基于动态资源能力服务的分布式协同调度技术

基于云制造技术中面向服务的思想,针对现有异地分布多车间协同生产计划的关联协调问题,提出并建立一种以云制造思想为指导的基于动态制造资源能力服务化的分布式协同生产调度技术。通过分析多车间协作生产下的制造资源动态服务共享和关联协调的问题,建立基于服务性制造资源的协同生产调度技术总体框架;提出服务性资源的地域和能力相结合的筛选规则,形成面向多车间协同生产的空闲占用和插入调整相结合的作业排产技术;提出资源指定占用下的作业排产处理技术,解决了协同生产环境下特定车间基于制造资源能力局部能力占用的调度排产;针对生产过程中频繁发生的突发事件,提出以降低生产扰动影响范围的时间容忍度技术及基于移动处理方式的动态调度方案调整技术,解决了制造资源服务动态变化下的快速响应处理问题。通过应用实例对以上关键技术进行了验证。     

船舶曲面分段建造作业调度数字化仿真与优化

针对船舶曲面分段建造难度大、效率低的问题,对曲面分段车间建造作业调度过程进行仿真与优化研究。首先,提出基于虚拟流水线的分段建造车间作业调度模式;其次,基于数字化工厂策略实现其作业调度过程的仿真;以最大完工时间最小化为目标,提出基于遗传算法的作业调度过程优化;最后,以实际生产计划为对象进行了实例验证。结果表明:基于虚拟流水线的作业调度模式和基于数字化工厂策略的仿真优化,有效缩短了曲面分段建造周期,为提升船舶建造效率和水平提供了支撑。     

基于并行协同的多车间协同调度问题研究

传统企业在实际生产中,其多个关联车间之间的生产计划与调度存在难以协作的问题。为此,针对多车间协同调度问题建立了调度模型,提出了一种多车间协同调度的并行协同进化遗传算法(PCE-GA),并且采用该算法对上述模型进行了求解。首先,以最小化订单完工时间为目标,建立了单目标调度模型;然后,采用了并行协同进化遗传算法,对上述单目标调度模型进行了求解,基于工件、机器、装配关系的三层整数编码的染色体编码方案,提出了一种协同适应度值计算的方法;最后,以某液压缸生产企业为例,针对单目标调度问题,采用该算法与单车间遗传算法(JSP-GA)、并行协同模拟退火算法(PCE-SA)分别进行了求解,并对其结果进行了比较,以验证PCE-GA算法的优越性。研究结果表明：采用PCE-GA算法得到的优化率为13.3%,比单车间作业调度遗传算法求解的数据优化11.5%,该结果证明了PCE-GA算法在解决多车间协同优化问题时的优越性。     

MATLAB下基于遗传算法作业车间调度系统开发

针对MATLAB环境下基于遗传算法的作业车间调度系统的关键技术和实现方法进行了研究,提出一种基于MATLAB/GUI的作业车间调度系统的实现方法。对作业车间问题、作业车间遗传算法思想、作业车间调度系统建立进行描述,并结合对MATLAB/GUI技术、MATLAB与Excel数据交换技术的研究开发了一种方便、实用的作业车间调度系统。通过实例验证了调度系统的有效与实用性。     

单件生产系统中动态车间作业计划与监控系统的集成研究

对于动态多变的单件生产系统来说，车间作业计划是最有效的生产管理措施之一；在编制这类生产系统的车间作业计划时，与监控系统有效集成又是必须考虑的最关键的问题。为此研究了单件生产系统中车间作业计划与监控系统的集成问题，又提出了集成的总体结构，基于这一结构，叉提出了日程作业计划的3种确定方法，较详细地分析了在进行车间作业计划滚动编制时，对各种监控反馈信息的处理措施。     

单件小批生产的作业计划与调度监控集成系统

介绍了一个实用化的单件小批生产条件下车间作业计划与调度监控集成系统。该系统由工艺准备子系统和生产作业计划及调度监控子系统组成。系统可快速建立或修正作业计划,实现车间物流实时仿真及调度监控,最大限度地减少材料和劳动力的消耗、提高设备的利用率,从而降低产品的制造成本。     

启发式算法在单件车间工序排序问题中的应用

本文探讨了车间作业计划的排序问题 ,深入分析和研究了启发式算法 ,结合模具车间的实际情况 ,采用基于无延迟作业计划的概率调度法 ,并进行适当的改进 ,实现对模具车间作业计划的排序 ,取得了较好的效果。     

动态联盟公司异地生产计划调度与控制系统的研究

针对复杂任务问题的求解，提出了一种基于敏捷企业间组成的动态联盟公司的运行模式，并给出了一种异地生产计划调度与控制系统的结构模式。详细讨论了分布式生产调度的协同控制方法。     

动态联盟公司异地生产计划调度与控制系统的研究

针对复杂任务问题的求解，提出了一种基于敏捷企业间组成的动态联盟公司的运行模式，并给出了一种异地生产计划调度与控制系统的结构模式，详细讨论了分布式生产调度的协同控制方法。     

基于Multi—Agent的虚拟车间调度作业原理与算法

在分析敏捷制造环境下车间生产控制方式的特点的基础上,认为构成虚拟车间调度的要素,资源和项目都可封装成智能体,提出基于Multi-Agent的虚拟车间调度单元模型,根据该模型,设计了一种调度遗传算法,应用实例表明该算法具有建模简单,运算速度快等特点,适用于任务类型相同的多个项目在多个虚拟车间的调度问题。     

Flexible job shop scheduling with tabu search algorithms

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers.     

