• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.236-243
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• 1994

In this paper we consider the part-machine cell formation decision of the generalized Group Technology(GT) problem in which multiple process routes can be generated for each part. The existing p-median model and similarity coefficient algorithm can solve only small-sized or well-structured cases. We suggest an assignment method for the cell formation problem. This method uses an assignment model which is a simple linear programming. Numerical examples show that our assignment method provides good separable cells formation even for large-sized and ill-structured problems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.288-297
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• 1994

The sewing machine is one of the oldest machine that has ever used, which is related with clothes' life. Modern sewing machines are divided into three groups by the sititch character, which are the lock stitch sewing machine group, the over lock sewing machine group and the specical sewing machine group. The lock stitch sewing machine have being used more than any others, which is also good model to study. This work is part of the improvement of an industrial lock stitch(ILS) sewing machine's design. The research objectives are the kinematic analysis and evaluations of stitch performance. The feed dog and the needle extreme's motion, which are important two part's motion in the sewing machine, are characterized by the stitch process and the needle trace. The needle trace is formulated as the stitch spacing, the stitch spacing's ratio(the static characteristic), and the stitch's phase difference(the dynamic characteristic). The tested ILS sewing machine is evaluated as a good static characteristic and a bad dynamic characteristic. Namely, a stitch spacing's ratio is 0.01~0.063(mm/mm) and a stitch's phase difference ratio is 0.06~0.13(mm/mm).

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.20-26
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• 2005

This paper proposes the heuristic algorithm for the generalized GT problems to consider the restrictions which are given the number of machine cell and maximum number of machines in machine cell as well as minimum number of machines in machine cell. This approach is split into two phase. In the first phase, we use the similarity coefficient which proposes and calculates the similarity values about each pair of all machines and sort these values descending order. If we have a machine pair which has the largest similarity coefficient and adheres strictly to the constraint about birds of a different feather (BODF) in a machine cell, then we assign the machine to the machine cell. In the second phase, we assign parts into machine cell with the smallest number of exceptional elements. The results give a machine-part grouping. The proposed algorithm is compared to the Modified p-median model for machine-part grouping.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.656-658
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• 2006

This study deals with the part-machine grouping (PMG) that considers realistic manufacturing factors, such as the machine duplication, operation sequences with multiple visits to the same machine, and production volumes of parts. Basically, this study is an extension of Won(2006) that has adopted fuzzy ART neural network to group parts and machines. The proposed fuzzy ART neural network algorithm is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Computational experiments applied to large-size PMG data sets with a psuedo-replicated clustering procedure show effectiveness of the proposed approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.91-97
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• 1995

This paper presents a possible application of artificial neural network in CM system design. CM systems can be designed based on product lines, part characteristics or part routines. GT(Group Technology) which uses part characteristics to design cells is widely applied, however, the identification of the part-machine families is the fundamental problem in the design process. A heuristic procedure using SOFM which requires only part-machine incidence matrix is proposed in this research. Comparison studies on ZODIAC and ROC with SOFM model are done and the results are discussed and summarized in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1576-1579
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• 1991

This paper discusses on the machine loading and tool allocation (MLTA) problem. Mathematical formulation of the problem is given first. Then a heuristic approach based on Group Technology (GT) is presented to deal with the MLTA problem effectively. By using this approach, part-tool group generation and their assignment to adequate machines can easily be obtained in consideration of the work load on each machine, the number of tool-set replacement, and the total number of cutting tools required through the interactive setting of the desired machine utilization rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.84-89
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• 2004

The machine cell formation problem is the problem to group machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts, and inter-cell part movements in cellular manufacturing systems and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net which is a kind of neural network model. To show the applicability of the proposed method, it presents some experiment results and compares the method with other cell formation methods. From the experiments, we observed that the proposed method could produce good cells for the machine cell formation problem.

• Korean Management Science Review
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• v.4
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• pp.76-83
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• 1987

The machine-part group formation(MPGF) problem arises frequently in FMS planning. By viewing the problem as one of finding good assignments, a powerful solution algorithm is presented. The new algorithm solves the threshold dilemma found in previous solution procedures employing similarity coefficients. It also compared favorably with other existing MPGF algorithms by finding minimum exceptional elements for the tested problems. Furthermore the new algorithm can solve dynamic and more realistic MPGF problems by considering production volumes or costs. Such diverse machine-part relationship values were not considered in previous MPGF studies, which included only 0,1 incidence values. An example problem is solved where production volumes are the elements of MPGF incidence matrix.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.113-125
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• 2008

This paper presents a model for designing cellular manufacturing systems (CMS) by integrating system cost, machine reliability, and preventive maintenance (PM) planning. In a CMS, a part is processed using alternative process routes, each consisting of a sequence of visits to machines. Thus, a level of 'system reliability' is associated with the machines along the process route assigned to a part type. Assuming machine reliabilities to follow the Weibull distribution, the model assigns the machines to cells, and selects, for each part type, a process route which maximizes the overall system reliability and minimizes the total costs of manufacturing operations, machine underutilization, and inter-cell material handling. The model also incorporates a reliability based PM plan and an algorithm to implement the plan. The algorithm determines effective PM intervals for the CMS machines based on a group maintenance policy and thus minimizes the maintenance costs subject to acceptable machine reliability thresholds. The model is a large mixed integer linear program, and is solved using LINGO. The results point out that integrating PM in the CMS design improves the overall system reliability markedly, and reduces the total costs significantly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.137-146
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• 2000

One of the problems faced in implementing cellular manufacturing systems is machine-part group formation. This paper proposes machine-part grouping algorithms based on Self-Organizing Map(SOM) neural networks and K-Means algorithm in cellular manufacturing systems. Although the SOM spreads out input vectors to output vectors in the order of similarity, it does not always find the optimal solution. We rearrange the input vectors using SOM and determine the number of groups. In order to find the number of groups and grouping efficacy, we iterate K-Means algorithm changing k until we cannot obtain better solution. The results of using the proposed approach are compared to the best solutions reported in literature. The computational results show that the proposed approach provides a powerful means of solving the machine-part grouping problem. The proposed algorithm Is applied by simple calculation, so it can be for designer to change production constraints.