Genetic cell formation using ratio level data in cellular manufacturing systems

Manufacturing cell formation is a useful strategy in batch type production industries for enhancing productivity and flexibility. The basic idea rests on grouping the parts into part families and the machines into machine cells. Most of the literature used zero-one incidence matrix representing the part visiting a particular machine as one and zero otherwise. The output is generated in the form of block diagonal structure where each block represents a machine cell and a part family. In such models real life production factors such as operation time and sequence of operations are not accounted for. In this paper, the operational time of the parts required for processing in the machines is considered. It is attempted to develop an algorithm using genetic algorithm (GA) with a combined objective of minimizing the total cell load variation and the exceptional elements. The results are compared with the solutions obtained from K-means clustering and C-linkage clustering algorithms.     

An ant-based clustering algorithm for manufacturing cell design

This paper proposes a new part clustering algorithm that uses the concept of ant-based clustering in order to resolve machine cell formation problems. The three-phase algorithm mainly utilizes distributed agents which mimic the way real ants collect similar objects to form meaningful piles. In the first phase, an ant-based clustering model is adopted to form the initial part families. For the purpose of part clustering, a part similarity coefficient is modified and used in the similarity density function of the model. In the second phase, the K-means method is employed in order to achieve a better grouping result. In the third phase, artificial ants are used again to merge the small, refined part families into larger part families in a hierarchical manner. This would increase the flexibility of determining the number of final part families for the factory layout designer. The proposed algorithm has been developed into a software system called the ant-based part clustering system (APCS). In addition to part family formation, APCS performs the tasks of machine assignment and performance evaluation. Finally, performance evaluation of the proposed algorithm was conducted by testing some well-known problems from literature. The evaluation results show that the algorithm is able to solve the cell formation problems effectively.     

A flow matrix-based heuristic algorithm for cell formation and layout design in cellular manufacturing system

Cellular manufacturing is a successful application of group technology (GT) concepts. The aim of a cellular manufacturing system (CMS) is to identify similar manufacturing processes and features where machines are grouped into machine cells based on their contributions to the production process. In the last three decades of research in cell formation, researchers have mainly used zero-one machine component incidence matrix as the input data for the problem. However, recently efforts have been made to use other data structures, such as interval data and ordinal data (consisting of sequence of processing). Sequence data provide valuable information about the flow patterns of various jobs in a manufacturing system. This study develops a heuristic algorithm based on flow matrix for cell formation and layout design in a simultaneous fashion using sequence data. The numerical results of the algorithm on the available problems in the literature indicate the usefulness of the algorithm regarding to performance indices.     

Machine cell formation for cellular manufacturing systems using an ant colony system approach

The aim of a cellular manufacturing system is to group parts that have similar processing needs into part families and machines that meet these needs into machine cells. This paper addresses the problem of grouping machines with the objective of minimizing the total cell load variation and the total intercellular moves. The parameters considered include demands for number of parts, routing sequences, processing time, machine capacities, and machine workload status. For grouping the machines, an ant colony system (ACS) approach is proposed. The computational procedure of the approach is explained with a numerical illustration. Large problems with up to 40 machines and 100 part types are tested and analyzed. The results of ACS are compared with the results obtained from a genetic algorithm (GA), and it is observed that its performance is better than that of GA.     

An automatic clustering algorithm suitable for use by a computer-based tool for the design,management and continuous improvement of cellular manufacturing systems

As part of the development of Celled, a tool for the design, management and continuous improvement of cellular manufacturing systems, a need was identified for a fully automatic clustering algorithm that takes the information it needs from the Cellect database, carries out clustering and returns the cell configurations back to the user for further analysis; all without user interaction. To achieve this, an algorithm called Black Box Clustering was created. This is a modified End Load Ratio algorithm aided by the use of similarity coeffi- cients that carry out rearrangement of a workstation-part matrix to obtain a block diagonal form which is marked off to identify workstation groups and part families. This paper describes the development of Black Box Clustering and demonstrates that when tested, the algorithm was shown to be effective and versatile.     

Design of cellular manufacturing systems using objective functional clustering algorithms

Cellular manufacturing (CM) has emerged as an alternative to conventional batch-type manufacturing owing to the former's capability of reducing set-up times, in-process inventories and throughput times. It provides the basis for implementation of just-in-time (JIT) and flexible manufacturing systems (FMS). The machine-part group formation is an important issue in the design of CMSs. This paper presents objective functional clustering algorithms for cell formation problems in the design of cellular manufacturing systems. A deterministic objective functional algorithm (hard clustering) and a fuzzy objective functional algorithm (fuzzy clustering) are used to form the part families and machine cells simultaneously. A collection of data sets from open literature is used to test these algorithms. A software package has been developed to verify the implementation.     

