敏捷制造下合作伙伴选择的有效算法

为实现虚拟企业实践中有效的合作伙伴选择，根据制造成本和产品上市时间这两个重要因素建立了问题优化模型。基于此模型，给出了一个两阶段求解算法。该算法首先通过一个多项式的精确算法求得最早完成时间的解，保证了解的可行性。然后以最早完成时间作为可行初始解，利用一个启发式算法对解进行改进。该算法最终使得在完成时间的约束下制造成本最小。最后给出了一个应用案例说明了该算法的有效性。     

敏捷制造中制造资源重构问题建模

敏捷制造是２１世纪的制造模式,这可以异地开发产品,以充分利用各个企业的资源优势。建立联盟是实施敏捷制造的关键。从产品开发的角度看,联盟的建立就是资源的重组,而车间的重构是制造资源重组的重要内容。本文从产品开发任务定义和分解出发,建立了制造资源重构的数学模型,提出了基于因特网的制造资源重构的机理和方法,同时给出了优化问题的启发式算法。     

敏捷制造单元动态重构研究

重构是实现敏捷制造单元的关键技术之一。本文首先给出了敏捷制造单元的定义 ,然后明确了敏捷制造单元重构的必要性 ,接着对敏捷制造单元重构问题进行了深入的分析 ,明确了重构问题的实质是制造资源的选择和组织问题 ,在此基础上提出了一种基于整数规划和排序聚类的重构算法 ,最后 ,用实例对算法加以验证     

可重构制造系统中基于相似性理论的虚拟制造单元生成方法

为实现可重构制造系统的逻辑重构，应用相似性理论对多种工件的多工艺路线进行相似性分析，提出了基于工艺相似性理论的设备集合模式的概念。建立了基于设备集合模式、图论和集合论的虚拟制造单元生成的框架模型，以及生成的有关定义、规则及算法。生成的有关定义包括伪设备、例外设备、设备集合模式识别和工艺路线更新等；生成规则包括候选首末设备生成和侯选制造单元处理两类规则；生成算法包括初始输入数据处理、候选制造单元生成、工艺路线更新和例外设备处理4个子算法。用Visual C 实现了所给出的算法，并通过示例测试验证了算法的正确性。     

可重构制造系统工艺路线与系统布局设计研究

为使可重构制造系统的重构过程更加快速有效,提出了一种工艺路线和系统布局设计的方法.首先,基于图论构建了可重构制造系统工艺路线的有向网络模型,并采用Dijkstra算法和双向扫视算法,进行最优工艺路线和备选工艺路线的选择;然后,以工艺路线为基础,依据排队论中的相关理论,对制造系统的布局进行优化设计,以获得可重构制造系统布局方案.实例表明,该设计能够适应可重构制造系统快速多变的特征,提高对制造系统已有资源的利用率.     

可重构制造系统建模方法

对可重构制造系统建模方法,包括基于Petri网的可重构制造系统建模、基于排队网络的可重构制造系统建模、基于马尔可夫链的可重构制造系统建模、基于多Agent理论的可重构制造系统建模等方法的研究现状进行了系统的介绍,并进行了分析和比较,最后给出了可重构制造系统建模的发展方向。     

可重构制造系统研究与发展

在竞争日益激烈的环境下，制造企业必须能够快速地响应市场需求的变化，可重构制造系统可以达到这一目的。本文从可重构制造系统的定义和特点、可重构制造系统与现有制造系统的对比、可重构制造系统研究的现状等方面综述了可重构制造系统研究的进展。     

面向可重构制造系统的生产资源信息管理系统

针对可重构制造系统的特征和研究内容,结合企业实际,建立了基于可重构制造单元的系统级可重构制造系统的生产资源信息模型,并将其应用于面向可重构制造系统的生产资源信息管理系统中,进而实现对生产资源的有效管理。     

敏捷制造单元重构算法

敏捷制造单元动态重构的实质是制造资源的优化选择及制造单元的构建。首先运用规划理论解决了针对待加工任务的可利用制造资源优化选择问题。其次，运用模糊数学理论，提出了基于粒度适中原则的模糊聚类方法，完成制造资源及待加工工件的聚合，实现了敏捷制造单元的构建。实例表明，设计的两阶段重构算法可有效地完成敏捷制造单元的重构过程，实现资源面向任务的动态优化。     

