可重构资源管理及硬件任务布局的算法研究

可重构系统具有微处理器的灵活性和接近于ASIC的计算速度,可重构硬件的动态部分重构能力能够实现计算和重构操作的重叠,使系统能够动态地改变运行任务,可重构资源管理和硬件任务布局方法是提高可重构系统性能的关键.提出了基于任务上边界计算最大空闲矩形的算法(TT-KAMER),能够有效地管理系统的空闲可重构资源;在此基础上使用FF和启发式BF算法进行硬件任务的布局.实验表明,算法能够有效地实现在线资源分配与任务布局,获得较高的资源利用率.     

可重构制造系统监督控制器的自动重构

提出了基于改进的网重写系统(Improved net rewriting system, INRS)的可重构制造系统(Reconfigurable manufacturing systems, RMS) Petri网监督控制器的自动重构方法, 以快速适应由市场需求变化所引起的制造系统构形的频繁变化. INRS解决了网重写系统存在的问题, 可动态调整给定Petri网模型的结构而不改变其行为属性. 以集合和图的组合形式定义了RMS的构形, 并提出了基于INRS的一类模块化、可重构的Petri网控制器的设计方法. 针对这类Petri网控制器, 提出了基于INRS的自动重构方法. 方法可将RMS构形的变化转变为INRS的图重写规则, 并作用于当前Petri网控制器, 使其快速、自动地重构为所求的新控制器. 所提出的Petri网控制器的设计与重构方法, 均从理论上保证了结果的正确性, 免校验. 仿真研究验证了方法的有效性.     

面向订单生产的制造单元动态重构方法研究

为提高单元制造系统的柔性和敏捷性,提出了面向订单生产情况下的制造单元动态重构的总体框架,设计了单元内部零件.设备可动态重组的优化算法.在满足约束的前提下,建立了以最小加工成本和最大生产工艺能力系数为目标的设备选择数学模型:同时,考虑重构成本和重构后带来的收益大小.引入新的相似系数,实现了新零件.设备的快速重组.最后,以某企业机加工车间为例,验证了算法的有效性.     

制造系统的可重构布局设计

研究重构系统的新布局具有重要的理论与实际意义 .本文讨论了制造系统可重构布局研究的目标、对象、基础及研究范畴 ,提出了利用模拟退火方法寻找重构布局最优解的搜索算法 .仿真实验验证了算法的有效性     

可重构制造系统重构算法的实例研究

可重构制造系统（RcMS)的模型及其重构算法是可重构制造系统的形式化表示，是研究RcMS优化的理论基础，基于赋时可重构Petri网的可重构制造系统模型（TRPN－RcMS)及其重构算法是RcMS的形式化表示方法之一。文中以实例对RcMS重构算法进行说明和论证。首先假设一个RcMS的实例系统；然后，根据RcMS的当前系统组成及其生成计划生成RcMS当前系统的TRPN－RcMS模型；最后，根据RcMS重构后的系统组成及其生产计划的变化情况，利用RcMS的TRPN－RcMS模型的重构算法，生成重构后的系统的TRPN－RcMS模型。     

利用决策支持系统分析可重构制造系统中的问题

可重构制造系统是面向新世纪的先进制造模式 .本文提出利用决策支持系统解决可重构制造系统所面临的重构决策问题 ,在系统决策、加工单元布局、以及可重构产品的生产计划问题上采用智能化算法 ,可以得到满意的结果     

基于免疫重构的阴性选择算法

针对阴性选择算法在解决实际问题中,易误判及自修复能力差的弱点,该文基于生物免疫系统内部学习优化机制以及工业领域中的可重构系统,提出了一种基于免疫重构的阴性选择算法.新算法将可重构系统的思想融入到阴性选择算法中,提出了重构串、重构模型与重构操作的概念与实现方法,以保证系统发生意外的时候能够及时恢复、重组.将算法应用于一个Web系统进行仿真实验,结果表明该算法是有效的.     

