Cell Formation In Group Technology Research Articles

A New Approach to Cell Formation in Group Technology with Alternative Solutions

The concept of group technology (GT) in design of manufacturing systems is explained. Achieving the highly appreciated benefits of applying GT mainly depends on proper design of manufacturing cells within a manufacturing system. As the ideal design of manufacturing cells is practically unattainable, research work aims at optimizing this process within a number of objectives. This paper categorizes and briefly introduces some of the previously published research work. Then, a proposed approach for design of manufacturing cells is introduced with the objective of minimizing the number of exceptional elements, needing processing in more than one cell, thus reducing the material handling needs between cells which reduces efficiency and increases costs. The mathematical model for this approach, based on the 0-1 incidence matrix of parts/machines, is explained. Five performance measures for the approaches of design of cells are applied to the proposed approach. Twelve previously published bench mark problems with eighty eight different solutions, based upon different approaches from literature, were used in the comparison and evaluation of the performance of the proposed approach/ model. The result of comparison indicated that the proposed approach gave better solutions in forty nine percent of the cases, with equal performance in the remaining cases. Thus, the proposed approach is a highly valued addition to the available approaches.

A New Approach To Cell Formation In Group Technology With Alternative Solutions

A New Approach To Cell Formation In Group Technology With Alternative Solutions

An exact model for cell formation in group technology

Despite the long history of the cell formation problem (CF) and availability of dozens of approaches, very few of them explicitly optimize the objective of cell formation. These scarce approaches usually lead to intractable formulations that can be solved only heuristically for practical instances. In contrast, we show that CF can be explicitly modelled via the minimum multicut problem and solved to optimality in practice (for moderately sized instances). We consider several real-world constraints that can be included into the proposed formulations and provide experimental results with real manufacturing data.

Cell formation in group technology using constraint programming and Boolean satisfiability

Cell formation consists in organizing a plant as a set of cells, each of them containing machines that process similar types or families of parts. The idea is to minimize the part flow among cells in order to reduce costs and increase productivity. The literature presents different approaches devoted to solve this problem, which are mainly based on mathematical programming and on evolutionary computing. Mathematical programming can guarantee a global optimal solution, however at a higher computational cost than an evolutionary algorithm, which can assure a good enough optimum in a fixed amount of time. In this paper, we model and solve this problem by using state-of-the-art constraint programming (CP) techniques and Boolean satisfiability (SAT) technology. We present different experimental results that demonstrate the efficiency of the proposed optimization models. Indeed, CP and SAT implementations are able to reach the global optima in all tested instances and in competitive runtime.

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.

Part-machine cell formation in group technology using a simulated annealing-based meta-heuristic

The part-machine cell formation problem (PMCFP) is a crucial step in the design of a cellular manufacturing system and has received considerable research attention over the last five decades. This study proposes a simulated annealing-based meta-heuristic for solving the PMCFP. The effectiveness of the proposed approach is compared to conventional algorithms across a set of PMCFPs available in the literature. Computational results using four types of performance measures show that the proposed simulated annealing-based meta-heuristic is highly effective by comparison with conventional algorithms for PMCFPs on the same test problems.

Machine-part cell formation in group technology using a modified ART1 method

Abstract Group Technology (GT) is a useful way of increasing the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation (CF) is a key step in GT. It is used in designing good cellular manufacturing systems using the similarities between parts in relation to the machines in their manufacture. It can identify part families and machine groups. Recently, neural networks (NNs) have been widely applied in GT due to their robust and adaptive nature. NNs are very suitable in CF with a wide variety of real applications. Although Dagli and Huggahalli adopted the ART1 network with an application in machine-part CF, there are still several drawbacks to this approach. To address these concerns, we propose a modified ART1 neural learning algorithm. In our modified ART1, the vigilance parameter can be simply estimated by the data so that it is more efficient and reliable than Dagli and Huggahalli’s method for selecting a vigilance value. We then apply the proposed algorithm to machine-part CF in GT. Several examples are presented to illustrate its efficiency. In comparison with Dagli and Huggahalli’s method based on the performance measure proposed by Chandrasekaran and Rajagopalan, our modified ART1 neural learning algorithm provides better results. Overall, the proposed algorithm is vigilance parameter-free and very efficient to use in CF with a wide variety of machine/part matrices.

Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications

Group technology (GT) is a useful way to increase productivity with high quality in flexible manufacturing systems. Cell formation (CF) is a key step in GT. It is used to design a good cellular manufacturing system that uses the similarity measure between parts and machines so that it can identify part families and machine groups. Recently, fuzzy clustering has been applied in GT because the fuzzy clustering algorithm can present partial memberships for part–machine cells so that it is suitably used in cellular manufacturing systems for a variety of real cases. However, these fuzzy clustering algorithms are only used for numeric data of parts and machines. In this paper, we apply a mixed-variable fuzzy clustering algorithm, called mixed-variable fuzzy c-means (MVFCM), to CF in GT. According to real application examples, the MVFCM algorithm gives good results when it is applied to the mixed-variable types of CF data.

Tabu search approach based on a similarity coefficient for cell formation in generalized group technology

Both a similarity coefficient method (SCM)-based algorithm and meta-heuristics have been widely applied to various cell formation problems; however, few studies have explored the combination of the two methods. This paper addresses a hybrid algorithm, in which, based on the initial solution produced by a new SCM-based hierarchical clustering method, a fast and effective tabu search approach is presented to solve cell formation in group technology (GT). The proposed algorithm is applied to several problems from literature and a group of the randomly generated instances with alternative process plans and compared with simulated annealing (SA) and other TS; the results demonstrate that the proposed algorithm is available and efficient for cell formation in generalized GT.