An hybrid procedure for machine duplication in cellular manufacturing systems

Cellular manufacturing represents an effective and even more applied alternative in production system organization especially when line or batch-type production is not economically convenient or technically feasible. This is particularly true when a wide range of quite similar items need to be produced in small lot sizes with frequent and expensive setups. Cellular manufacturing is supported by the so-called cell formation problem whose aim is forming part groups to be assigned to manufacturing cells, composed by a defined subset of machines, so that the sum of intercellular flow costs and direct intra-cell costs is minimized. An effective approach to form manufacturing cells is based on cluster analysis and on the evaluation of similarity coefficients: machines are grouped by the application of clustering techniques and finally parts are assigned to clusters. The aim of this paper is to present a hybrid and original procedure for the cell formation problem based on cluster analysis and integer linear programming. In particular, an integer linear programming model optimizes and re-arranges the configuration of the cells as the result of the application of a hierarchical clustering algorithm. The proposed model evaluates the possibility of duplicating a machine in one or more cells in order to reach the best trade-off between direct cell costs and indirect costs caused by intercellular flows. As a result, all work areas are correctly designed with the optimal number of machines of each type and total production system cost is quantified. The presentation and discussion of the proposed approach is supported by the illustration of a significant case study which takes inspiration from the literature.     

Solving cell formation problem in cellular manufacturing using ant-colony-based optimization

Grouping the machines and parts in a cellular manufacturing system based on similarities is known as the cell formation problem. It has been shown that cell formation problem is a NP-hard problem. In this paper, the ant colony optimization (ACO) method is used as an evolutionary approach to solve the cell formation problem. This model uses a P?=?[P _ij ] _{(C)?×?(M?+?P)} pheromone matrix in which C, M, and P are the number of cells, machines, and parts, respectively. In order to represent the sequence of operations, the machine–part incidence matrix entries are considered as positive integers. Performance of the proposed algorithm is tested on some benchmark problems existing in the literature to show the applicability and effectiveness of the proposed model. Comparison of the solutions obtained by the proposed algorithm with those reported in the literature indicates that application of the proposed algorithm has resulted in 5.73% improvement in the total number of intercellular movements and voids on average.     

Adaptive clustering algorithm for recycling cell formation: An Application of fuzzy ART neural networks

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end-of-life phase. Disposal products have the uncertainties of product status by usage influences during product use phase, and recycling cells are formed design, process and usage attributes. In order to deal with the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. Fuzzy C-mean algorithm and a heuristic approach based on fuzzyART neural network is suggested. Especially, the modified FuzzyART neural network is shown that it has a good clustering results and gives an extension for systematically generating alternative solutions in the recycling cell formation problem. Disposal refrigerators are shown as examples.     

A tradeoff analysis between process plan selection and cell formation in cellular manufacturing

In this paper, an algorithm to evaluate the tradeoff between conflicting objectives in process plan selection and cell formation is developed. Consideration of the minimisation of intercell material movement in cellular manufacturing is necessary but not in itself sufficient to produce a system for which the total work content is minimised. Solving the process plan selection and the cell formation problem for all possible alternative process plans is a time-consuming task, and therefore not economically justifiable. The algorithm is illustrated through the use of a sample problem that shows how it is possible to create a cell using the algorithm presented in this paper.     

A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem

The manufacturing cell formation problem, with the aim of grouping parts into families and machines into cells, is considered with the objective of maximizing grouping efficacy. A new solution approach based on the particle swarm optimization (PSO) algorithm is presented for the problem. Unlike the original PSO algorithm which works with arithmetic operators and scalars, the new algorithm uses group-based operators, in place of arithmetic operators, in the body of the updating equations analogous to those of the classical PSO equations (given the fact that the cell formation problem is essentially a grouping problem, all operators in the new algorithm work with constructed cells (groups) rather than parts/machines (objects), isolatedly). We benchmark a set of 40 test problem instances from previous researches and do comparisons between the new algorithm and existing algorithms. We also compare the performance of our algorithm when it is hybridized with a local search module. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution’s quality presented in the literature.     

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.     