一种基于双仲裁时间片策略的可重构硬件任务调度算法

在可重构系统中,二维布局模型比一维布局模型具有更高的自由度.然而,二维模型获得较高的资源利用率要以复杂的资源管理和任务调度算法为代价,这不但使调度过程变得复杂,而且导致时间开销大,直接影响系统实时性.针对这一问题,在综合考虑性能和算法复杂度的基础上,提出了一种适用于二维可重构器件的双仲裁时间片可重构硬件任务调度算法DATS(Double Arbiters Time-Sliced).算法采用两个仲裁器对硬件资源进行管理,并根据空间和时间约束动态裁决任务布局位置;同时设计了双仲裁时间片任务调度模式图,对任务的调度和布局过程进行合理分离,使任务调度和布局过程相对独立并简化处理过程.DATS算法的调度时间复杂度为O(N),单任务布局算法的时间复杂度为O(E),其中N为被调度的任务总数,E(＜N)为器件中正在执行的任务数目,实验表明,DATS算法时间开销小,在轻负载情况下任务调度成功率比stuffing算法高1％～2％,在重负载情况下资源利用率保持在80％～85％的水平,与时间复杂度为O(N2)的算法基本一致,所以更适合于实时情况下的任务调度.     

基于混沌PSO的动态可重构系统软硬件划分

软硬件划分是动态可重构系统软硬件协同设计中的关键技术之一,如何兼顾划分效率和划分效果,达到两者的最佳结合是软硬件划分的主要问题.在考虑动态部分重构及重构延时等特征的基础上,提出一种微粒群优化算法与混沌优化算法相结合的混沌微粒群软硬件划分方法.该算法使用基于实数编码的微粒群优化算法执行全局搜索,再根据搜索结果采用混沌优化算法执行局部搜索,具有较强的全局搜索和跳出局部最优的能力.仿真实验表明,该算法比标准微粒群算法和遗传算法具有更好的有效性和快速性,能够有效地实现应用任务图到可重构系统的时空映射.     

基于遗传算法的可重构系统软硬件划分

在考虑动态部分重构及重构延时等特征的基础上,采用遗传算法及其与爬山算法的融合实现可重构系统软硬件任务的划分,并采用动态优先级调度算法进行划分结果的评价。实验表明,在可重构系统的资源约束等条件下,算法能够有效地实现应用任务图到可重构系统的时空映射。     

Multiobjective layout optimization of robotic cellular manufacturing systems

This paper proposes a multiobjective layout optimization method for the conceptual design of robot cellular manufacturing systems. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. In this paper, the design criteria for robot cellular manufacturing system layout designs are clarified, and objective functions are formulated. Next, layout design candidates are represented using a sequence-pair scheme to avoid interference between assembly system components, and the use of dummy components is proposed to represent layout areas where components are sparse. A multiobjective genetic algorithm is then used to obtain Pareto optimal solutions for the layout optimization problems. Finally, several numerical examples are provided to illustrate the effectiveness and usefulness of the proposed method.     

Synchronization-oriented reconfiguration of FPAI under graduation intelligent manufacturing system in the COVID-19 pandemic and beyond

Companies with manufacturing systems that are more responsive and resilient will be able to survive or even gain market shares in the face of the unpredicted variable of an outbreak similar to the COVID-19 pandemic. Motivated by an industrial company restructuring its manufacturing system with the layout of fixed-position assembly islands (FPAI) during the COVID-19 pandemic, this paper introduces the synchronization-oriented reconfiguration of FPAI under Graduation Intelligent Manufacturing System (GiMS). Inspired by the graduation ceremony, a novel manufacturing mode-Graduation Manufacturing System (GMS) with ticket-based reconfigurable structures, is designed for organizing production operations with simplicity and resilience for the layout of FPAI. The IIoT and digital twin-enabled GiMS is developed for transforming real-time visibility in operations to support the reconfiguration of the manufacturing system. A synchronization-oriented reconfiguration mechanism is proposed to achieve the synchronous interaction among changing customer demand, island configuration, and production activities allocation rapidly and cost-effectively. Cloud services integrating the proposed reconfiguration mechanism are developed for managers and onsite operators for supporting the successful reconfiguration implementation with enhanced operational visibility. Through the case study of an industrial company, the effectiveness of the proposed concept and approach is verified.     

