柔性制造单元一种多层计划调度方法

柔性制造单元的计划与调度问题已有许多的讨论,但大都针对具体的系统以及对任务做某些假设,本文提出一种多层计划和调度的方法并对每层的功能进行讨论,这种方法的结构可应用于各类单元.同时提出三种机器资源的分配方法.文中给出了仿真结果表明此方法是比较有效的.     

柔性制造单元的一种调度算法

本文提出了一种新的柔性制造单元的调度算法,它在离散周期控制的基础上加上事件中断以适应系统状态的随机干扰,在流控制中,我们不仅决定产品类型的混合比,同时决定每种工件在各加工路径的比。文中给出了一个制造单元的仿真结果,它表明了算法的有效性。     

柔性制造单元的智能调度系统

本文基于递阶控制原理提出了柔性制造单元的一种新的计划与调度方法,并综合采用了理论分析、专家系统技术和仿真技术,建立了一个智能调度系统原型.另外,在制造单元调度问题的描述上采用了状态方程形式,从系统的观点来研究调度问题.并在此基础上建立了仿真模块,对单元的加工过程进行了仿真实验.     

一种基于动态决策块的超启发式跨单元调度方法

对运输能力受限条件下的跨单元调度问题进行分析, 提出一种基于动态决策块和蚁群优化 (Ant colony optimization, ACO) 的超启发式方法, 同时解决跨单元生产调度和运输调度问题. 在传统超启发式方法的基础上, 采用动态决策块策略, 通过蚁群算法合理划分决策块, 并为决策块选择合适的规则. 实验表明, 采用动态决策块策略的超启发式方法比传统的超启发式方法具有更好的性能, 本文所提的方法在最小化加权延迟总和目标方面有较好的优化能力 并且具有较高的计算效率.     

基于混合蛙跳和遗传规划的跨单元调度方法

针对运输能力受限条件下的跨单元问题,提出了一种基于混合蛙跳与遗传规划的超启发式算法.将改进的混合蛙跳算法作为超启发式算法的高层框架,为跨单元调度问题搜索启发式规则,同时利用遗传规划产生可以兼顾多因素的优质规则,用于扩充超启发式算法的规则集.实验表明,提出的算法可以有效地搜索出优异的规则组合,并且通过遗传规划产生的规则可以在很大程度上改善候选规则集,提升算法性能.     

基于成组技术的一种柔性调度方法

研究柔性制造系统中工件的加工调度问题,通过对加工路线作弱化处理来提高调度质量、即在工艺约束条件下确定工序的成组优化。利用遗传算法可以有效地解决这一过程的寻找问题。仿真实验表明方法有效。     

考虑运输能力限制的跨单元调度方法

工件在生产单元之间频繁转移产生了跨单元调度问题.本文结合我国装备制造业的生产实际,提出考虑运输能力的跨单元调度方法,设计了一种基于离散蜂群与决策块结构的超启发式算法.针对传统超启发式算法的局限性提出动态决策块策略, 同时改进传统蜂群算法的侦查蜂策略,使之具有更好的优化性能.实验表明,动态决策块具有比静态决策块更好的性能,算法在优化能力和计算效率的综合性能上优势显著,并且问题的规模越大,优势越明显.     

一种基于服务质量保证的多天线跨层调度方法

多天线技术由于其能显著地提升系统吞吐率而备受关注.基站通过选择当前信道状态最好的用户进行通信能进一步提升系统吞吐率.但系统仅根据用户的信道状态信息调度用户会引起用户服务质量的下降.基于以上考虑.提出了一种基于服务质量保证的跨层调度算法.考虑了两种业务类型,实时业务和非实时业务.为了保证这两种不同业务类型不同的服务质量要求,系统为每个用户赋予一个与之对应的优先级参数.优先级参数随用户的服务状态和信道信息动态变化.调度器根据用户的优先级参数调度用户.仿真结果表明,提出的多用户调度算法能在满足用户服务质量要求的前提下,实现对无线资源的有效利用.     