面向订单制造的可重构制造系统中虚拟制造单元构建技术

为适应单件、小批、个性化和市场需求动态多变的特点.研究了多种不同交货期生产订单并存时的可重构制造系统中虚拟制造单元构建问题.构造了以连续加工产品间相似系数之和最大、工作加班时间最少、单元的封闭性最好(工件跨单元搬运次数最少)、制造系统重构成本最小及设备生产负荷均衡为目标的非线性多目标0-1整数规划模型.采用两阶段的求解策略进行求解.在第一阶段采用启发式方法对非瓶颈设备和工艺进行预处理,以缩小问题解的搜索空间;第二阶段采用一种基于网格计算的分布式平行协同多目标粒子群算法,随机搜索Pareto优化解集.最后,利用globus 4.0 工具箱搭建计算网格和Java语言实现了算法.从生产实际出发给出了算例,证明结果可行,从而验证了算法的有效性.     

基于双链遗传算法的网络化制造资源优化配置

网络制造环境下合作伙伴的选择采用的主要方法是对被选企业的一些宏观信息进行评价,该方法无法保证在形成动态联盟后制造系统的运行效率,并且要求预先确定任务的分配,无法保证各个企业之间任务分配的合理性.针对这样的问题,从更底层的制造资源优化配置入手,研究制造资源如何在网络制造环境下优化配置,从而确定合作伙伴和各合作伙伴所需要承担的生产任务.任务分配涉及企业-资源-任务三者之间的匹配,使得原本就是NP难题的调度问题更加复杂,借鉴传统遗传算法和生物学进化原理,提出一种双链遗传算法来解决这样的规划问题,相对于传统遗传算法这样算法的效率更高,编码解码更容易,可以解决网络制造资源优化配置问题.     

遗传算法在单元制造系统集成设计中的应用

本文基于单元制造系统集成优化模型,提出了实现单元地系统集成设计的遗传算法设计方案,并通过仿真实验验证了所设计算法的有效性。     

智能制造单元生产调度算法研究

针对智能制造单元的调度问题,建立了相关的数学模型,并提出了一种改进的遗传算法来求解该问题。在算法的初始过程中加入爬山算法来提高初始解的质量,并在交叉操作时,对机器选择部分染色体采用一种由当前种群最优个体指导交叉的方法来改进交叉算子,以扩大算法的搜索能力。最后采用一个实例对该算法进行仿真验证,结果表明其性能比传统遗传算法更优。     

Production Scheduling and Cell Formation for Virtual Cellular Manufacturing Systems

In this paper, an approach using the concept of genetic algorithms is proposed as a powerful but simple means of scheduling the manufacturing operations of a virtual cellular manufacturing system (VCMS). A mathematical model is developed to describe the characteristics of a VCMS, which includes the constraints related to the delivery due dates of the various products and the maximum capacities of the manufacturing resources. The objectives are to set up virtual manufacturing cells and to formulate feasible production schedules for all manufacturing operations, in order to minimise the total material and component travelling distance incurred in manufacturing the products. A new genetic based scheduling algorithm is proposed as an optimisation tool to determine the solution. The proposed algorithm differs from the conventional genetic algorithms in that the populations of the candidate solutions consist of individuals from various age-groups, and each individual is incorporated with an age attribute to enable its birth and survival rates to be governed by predefined ageing patterns. By generating the evolution of the populations with the genetic operators of selection, crossover and mutation, the proposed approach provides excellent results by maintaining a better balance between the exploitation and the exploration of the solution space, and thus improves the computational speed and the solution quality. The condition ensuring stable search performance is also derived. The superiority of the proposed algorithm is illustrated by solving the production-scheduling and cell-formation problems for a virtual cellular manufacturing system, and the results are compared with those obtained by using a conventional optimisation technique.     