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.

     

面向网络化制造的产品再配置模型

该文提出一个面向网络化制造的产品再配置概念模型．该模型突出配置过程的动态特性,在分析了基于版本模型的部件、配置模型演化方式以及两者在演化过程中的相互影响的基础上,给出在集成产品配置的产品数据管理系统中对部件演化和模型演化进行跟踪和记录的方法,以实现产品再配置．该模型具有较强的时态描述能力,可广泛应用在网络化制造系统中．最后还提出了一个面向网络化制造的产品再配置结果相关度匹配算法．     

制造系统中的单向环型设备布局设计

提出一种优化建模与虚拟现实技术相结合的求解策略，较好地解决了制造系统中的单向环型设备布局问题．研究该问题的固有特性，提出三条定理，构建了一个启发式算法，并实现了一个沉浸式虚拟布局设计的例子．     

基于模糊决策与进化算法的生产设备布局优化

为解决复杂情况下制造系统的生产设备布局优化问题,提出了一种将模糊决策与进化算法相结合的设备布局优化方法。进一步完善了优化模型,优化目标包括总成本最小、设备相邻要求最大化和面积利用率最大化等优化目标;其中总成本最小目标考虑了物料搬运成本,设备重置导致的设备拆装、移动成本,生产停工造成的产能损失成本。该方法考虑了用户对于成本、利用率及相邻性要求等存在的满意度、优先度等模糊情况,基于模糊决策理论,对多目标优化模型进行了模糊化处理,设计了模糊适应度函数,用以根据用户的优先关系评价pareto解集。基于求解模型的特点,对多目标进化算法的染色体编码方式与交叉、变异等遗传操作方式进行改进,以提高求解该模型的实用性与效率。最后以实际案例的优化结果证明了该方法的有效性。     

单线图动态规划最优布局成图技术

提出了一种基于动态规划算法得到布局最优解实现区域电网单线图生成的方法.根据电网空间数据构建拓扑模型,执行广度优先算法得到多个能构成连通图的邻接矩阵以及矩阵遍历序列,根据邻接矩阵宽度计算出能容纳全部设备的正方形范围,并建立了设备最小间距为优化目标的数学模型.提出了动态规划最优布局求解的算法,应用该算法求解布局最优解数组,最后按照最少交叉原则进行正交化处理.应用实例表明通过最优解布局的成图美观且高效.     

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings,lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.     

基于Virtools的墙材生产线可视化设计系统开发

布局问题一直是生产线设计过程中的一个难点问题,生产线布局的优劣直接影响到生产线的生产效率。针对墙材生产企业生产线布局中存在的问题,研究生产线的快速动态布局方法。利用当前虚拟仿真技术的优势,基于虚拟仿真软件Virtools,开发墙材生产线可视化布局设计系统。并且结合某墙材生产企业生产线的布局问题,应用所开发的布局设计系统进行仿真布局,在此基础上利用布局设计系统的生产过程仿真功能对此生产线的生产过程进行仿真模拟,检验生产线布局的合理性,以便修改和优化。     

考虑数据时变性的主动配电网动态网络重构

目前的配电网网架重构主要考虑停电发生时刻单场景下的电网信息,但实际上配电网网架重构贯穿于整个故障修复时期, 仅考虑停电发生时刻的电网信息可能造成后续时段电网过载,因此本文提出计及数据时变性的动态网络重构方案。本文以分布式电源大规模接入下的主动配电网为研究对象,首先基于贪婪法思想提出单一场景下的网络重构方法, 然后利用负荷及各类分布式电源的预测信息,以故障排除周期内能量损失最少为目标建立基于连续场景考虑数据时变性的动态网络重构模型。同时本文引入滚动优化的思想, 对动态网络重构方案进行纠偏,以适应分布式电源的随机波动。最后, 通过仿真分析, 论证了分布式电源对优化系统恢复方案的作用,说明了所提模型和方法的有效性。