An immune algorithm approach to the scheduling of a flexible PCB flow shop

Scheduling is an important task in manufacturing. It involves assigning jobs to machines in a particular order so as to meet the manufacturing target. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA), which is a new evolutionary algorithm, can be used to tackle complex scheduling problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes an IA approach to the scheduling of a flexible flow shop for PCB manufacturing. An overview of the flexible flow shop problem and the basic notions of an IA are first presented. Subsequently, the details of an IA approach are described and implemented. A case study of a flexible flow shop for the manufacture of PCBs for communication equipment was then conducted. The results obtained are compared with those computed by genetic algorithms (GAs), which are well-known evolutionary algorithms. From the case study, it was established that IAs outperformed GAs. The details of the case study as well as the comparative study are described.     

一种改进的遗传算法在车间调度中的应用

研究现代生产先进制造车间调度问题 ,对于发挥先进制造车间的高效性和灵活性具有十分重要的意义。生产过程的计划调度问题 ,是实现制造系统运筹技术、管理技术与优化技术的核心。因此 ,优化调度算法的性能直接影响到FMS的生产效率和应变能力。本文在传统的遗传算法的基础上 ,对车间调度算法进行了深入的研究。基于机床负荷平衡的思想 ,提出了面向先进制造车间的平衡遗传算法 ,并与传统的遗传调度算法进行了对比。实验表明 ,该调度方法具有较高的可行性、有效性和稳定性。保证了各个机床的负荷平衡 ,满足了交付期的要求     

基于PetriNets和混合遗传算法的双资源JSP动态优化调度

以带有控制器的Petri网为建模工具对柔性生产调度中的离散事件建模,利用遗传算法和模拟退火算法获得调度结果,并通过Petri网进行控制.用于解决作业车间的加工受到机床、操作工人等生产资源制约条件下的优化调度.以生产周期为目标进行的优化调度,将遗传算法和模拟退火相结合.通过多种交叉、变异、概率更新选择、再分配策略等遗传和模拟操作,得到目标的最优或次优解.对算法进行了仿真研究,仿真结果表明该算法是有效性.     

Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm

This paper focuses on the scheduling problem of the reconfiguration manufacturing system (RMS) for execution level, where the final objective is to output a production plan. The practical situation in Chinese factory is analyzed, and the characteristics are summarized into the contradiction between flow and job shop production. In order to handle this problem, a new production planning algorithm in virtual cells is proposed for RMS using an improved genetic algorithm. The advantages of this algorithm have three parts: (1) the virtual cell reconfiguration is formed to assist making production plans through providing relationship among task families and machines from cell formation; (2) The operation buffer algorithm is developed for flow style production in cells, which can realize the nonstop processing for flow style jobs; and (3) The multicell sharing method is proposed to schedule job shop jobs in order to fully utilize manufacturing capability among machines in multicells. Based on the above advantages, an improved genetic algorithm is developed to output scheduling plan. At last, the algorithm is tested in different instances with LINGO and the other genetic algorithm, and then the scheduling solution comparison shows the proposed algorithm can get a better optimum result with the same time using the comparison algorithm.     

基于MES的生产车间管理信息系统

针对制造企业在实施生产管理信息化过程中存在的问题,提出以制造执行系统(MES)为核心的三层结构———企业资源计划(ERP) /制造执行系统(MES) /过程控制系统(PCS)的生产管理信息化的实施方案,用于解决生产与计划相互脱节的难题,实现了车间的现场调度、生产过程控制、在线质量控制,以及对生产过程中“非常规信息”的控制与管理,加强了制造计划的可执行性。     

The Effects of Scheduling Rules and Routing Flexibility on the Performance of a Random Flexible Manufacturing System

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance.     

工艺设计与车间作业计划优化运行模式的研究

工艺设计与车间作业计划是制造系统中的两个重要环节，实现二者的优化运行具有很大的实际意义。本文详细分析了工艺设计与车间作业计划之间的联系和区别，研究了串行模式下二者之间存在的问题和目前的解决方法，基于分布式工艺设计的任务划分思想，考虑了并行工程的要求和特点，提出一种工艺设计与车间作业计划的优化运行模式。     

面向产品快速开发的几何推理和虚拟原型

计算制造旨在解决制造系统和制造过程的计算复杂性问题，以实现产品快速开发，探讨了计算制造的基本概念、研究内容和典型问题，对计算制造的理论基础和体系结构进行了有益探索。详细讨论了支持产品快速开发的几个关键使能技术：几何推理、反求工程和虚拟原型，并扼要介绍了在这些方面的有关研究成果，为产品快速开发的研究提供了一些新的思路和方法。     

Conjunctive simulated scheduling

The scheduling of highly complex job shops, such as those found in semiconductor manufacturing, involves multiple types of work centers, large and changing varieties of the products, sequence dependent set up times, reentrant process flow, dynamically changing environment, etc. These complexities make the job shop scheduling problem very strongly nondeterministic polynominally (NP)-hard and intractable to solve by the traditional scheduling approaches. This paper presents the concept and applications of the conjunctive simulated scheduling (CSS) approach, which incorporates the benefits of discrete event simulation in scheduling the shop and avoids the NP-hard formulation of these complex scheduling problems. The differences between CSS and typical simulation studies and its application to online simulation are also presented.