性,表明了组合道路在ADAMS环境中具有良好的可行性.三维虚拟道路的再现为今后道路谱数据库的建立提供了一种行之有效的方法.     

基于粗糙-神经网络的非线性系统逆模型控制

粗糙控制是近年来兴起的一种新的智能控制方法,作为对粗糙控制理论的探索,提出了粗糙规则逆模型的概念,并分析了粗糙规则逆模型的一致性和完备性问题,引入了基于径向基函数网络的粗糙决策规则推理方法,构造了粗糙-神经网络逆模型.对粗糙-神经网络逆系统模型的辨识以及基于粗糙-神经网络逆模型的控制理论和方法进行了分析和讨论,并通过实例仿真计算与实验分析,验证了粗糙-神经网络逆模型控制方法的可行性.     

基于自适应模糊的印刷色彩质量控制及Simulink仿真

本文通过对印刷机印刷色彩质量控制问题的研究,针对印刷过程受工艺条件影响,生产加工曲线发生变化导致印刷色彩质量出现偏差的问题,为控制印刷色彩质量并尽量减少调整次数,提出了利用自适应模糊控制,基于T—S模型设计控制规则并根据色彩检测偏差对控制规则的参数进行调整的控制方案,最终使控制器的控制规则符合现场生产条件。并以某印刷条件下的印刷机色彩质量控制为例,利用Matlab/Simulink对该控制方案进行建模仿真,验证其能达到较好的控制效果。     

基于混沌退火算法的非线性系统Fuzzy控制规则自调整

非线性系统模糊控制实际应用中存在控制精度和自适应能力问题，模糊控制规则的自调整和自寻优，是提高和改善模糊控制器性能重要手段。带有修正因子的控制规则能够根据误差E和误差变化EC自动产生规则，利用全局寻优的混沌模拟退火算法对修正因子优化，优化模糊规则；并引入综合考虑误差、误差绝对值时间、控制能量、超调诸因素的性能函数。非线性系统仿真实例说明该方法响应上升时间快，超调小提高了模糊控制器性能。     

Design of due-date oriented look-ahead batching rule in wafer fabrication

In wafer fabrication processes, batch processing accounts for over 30% of the overall processing time. And it’s a trade-off between machine utilization and wafer waiting time. Therefore, batch machines have become one of the constraint resources during wafer fabrication. How to maintain the utilization and reduce the waiting time are important tasks for production control. Plenty of research in the past several years focused on the dispatching rules of batch processing. According to many researchers, look-ahead batch dispatching rules outperform MBS on waiting times and machine utilization. The look-ahead batching rules that have been developed are DBH, NACH, MCR, and DJAH. However, these rules do not take the due-date information of wafers into consideration, and can’t accelerate the wafer’s fabrication that will not be completed before the due-date. This study will develop a due-date oriented look-ahead batching rule, namely LBCR, that considers the due-date and expects to raise delivery rates and reduce the average tardiness. Firstly, this study will modify those batching rules to fit the manufacturing environment of wafer fabrication. There are serial simulation tests on those batching rules under various kinds of factors in terms of environment, including traffic intensity, product numbers and product mix rate. Finally, this study will compare the five batching rules on different performance indicators. After the simulation and statistic analysis undertaken, LBCR does outperform other batch dispatching rules on due-date related performance indicators, such as tardy rate and average tardiness.     

Efficient dispatching rules for dynamic job shop scheduling

This study attempts to provide efficient dispatching rules for dynamic job shop scheduling by combining different dispatching rules. A dispatching rule is used to select the next job to be processed from a set of jobs awaiting service. A job shop will be treated as dynamic, when conditions such as continuously arriving new jobs and deviations from current schedule need to be accommodated, and a job shop should be treated as an integrated part of a manufacturing system. The discussion includes a simulation technique which uses ARENA 4.0. software to simulate the dynamic model of a job shop under various rules and performance measures . Results of the simulation show that, for most of the performance measures, combined rules perform well. In this study, the combined rules MWKR_FIFO and TWKR_SPT do well under most conditions.     

New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop

This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0–1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions.     

Analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times

Stochastic dynamic job shop scheduling problem with consideration of sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of nine dispatching rules in such shop from makespan, mean flow time, maximum flow time, mean tardiness, maximum tardiness, number of tardy jobs, total setups and mean setup time performance measures viewpoint. A discrete event simulation model of a stochastic dynamic job shop manufacturing system is developed for investigation purpose. Nine dispatching rules identified from literature are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90 % and setup times less than processing times. Results indicate that shortest setup time (SIMSET) rule provides the best performance for mean flow time and number of tardy jobs measures. The job with similar setup and modified earliest due date (JMEDD) rule provides the best performance for makespan, maximum flow time, mean tardiness, maximum tardiness, total setups and mean setup time measures.     

A production order-driven AGV control model with object-oriented implementation

To effectively manage the material handling in Flexible Manufacturing Systems (FMS), where a large amount of data is required in the dynamic decision-making, integrated control is needed to consider the overall production schedule. The focus of this research is on the development of an integrated Automated Guided Vehicle System (AGVS) control model that includes essential features like dynamic vehicle path determination and conflict-free routing. An object-oriented implementation of the AGVS model is proposed that forms the basis of systems integration with a production planning module such as MRP. Static and dynamic informational and functional models of the AGVS are developed. The system incorporates: (i) conflict-free shortest path routing procedures, and (ii) vehicle assignment rules or scheduling strategies. A prototype version of each of these has been developed for demonstrative purposes. This object-oriented modelling methodology provides the capability of rapid development and change. The approach has been demonstrated for a real manufactured product through simulation studies which confirm the superior performance of anticipatory AGVS control rules, even in a production orderdriven environment.     

Dissimilarity Maximization Method for Real-time Routing of Parts in Random Flexible Manufacturing Systems

This paper presents a dissimilarity maximization method (DMM) for real-time routing selection and compares it via simulation with typical priority rules commonly used in scheduling and control of flexible manufacturing systems (FMSs). DMM aims to reduce the congestion in the system by selecting a routing for each part among its alternative routings such that the overall dissimilarity among the selected routings is maximized. In order to evaluate the performance of DMM, a random FMS, where the product mix is not known prior to production and off-line scheduling is not possible, is selected for the simulation study. A software environment that consists of a computer simulation model, which mimics a physical system, a C++ module, and a linear program solver is used to implement the DMM concept. In addition to DMM, the simulation study uses two priority rules for routing (i.e., machine) selection and seven priority rules for selecting parts awaiting service at machine buffers. The results show (1) DMM outperforms the other two routing selection rules on production rate regardless of the part selection rule used, and (2) its performance is highly dependent on the part selection rules it is combined with.     

Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach

This paper studies a flexible job shop problem considering dynamic events such as stochastic job arrivals, uncertain processing times, and unexpected machine breakdowns. Also, the considered job shop problem has routing flexibility and process flexibility. A multi-agent scheduling system has been developed for solution with good quality and robustness. A pheromone-based approach is proposed for coordination among agents. The proposed multi-agent approach is compared with five dispatching rules from literature via simulation experiments to statistical analysis. The simulation experiments are performed under various experimental settings such as shop utilization level, due date tightness, breakdown level, and mean time to repair. The results show that the proposed agent-based approach performs well under all problem settings.     

Makespan minimization for m-machine permutation flowshop scheduling problem with learning considerations

Increased complexity of current manufacturing systems together with dynamic conditions and permanent demands for flexible and robust functionality makes their management and control very difficult and challenging. Workflow simulation is an effective approach to investigate dynamic workflow scheduling policies and evaluate the overall manufacturing system performance. The results attained in simulation model can give directions on how to maximize system output when selecting an appropriate scheduling practice for a real system. In this paper, we investigate the abilities of multi-agent systems in combination with dynamic dispatching rules and failure handling mechanisms to manage dynamic environment conditions (such as machine failures) for systems in the production automation domain. We measure system robustness by systematically assessing the total system performance (e.g., number of finished products) in a number of representative test cases. We use an agent-based simulation environment, MAST, which has been validated with real-world hardware to strengthen the external validity of the simulation results. We investigated the performance of a re-scheduling component which uses four different policies that define how to adjust the system schedule in case of machine disturbances/failures. In the context of the empirical study the Complete Rerouting re-scheduling policy outperformed all other policies.     

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.