Integrated tolerance optimisation with simulated annealing

Tolerance is one of the most important parameters in design and manufacturing. The allocation of design and machining tolerances has a significant impact on manufacturing cost and product quality. This article presents an analytical model for simultaneously allocating design and machining tolerances based on the least-manufacturing-cost criterion. In this study, tolerance allocation is formulated as a non-linear optimisation model based on the cost-tolerance relationship. A new global optimisation algorithm, simulated annealing, is employed to solve the non-linear programming problem. An example for illustrating the optimisation model and the solution procedure is provided.     

基于多代理的敏捷制造单元调度研究

在分析敏捷环境下敏捷制造单元调度特点的基础上 ,提出了基于多代理协作的敏捷制造单元模型 ,并根据该模型设计了一种混合遗传模拟退火 (SAGA)单元调度算法。并给出SAGA和GA两种方法的比较结果。应用实例表明 ,该方法调度性能良好 ,调度过程快 ,支持任务的随机加入 ,为制造企业快速有效的响应市场 ,提高敏捷性提供了强有力的理论与技术支持。     

基于改进混合蚁群算法的大批量定制协同制造链优化 

针对大批量定制协同制造链优化配置问题,以质量、成本、时间、提前交货期和协同度为评价指标,以最小化评估指标综合值为目标,建立了大批量定制协同制造链的优化配置数学模型,构造了适合该模型特征的改进混合蚁群算法并阐述了其求解过程。范例计算及对比分析表明,该方法是有效的、可行的。研究结果为大批量定制协同制造链的优化配置提供了可参考的模型和求解算法。     

Application of an Autonomous Agent Network to Support the Architecture of a Holonic Manufacturing System

To remain a competitive force in the world market, manufacturing enterprises must design and produce new products in an effective way. To reduce the product launching time, manufacturing enterprises must be versatile, open to changes, and capable of designing and modifying their own facilities and processes efficiently for the design of new products. In this context, the concept of autonomous, adaptive, cognitive and cooperating entities known as "holons" is conceived which leads to the evolution of a holonic manufacturing system (HMS) where highly distributed control paradigms are adopted to alleviate the problems related to frequent process disturbances. In order to streamline the functioning of an HMS, it is necessary to form an efficient, flexible and responsive network of agents, which are intra-holonic entities that inherit the same characteristics as the holons. This network of agents can be termed an autonomous agent network. The agent is formed by the parties, which are the functional units of the holonic manufacturing system. The aim of this paper is to specify the communication protocols and subsequently synthesise and cluster the individual parties into autonomous agents in accordance with the basic constraints of a holonic manufacturing system. Here a fuzzy c-means clustering algorithm is proposed to club the parties to capture effectively the uncertainty and imprecision associated with them. Besides the grouping of the parties to form agents, the proposed fuzzy-based clustering algorithm ensures that the agents formed are more amenable to the dynamic environment prevailing on the shop floor of present day automated manufacturing systems and thus makes the essence of a holonic manufacturing system successful . Keeping in mind the imprecision, uncertainty, and conflicting nature of objectives, the proposed approach aptly models the problem, and its applicability is exemplified by a test problem. ID="A1"Correspondance and offprint requests to: M. K. Tiwari, Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Hatia, Ranchi 834 003, India. E-mail: mkt09@hotmail.com     

Rule-based production planning in flexible manufacturing systems

Production planning in flexible manufacturing may require the solution of a large-scale discrete-event dynamic stochastic optimization problem, due to the complexity of the system to be optimized, and to the occurrence of discrete events (new orders and hard failures). The production planning problem is here approached for a multistage multipart-type manufacturing shop, where each work cell can share its processing time among the different types of parts. The solution of this problem is obtained by an open-loop-feedback control strategy, updated each time a new event occurs. At each event time, two coupled problems are solved: 1) a product-order scheduling problem, conditioned on estimated values of the production capacities of all component work cells; and 2) a production-capacity planning problem, conditioned on predefined sequences of the product orders to be processed. In particular, the article aims at defining a production planning procedure that integrates both analytical tools, derived from mathematical programming, and knowledge-based rules, coming from experience. The objective is to formulate a hybrid (knowledge-based/analytical) planning architecture, and to analyze its use for multicell multipart-type manufacturing systems.