Identification Of Part Families Research Articles

Machine cell formation and part family identification by combined algorithm

Two major problems in the cell manufacturing process are machine cell formation and component family identification. A combined algorithm is proposed in this paper to improve the cellular production system. The aim of this algorithm is to maximise the effectiveness of grouping and efficacy of grouping to reduce the part's travel time and increase production. Grouping efficiency and grouping efficacy are the two most widely utilised measures of quality for the proposed cellular manufacturing systems. The proposed method integrates appropriate production data to shape the device cell and classify the component unit, compared to the well-known approaches. The combined algorithm is increased the grouping efficiency at maximum of 30.85% and it is maximising the grouping efficacy at the maximum of 5%. It will reduce the total travelling time of parts and increase the machine utilisation.

Machine cell formation and part family identification by combined algorithm

Machine cell formation and part family identification by combined algorithm

Design of cellular manufacturing system for power press industry to reduce total travelling time by hybrid algorithm

Part family identification and machine cell formation are the two major steps in cellular manufacturing system. Cell formation is a complex process and there are several approaches for this purpose vis., array based clustering, agglomerative clustering, mathematical programming, graph partitioning and non-traditional methods. The problem with first two approaches is that the quality of the solution is dependent of the initial part machine incidence matrix. A new algorithm for cell formation combining the techniques of array based clustering and agglomerative clustering is proposed. The above proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is the most widely used measures of quality for cellular manufacturing systems. The proposed algorithm is used to form manufacturing cells in a power press industry to increase productivity by reducing total travelling time.

Design of cellular manufacturing system for power press industry to reduce total travelling time by hybrid algorithm

Part family identification and machine cell formation are the two major steps in cellular manufacturing system. Cell formation is a complex process and there are several approaches for this purpose vis., array based clustering, agglomerative clustering, mathematical programming, graph partitioning and non-traditional methods. The problem with first two approaches is that the quality of the solution is dependent of the initial part machine incidence matrix. A new algorithm for cell formation combining the techniques of array based clustering and agglomerative clustering is proposed. The above proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is the most widely used measures of quality for cellular manufacturing systems. The proposed algorithm is used to form manufacturing cells in a power press industry to increase productivity by reducing total travelling time.

Development of Visual Identification System for Classification of Part Family for Medium Scale Industries

The identification of industrial components based on their part family is very much required for the production planning and sequencing. In the present paper, a visual identification system is proposed for identifying the engineering works (or parts or components) based on their part families in group technology, computer vision and artificial intelligence techniques. The computer vision system is responsible for extracting the features of the component. The developed software provides the part family classification code by using a group technology method on the component from the data obtained by the vision system. The classification code can be used for further processing. The algorithms and the design details are deliberated. In this paper, the identification of part families by categorization with vision system is discussed, and it has been used to identify the part families obtaining a 100% accuracy.

Cell Formation and Part Family Identification by Using Traditional Methods

A Combined Algorithm is proposed to form the machine cell and part family identification in the cellular manufacturing system. In the first phase, part families identification, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC). In second phase, Machine cell formation, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC), which is to assign machines into machine cells to produce part families. The above Proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is one of the most widely used measures of quality for Cellular Manufacturing Systems.

Developing a logical model for cellular manufacturing systems by theory of constraints thinking process approach

Performance of cellular manufacturing systems has been rigorously investigated during the last two decades. Many of these studies are quantitative and involve precise modelling. The purpose of this paper is to determine critical factors in cellular manufacturing system (CMS) and to identify relationships between different factors of CMS via systematic approach of the theory of constraint thinking process. For this purpose, potential undesirable effects and critical factors of CMS have been identified through a brainstorming session with experts. Then, factors are analysed using thinking process by examining casual relationships, and some strategies have been suggested based on the critical factors. Findings show that knowledge of managers, management information system, performance evaluation, market demands, work-in-process inventory, number of cells, assigning of workers to cells, and identification of part families are recognised as the critical factors in strategic decision making. The proposed factors and the relationships have been validated by 68 experts using a binomial test.

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Though there are a number of benefits associated with cellular manufacturing systems, its implementation (identification of part families and corresponding machine groups) for real life problems is still a challenging task. To handle the complexity of optimizing multiple objectives and larger size of the problem, most of the researchers in the past two decades or so have focused on developing genetic algorithm (GA) based techniques. Recently this trend has shifted from standard GA to hybrid GA (HGA) based approaches in the quest for greater effectiveness as far as convergence on to the optimum solution is concerned. In order to prove the point, that HGAs possess better convergence abilities than standard GAs, a methodology, initially based on standard GA and later on hybridized with a local search heuristic (LSH), has been developed during this research. Computational experience shows that HGA maintains its accuracy level with increase in problem size, whereas standard GA looses its effectiveness as the problem size grows.