基于改进变邻域NSGA-Ⅱ的绿色跨单元调度问题

针对绿色背景下跨单元调度存在加工效率低和能源消耗高等问题,建立了以最小化完工时间和全局能耗的多目标数学模型。提出了一种改进变邻域NSGA-Ⅱ算法求解模型。首先引入三层编码表达问题特征,然后设计了考虑运输时间的解码方法,提出一种基于Sigmoid函数的自适应交叉变异率以保证种群多样性,最后构建了三种变邻域结构融入改进后的NSGA-Ⅱ算法来增强局部搜索能力。实验表明,改进后的算法能有效求解模型,运输时间能够协调完工时间和能耗关系。     

基于两阶段调度的制造单元协同调度算法研究

针对单元制造问题;提出了一种基于两阶段的调度算法;通过过程分解和算法优化两方面实现问题求解。调度过程分为“预调度”和“整体调度”两个阶段;对大规模调度进行调度;不仅有效地降低了问题规模;同时制造单元调度结果对实际生产具有现实意义;调度算法采用了“精确”计算和“近似”求解相结合的方式;既提高计算效率又兼顾了全局优化目标。数值实验结果表明了的这一设计思路的有效性。     

Using multi-agent architecture in FMS for dynamic scheduling

The proposed scheduling strategy is based on a multi-agent architecture. Each agent of this architecture is dedicated to a work centre (i.e. a set of resources of the manufacturing system); it selects locally and dynamically the most suitable dispatching rules. Depending on local and global considerations, a new selection is carried out each time a predefined event occurs (for example, a machine becomes available, or a machine breaks down). The selection depends on: (1) primary and secondary performance objectives, (2) the operating conditions, and (3) an analysis of the system state, which aims to detect particular symptoms from the values of certain system variables. We explain how the scheduling strategy is shared out between agents, how each agent performs a local dynamic scheduling by selecting an adequate dispatching rule, and how agents can coordinate their actions to perform a global dynamic scheduling of the manufacturing system. Each agent can be implemented through object-oriented formalisms. The selection method is improved through the optimization of the numerical thresholds used in the detection of symptoms. This approach is compared with the use of SPT, SIX, MOD, CEXSPT and CR/SPT on a jobshop problem, already used in other research works. The results indicate significant improvements.     

A novel approach to determine cell formation,intracellular machine layout and cell layout in the CMS problem based on TOPSIS method

This paper deals with the cellular manufacturing system (CMS) that is based on group technology (GT) concepts. CMS is defined as identifying the similar parts that are processed on the same machines and then grouping them as a cell. The most proposed models for solving CMS problems are focused on cell formation and intracellular machine layout problem while cell layout is considered in few papers. In this paper we apply the multiple attribute decision making (MADM) concept and propose a two-stage method that leads to determine cell formation, intracellular machine layout and cell layout as three basic steps in the design of CMS. In this method, an initial solution is obtained from technique for order preference by similarity to the ideal solution (TOPSIS) and then this solution is improved. The results of the proposed method are compared with well-known approaches that are introduced in literature. These comparisons show that the proposed method offers good solutions for the CMS problem. The computational results are also reported.     

A knowledge-based system for reactive scheduling decision-making in FMS

This paper describes research into the development of an intelligent simulation environment. The environment was used to analyze reactive scheduling scenarios in a specific flexible manufacturing systems (FMS) configuration. Using data from a real FMS, simulation models were created to study the reactive scheduling problem and this work led to the concept of capturing instantaneous FMS status data as snapshot data for analysis. Various intelligent systems were developed and tested to asses their decision-making capabilities. The concepts of History Logging and expert system learning is proposed and these ideas are implemented into the environment to provide decision-making and control across a FMS schedule lifetime. This research proposes an approach for the analysis of reactive scheduling in an FMS. The approach and system that was subsequently developed was based on the principle of automated intelligent decision-making via knowledge elicitation from FMS status data, together with knowledge base augmentation to facilitate a learning ability based on past experiences.     