Fractional cell formation in group technology using modified ART1 neural networks

Group technology (GT) is a manufacturing philosophy that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted successfully to minimize the number of exceptional elements. The fractional cell formation problem is solved using modified adaptive resonance theory1 network (ART1). The input to the modified ART1 is machine-part incidence matrix comprising of the binary digits 0 and 1. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or superior to other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.

Cell formation in group technology: Review, evaluation and directions for future research

This paper discusses and reviews a fundamental issue in cellular manufacturing—cell formation. This problem is of strategic and operational importance in that it affects the fundamental structure and the overall layout of a cellular manufacturing system. We first provide a comprehensive mathematical formulation of the cell formation problem and then propose a methodology-based classification of prior research. This classification is used in reviewing the most recent literature on the cell formation problem. Based on a comparison and critical evaluation, we highlight the shortcomings of current approaches and also outline directions for future research.

Cell formation in group technology: A combinatorial search approach

This paper addresses the cell formation problem in group technology (GT). We develop two heuristic methods for generating solutions to the problem. These methods are based on two powerful combinatorial search methods simulated annealing and tabu search. The performance of the heuristics is examined using randomly generated, published and industry data. The results indicate that the simulated annealing based heuristic is the preferred technique in the context of the problem addressed in this paper. Further, we also demonstrate that the simulated annealing based heuristic generates near-optimal solutions to the cell formation model formulated in this paper.

Efficient solution for machine cell formation in group technology

Abstract The design of cellular manufacturing (CM) systems has received considerable attention from both practitioners and academics in the study of automated manufacturing systems. CM, an application of group technology (GT), focuses on grouping parts with similar design and/or process characteristics into part families and forming associated machines into machine cells in order to achieve higher manufacturing efficiency. In this paper, we propose a quadratic programming model for machine cell formation with congestion constraint, which in addition to assigning the machines to the cells, finds the optimal number of machine cells. An efficient algorithm based on graph partitioning is used for solving the problem. Extensive computational tests are conducted to test the performance of the proposed algorithm with respect to several performance measures under various problem parameters.

Fractional cell formation in group technology

Group Technology (GT) aims at improving productivity in batch manufacturing. Here components are divided into families and machines into cells such that every component in a part family visits maximum number of machines in the assigned cell with an objective of minimizing inter-cell movement. In situations where too many inter-cell moves exist, fractional cell formation using remainder cells can be used. Here, machines are grouped into GT cells and a remainder cell, which functions like a job shop. Component families are formed such that the components visit the assigned cell and the remainder cell and do not visit other cells. The fractional cell formation problem to minimise inter-cell moves is formulated as a linear integer programming problem. Here, movement between machine cells and remainder cells is not counted as inter-cell moves but movement of components among GT cells is considered as inter-cell movement. The fractional cell formation problem is solved using Simulated Annealing. A heuristic alg...

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

A clustering algorithm for machine cell formation in group technology using minimum spanning trees

Abstract I address the machine cell part family formation problem in group technology. The minimum spanning tree (MST) For machines is constructed from which seeds to cluster components are generated. Seeds to cluster machines are obtained from component clusters. The process of alternate seed generation and clustering is continued until feasible solutions are obtained. Edges are removed from the MST to identify alternate starting seeds for clustering. The algorithm is tested with matrices available in the literature. The results compare favourably with existing methods.

Machine-component cell formation in group technology: a neural network approach

SUMMARY This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network is selected due to the fact that this clustering method utilizes binary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offers the capability of handling large, industry-size data sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained were found to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experiments on larger data sets, involving 10000 parts by 100 machine types, revealed that the method results in the identification of clusters with fast execution times. If a block diagonal structure existed in the input data, it was identified to a good degree of perfection. It was also found to be efficient with some imperfections i...

Role of similarity measures in PCB grouping procedures

The problem of grouping printed circuit boards (PCBs) for simultaneous assembly on electronic assembly machines is an extension of the set-covering problem. A general approach based on cluster analysis and measure of similarity between PCBs is suggested as a basis of PCB grouping. The approach is independent of the specific similarity measure used. We show that, due to the analogy between this problem and the problem of machine cell formation in group technology, similarity measures developed for the second problem are useful as building blocks in a general framework that solves the PCB grouping problem.

A graph partitioning procedure for machine assignment and cell formation in group technology†

SUMMARY This paper proposes a mathematical model and solution procedure for the group technology configuration problem—the grouping of individual machines into cells and the routing of components to machines within cells. Costs of inventory, machine depreciation, machine setup and material handling are first incorporated into a mathematical programming formulation. The formulation is then divided into two subproblems to facilitate solution. A heuristic graph partitioning procedure is then proposed for each subproblem. The first subproblem assigns components to specific machines. The second subproblem groups machines into cells. An approach for determining economic batch sizes in this environment is also included. Examples are included and the flexibility of the approach to various environments is discussed.

Cell formation in group technology: A new approach

In the U.S.A., machine-component cluster formation is considered a poor alternative to classification and coding as a planning tool for Cellular Manufacture. This paper introduces a heuristic procedure, the Occupancy Value method, for identifying clusters in a machine-component matrix created from route card data. A unique feature of this method is that it progressively develops block diagonalization starting from the northwest corner of the matrix. The flexibility of the procedure is illustrated in detail through a small example. Another large matrix is analyzed to demonstrate the inherent simplicity of the method. Further extensions of this method to implement the manufacturing cells from these initial clusters are discussed.