An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems

Cellular manufacturing systems (CMS) are used to improve production flexibility and efficiency. They involve the identification of part families and machine cells so that intercellular movement is minimized and the utilization of the machines within a cell is maximized. Previous research has focused mainly on cell formation problems and their variants; however, only few articles have focused on more practical and complicated problems that simultaneously consider the three critical issues in the CMS-design process, i.e., cell formation, cell layout, and intracellular machine sequence. In this study, a two-stage mathematical programming model is formulated to integrate the three critical issues with the consideration of alternative process routings, operation sequences, and production volume. Next, because of the combinatorial nature of the above model, an efficient tabu search algorithm based on a generalized similarity coefficient is proposed. Computational results from test problems show that our proposed model and solution approach are both effective and efficient. When compared to the mathematical programming approach, which takes more than 112h (LINGO) and 1139s (CPLEX) to solve a set of ten test instances, the proposed algorithm can produce optimal solutions for the same set of test instances in less than 12s.

Particle swarm optimisation in development of component families using classification and coding system: a case study in an Indian manufacturing firm

Component/part family identification is an NP class problem in the extent of group technology (GT). In preceding literature it has been evidenced that part family identification techniques are ordinarily grounded on production flow analysis which typically studies operational requirements, sequences and time required. Recently, various soft-computing-based techniques are heavily attempted to address such problems. However in designing of parts, process planning, these methods are not convenient. To accomplish such issues coding and classification-based techniques are believed to be extremely proficient. This article portrays a minimal and competent nature inspired heuristic approach based on particle swarm optimisation (PSO) to acquire effective component/part families; exploiting part coding scheme and the technique is verified on top of test data as well as industrial data. The simulation outcomes are assessed with the results achieved using simple heuristic clustering method. The experimental results r...

A new approach to the cell formation problem with alternative processing routes and operation sequence

Cellular manufacturing (CM) is an important application of group technology in manufacturing systems. One of the crucial steps in the design of CM is the identification of part families and manufacturing cells. This problem is referred to as cell formation problem (CFP) in the literature. In this article, a solution approach is proposed for CFP, which considers many parameters such as machine requirement, sequence of operations, alternative processing routes, processing time, production volume, budget limitation, cost of machines, etc. Due to the NP-hardness of CFP, it cannot be efficiently solved for medium- to large-sized problems. Thus, a genetic algorithm (GA) is proposed to solve the formulated model. Comparison of the results obtained from the proposed GA to the globally optimum solutions obtained by Lingo Software and those reported in the literature reveals the effectiveness and efficiency of the proposed approach.

Cell formation using fuzzy relational clustering algorithm

Cellular manufacturing is a useful way to improve overall manufacturing performance. Group technology is used to increase the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation is an important step in group technology. It is used in designing good cellular manufacturing systems. The key step in designing any cellular manufacturing system is the identification of part families and machine groups for the creation of cells that uses the similarities between parts in relation to the machines in their manufacture. There are two basic procedures for cell formation in group technology. One is part-family formation and the other is machine–cell formation. In this paper, we apply a fuzzy relational data clustering algorithm to form part families and machine groups. A real data study shows that the proposed approach performs well based on the grouping efficiency proposed by Chandrasekharan and Rajagopalan.

Optimization of the multi-objective dynamic cell formation problem using a scatter search approach

This paper addresses the dynamic cell formation (DCF) problem with multiple conflicting objectives. Recent researches have mainly focus on single-objective cell formation procedures that deals with the identification of part families and associated machine groups for constant demands. However, varying market demands and fluctuations of the business environment have caused cellular manufacturing systems to operate under dynamic conditions. Thus, the optimal configuration of manufacturing cells in each period is different and the reconfiguration of cells is required. This paper proposes a nonlinear multi-objective mathematical model of the DCF problem by giving weighing to three conflicting objectives including the machine relocation cost in the process of reconfiguring cells, the utilization rate of machine capacity, and the total number of intercell moves over the entire planning horizon. To solve the nonlinear multi-objective model, a scatter search approach is developed, which redesigns the common components of scatter search and incorporates diversification generator, global criterion method, local search method, and other improvement mechanisms to provide a wide exploration of the search space through intensification and diversification. The proposed approach is compared with the commercial solver CPLEX on 10 test problems, some of which are large dimensions. Computational results have demonstrated the effectiveness of the scatter search approach.

Design of cellular manufacturing systems using a genetic algorithm method

Cellular Manufacturing (CM) is an important application of group technology in which sets or families of parts are produced in manufacturing cells or a group of various machines, which are physically close together and can process a family of parts. The identification of part families and machine groups in the design of cellular manufacturing systems is commonly referred to as cell design or cell formation. The cells are formed to capture the inherent advantages of group technology such as reduced setup times, reduced in-process inventories, improved product quality, shorter lead time, reduced tool requirements, improved productivity etc. This paper presents the design of cellular manufacturing systems using a genetic algorithm method considering maximization of flexibility and efficiency. An example is included to illustrate the approach.