An integrated model for solving cell formation and cell layout problem simultaneously considering new situations

Cellular manufacturing system (CMS) is one of the group technology (GT) usages. Among the necessary decisions for a successful CMS implementation, cell formation problem (CFP) and cell layout problem (CLP) are two most popular ones. The majority of past studies in CMS discussed on CFPs and some of those focused on CLP ones. A few researchers solve the CPF and CLP simultaneously. In this paper, we present a new integrated mathematical model considering cell formation and cell layout simultaneously. The goal of our model is to group similar parts and corresponding different machines in same cells. Machines sequence in each cell and cell positions is also specified in the system. Moreover, our proposed model considers forward and backtracking movements as well as new assumptions for distances between cells using sequence data and production volume. One appropriate adjusted measure from the literature and two new measures of performance for evaluating solutions are defined. To validate the model, two well-known critical benchmark examples are employed. Computational experiments demonstrate that our proposal is a proficient model and show the effectiveness of our implementation.     

基于Petri网的柔性制造系统无死锁遗传调度算法

对以最小化加工时间为目标的柔性制造系统无死锁调度问题, 提出了一种遗传调度算法. 算法考虑到同类工件具有预先确定的相同加工路径, 而各工序的处理时间与工件有关. 用Petri网对工序和资源分配进行逻辑建模,利用遗传算法, 采用工序自然编码方式, 基于系统的最佳避免死锁Petri网控制器, 检测染色体的可行性, 修复不可行染色体使其对应的调度满足资源约束和无死锁控制约束, 从而保证算法所利用的所有染色体都对应系统的可行调度. 仿真结果表明了算法的可行性和有效性.     

Formation of machine cells and part families in cellular manufacturing systems using a linear assignment algorithm

This paper presents a linear assignment algorithm for machine-cell and part-family formation for the design of cellular manufacturing systems. The present approach begins with the determination of part-family or machine-cell representatives by means of comparing similarity coefficients between parts or machines and finding a set of the least similar parts or machines. Using the group representatives and associated similarity coefficients, a linear assignment model is formulated for solving the formation problem by allocating the remaining parts or machines and maximizing a similarity index. Based on the formulated linear assignment model, a group formation algorithm is developed. The results of a comparative study based on multiple performance criteria and many existing data sets show that the present approach is very effective and efficient, especially in dealing with large-sized problems.     

Deadlock avoidance in flexible flow shops with loops

This paper presents a deadlock-avoidance scheme that was developed during the MASCADA Project—Esprit LTR Project 22728. The deadlock problem occurs in a car body painting shop. From this application, an abstract problem was derived for which a deadlock-avoidance method was developed. The paper presents the deadlock-avoidance scheme and its correctness proof. In addition, implementation issues are introduced and solutions are discussed.     

半导体封装测试生产线排产研究

以某半导体封装测试（Semiconductor assembly and test manufacturing,ATM）企业为研究背景,对半导体封装测试的生产过程进行分析总结,提出一种新的“产能限定混线车间”（Capacity-limit flexible flow-shop,CLFFS）模型作为半导体封装测试生产线的排产模型.通过对半导体封装测试的特殊逻辑处理、排产方法以及排产规则等进行研究,提出采用逻辑约束和调度规则双层优化控制的启发式正序排产算法作为半导体封装测试的总体排产方法,同时针对批准备单处理生产阶段,提出一种新的预测开机控制优化调度方法.最后,结合CLFFS排产模型和所提出的策略方法,给出半导体封装测试排产的应用研究示例与比较,结果证明本文给定的总体排产方法在ATM中具有很好的可行性和业务逻辑嵌入的即便性,同时本文所提出的新的预测开机控制优化调度方法能够很好的缩短生产周期,提高生产效率.     

Locally perceiving hard global constraints in multi-agent scheduling

We propose how to model enterprise facilities (like factories, warehouses, etc.) in a multi-product production/distribution network, capacity management at those facilities, and scheduling agents which act as enterprise managers, taking decisions that affect the available capacity. A coordination mechanism through which scheduling agents can locally perceive hard global temporal constraints is also proposed.