Management of product variety in cellular manufacturing systems

In today’s markets, non-uniform, customized products complicate the manufacturing processes significantly. In this paper, we propose a cellular manufacturing system design model to manage product variety by integrating with the technology selection decision. The proposed model determines the product families and machine groups while deciding the technology of each cell individually. Hedging against changing market dynamics leads us to the use of flexible machining systems and dedicated manufacturing systems at the same facility. In order to integrate the market characteristics in our model, we proposed a new cost function. Further, we modified a well known similarity measure in order to handle the operational capability of the available technology. In the paper, our hybrid technology approach is presented via a multi-objective mathematical model. A filtered-beam based local search heuristic is proposed to solve the problem efficiently. We compare the proposed approach with a dedicated technology model and showed that the improvement with the proposed hybrid technology approach is greater than 100% in unstable markets requiring high product varieties, regardless of the volumes of the products.     

A genetic algorithm for scheduling flexible manufacturing systems

General job shop scheduling and rescheduling with alternative route choices for an FMS environment is addressed in this paper. A genetic algorithm is proposed to derive an optimal combination of priority dispatching rules pdrs (independentpdrs one each for one Work Cell WC), to resolve the conflict among the contending jobs in the Giffler and Thompson GT procedure. The performance is compared with regard to makes-pan criteria and computational time. The optimal WCwise-pdr is proved to be efficient in providing optimal solutions in a reasonable computational time. Also, the proposed GA based heuristic method is extended to revise schedules on the arrival of new jobs, and on the failure of equipment to address the dynamic operation mode of flexible manufacturing systems. An iterative search technique is proposed to find the best route choice for all operations to provide a feasible and optimal solution. The applicability and usefulness of the proposed methodology for the operation and control of FMS in real-time are illustrated with examples. The scope of the genetic search process and future research directions are discussed.     

Consensual cell searching in cellular manufacturing design

This paper presents an approach to consensual cell formation in cellular manufacturing design. The cell formation is a multi-measurable criteria problem. However, a number of factors limit the possibility of simultaneously considering multiple criteria during the cell formation. During the cellular manufacturing system design, the engineers must know the cells responding individually to each criterion. Thus, for discerning the consensus in multiple-criteria cell formation problem, a formal approach is proposed. The problem of consensus searching is formulated as the problem of searching the best overlap between potential solutions, called conjecture, under some constraints. Two models are proposed. The first model is devoted to the consensus conjecture which satisfies globally the set of measurable criteria. The second model addresses the problem of nucleus recognition. Thus, the problem of consensus searching is an alternative to the multiple-criteria problem optimisation. The results confirm the assumption that the cell formation is a process of searching and evaluating the consensual information.     

作业车间类型多机器人制造单元调度算法

针对带多台机器人的作业车间类型机器人制造单元调度问题的特点,研究了以最小化最大完工时间为优化目标、将邻域搜索策略与启发式规则相结合的混合遗传算法,建立了作业车间类型多机器人制造单元调度问题的数学优化模型和析取图模型。基于析取图关键路径,采取移动机床块、交换机器人块、调整任务分配来构建搜索邻域;用启发式搬运工序插入法和启发式搬运任务分配法相结合的三层调度方法初始化种群;将基于邻域结构的局部搜索算法和基于三层调度的遗传算法相结合,有效实现问题的求解。通过基准算例测试表明,混合遗传算法有效并优于其他算法。     

基于成本模型的制造单元成组方案优选方法

考虑了设备投资成本、准备成本、物料传输成本等方面,提出了一个简单易用的成本模型,定量地计算制造单元建设和运行的成本,为可重构制造单元配置提供优选方案评估依据。算例说明了该成本模型的有效性。     

一种多维不确定性数据流聚类算法

目前在很多不确定性数据流聚类方法研究中,存在着聚类模型和数据流的数据模型失配问题,且它们往往假定不确定性数据的概率密度函数、概率分布函数或者概率是已知的,然而这些信息在实际系统中很难获得.鉴于此,本文提出一种基于区间数的多维不确定性数据流聚类算法(UIDMicro).在该算法中,首先利用区间数结合不确定性数据的统计信息表示多维不确定性数据流,然后采用“当前簇”和“候选簇”两层簇窗口对不确定性数据流进行聚类,通过动态调整两层簇窗口实现聚类模型和数据模型的实时匹配.实验结果表明,该方法具有较高的聚类精度和处理效率.     

制造系统数据采集技术研究

数据采集技术是多学科交叉、融合的产物,及时将相关学科的研究新成果应用到数据采集技术中,研究新的数据采集技术与方法对于制造企业采用先进制造技术实现制造自动化、全面提高产品质量、增强企业竞争力有着重要意义。本文以制造系统为背景,应用分布式人工智能技术,研究了一种基于智能主体的制造系统数据采集模型,并详细介绍了该模型的结构、原理与特点。