Similarity coefficient methods applied to the cell formation problem: A taxonomy and review

The initial step in the design of a cellular manufacturing (CM) system is the identification of part families and machine groups and forming manufacturing cells so as to process each part family within a machine group with minimum inter-cellular movements of parts. One methodology to form manufacturing cells is the use of similarity coefficients in conjunction with clustering procedures. In this paper, we give a comprehensive overview and discussion for similarity coefficients developed to date for use in solving the cell formation (CF) problem. More than 160 sources from premier scientific journals, conferences and books have been reviewed. Despite previous studies indicated that similarity coefficients based method (SCM) is more flexible than other CF methods, none of the studies has explained the reason why SCM is more flexible. This paper tries to explain the reason explicitly. We also develop a taxonomy to clarify the definition and usage of various similarity coefficients in designing CM systems. Existing similarity (dissimilarity) coefficients developed so far are mapped onto the taxonomy. Additionally, production information based similarity coefficients are discussed and a historical evolution of these similarity coefficients is outlined. Finally, recommendations for future research are suggested in this paper.

Part family identification using a simple genetic algorithm

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed.

A methodology for designing flexible cellular manufacturing systems

Cell formation in cellular manufacturing deals with the identification of machines that can be grouped to create manufacturing cells and the identification of part families to be processed within each cell. Dynamic and random variations in part demands can negatively impact cell performance by creating unstable machine utilizations. The purpose of this paper is to introduce and illustrate an interactive cell formation method that can be used to design 'flexible' cells. Flexibility in this context refers to routing flexibility (i.e., the ability for the cellular system to process parts within multiple cells) and demand flexibility (i.e., the ability of the cell system to respond quickly to changes in part demand and part mix). Through an experimental analysis using multiple data sets, we also validate the procedure and provide guidelines for parameter settings depending upon the type of flexibility of interest to the user. Finally, trade-offs and interdependences between alternative types of flexibility in the context of cellular systems are illustrated.

A METHODOLOGY FOR DESIGNING FLEXIBLE CELLULAR MANUFACTURING SYSTEMS

Cell formation in cellular manufacturing deals with the identification of machines that can be grouped to create manufacturing cells and the identification of part families to be processed within each cell. Dynamic and random variations in part demands can negatively impact cell performance by creating unstable machine utilizations. The purpose of this paper is to introduce and illustrate an interactive cell formation method that can be used to design ‘flexible’ cells. Flexibility in this context refers to routing flexibility (i.e., the ability for the cellular system to process parts within multiple cells) and demand flexibility (i.e., the ability of the cell system to respond quickly to changes in part demand and part mix). Through an experimental analysis using multiple data sets, we also validate the procedure and provide guidelines for parameter settings depending upon the type of flexibility of interest to the user. Finally, trade-offs and interdependences between alternative types of flexibility in the context of cellular systems are illustrated.

Moments-based clustering techniques for manufacturing cell formation

Abstract Clustering and data organization algorithms and array sorting methods are most widely used in solving machine-part grouping problems in Group Technology (GT). One of the most recent search algorithms for part-family identification is the close-neighbour search algorithm. However the method does not suggest a solution that tries to eliminate exceptional elements after the block diagonal structure is produced. This paper presents a Method of Moments approach such as Weighted Moment, End Load Ratio and Differential Moment Methods to solve this problem. The effectiveness of these algorithms is tested against sixteen problems from the literature. The results show that the algorithms are very efficient and reliable.

A general search algorithm for cell formation in group technology

Abstract Group Technology (GT) is a manufacturing approach, which organizes and uses the information about an item's similarity (parts and/or machines) to enhance efficiency and effectiveness of batch manufacturing systems. The application of group technology to manufacturing requires the identification of part families and formation of associated machine-cells. One approach is the Similarity Coefficient Method (SCM), an effective clustering technique for forming machine cells. SCM involves a hierarchical machine grouping process in accordance with computed ‘similarity coefficients’. While SCM is capable of incorporating manufacturing data into the machine-part grouping process, it is very sensitive to the data to be clustered (Chan and Milner 1982). It has been argued that for SCM to be meaningful, all machines must process approximately the same numbers of parts (Chan and Milner 1982).We present a new approach, based on artificial intelligence principles, to overcome some of these problems by incorporating an evaluation function into the grouping process. Our goal is to provide a method that is both practical and flexible in its use for the process of cell formation. Our method uses the similarity matrix to generate the feasible machine groups. Then an evaluation function is applied to select a machine-cell arrangement through an iterative process. The approach features a graph-based representation (N-tuple) to represent the problem and illustrate the solution strategies. Also, we develop an algorithm to search for the most promising machine groups from the graph. Compared with Single Linkage Clustering and Average Linkage Clustering approaches, our approach attains comparable or better results