• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.705-718
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• 1996

A similarity coefficient based algorithm is proposed to solve the machine cells and part families formation problem in group technology. Similarity coefficients are newly designed from the machine-part incidence matrix. Machine cells are formed using a recurrent neural network in which the similarity coefficients are used as connection weights between processing units. Then parts are assigned to complete the cell composition. The proposed algorithm is applied to 30 different kinds of problems appeared in the literature. The results are compared to those by the GRAFICS algorithm in terms of the grouping efficiency and efficacy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.253-261
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• 1996

In optimizing the layout design of a multi-product assembly environment, tile analysis of the material flow is a vital ingredient. In flexible assembly systems, assembly time is usually very short thus the transfer time is relatively more important Therefore operations sequence must be so determined, that have no backtracking operations as possible as, It is important to form cells, so that they have no intercell movement in curring much processing delay, and to arrange machines as possible as densly. This study presents a independent cell formation method considering operation sequences and machine capacity in flexible assembly systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.4-4
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• 1996

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.45-54
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• 1999

Cellular manufacturing(CM) is a manufacturing philosophy and strategy for improving both productivity and flexibility. Cell formation(CF), the first and key problem faced in designing an effective CM system, is a process whereby parts with similar design features or processing requirements are grouped into part families, and the corresponding machines into machine cells. In this paper, a sophisticated fuzzy mixed-integer programming model is proposed to simultaneously form manufacturing cells and minimize the total costs of dealing with exceptional elements. Also, we will proposed a new method to solve the cell formation problem in the fuzzy environment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.21-24
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• 2001

This study is concerned with the development of efficient p-median approach to nondegenerate cell formation(CF) in group technology(GT) manufacturing. Unlike most of existing CF methodologies allowing degenerate cells or families that contains no parts or machines, this study attempts to find cell configuration where each machine cell contains at least two or more machines processing at least two or more parts so as to fully utilize the similarity in designing and processing parts. Nondegenerate CF seeks to minimize both the exceptional elements outside the diagonal block and the voids within the diagonal block. To find nondegenerate cells, a two-phase p-median methodology is proposed. In phase 1, the classical p-median model is implemented to find initial cells. In phase 2, bottleneck machines and parts are reassigned until no further degenerate cells and families are found. Test results on moderately medium-sized CF problems show the substantial efficiency of the proposed approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.255-264
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• 1996

This Paper presents a heuristic algorithm for machine-part group formation considering part production information (Production volume, roution sequence, unit operation time, facility size) in cellular manufacturing logistics system. In general, factory space is restricted within limited size when cells are located. A twofold heuristic algorithm is developed for considering factory space restrictions of located cells. The first phase is a aggregation procedure to minimize inter cell movement for satisfactoring space restriction. The second phase is a rearrangement procedure to maximize line balancing efficiency between machines within the cell and non assigned machine during first phase. Numerical example is presented to verify the efficiency of proposed algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.41-54
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• 2005

In this paper, we consider a multi-objective machine cell formation problem. This problem Is characterized as determining part route families and machine cells such that total sum of inter-ceil part movements and maximum machine workload imbalance are simultaneously minimized. Together with the objective function, alternative part routes and the machine sequences of part routes are considered In grouping Part route families. Due to the complexity of the problem, a two-phase heuristic algorithm is proposed. And we developed an n-stage look-ahead heuristic algorithm that generalizes the roll-out algorithm. Computational experiments were conducted to verify the performance of the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.589-594
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• 1991

Group Technology (GT) is a technique for identifying and bringing together related or similar components in a production process in order to take advantage of their similarities by making use of, for example, the inherent economies of flow production methods. The process of identification, from large variety and total of components, of the part families requiring similar manufacturing operations and forming the associated groups of machines is referred as 'machine-component grouping'. First part of this paper is devoted to describing a hierarchical divisive algorithm based on graph theory to find the natural part families. The objective is to form components into part families such that the degree of inter-relations is high among components within the same part family and low between components of different part families. Second part of this paper focuses on establishing cell design procedures. The aim is to create cells in which the most expensive and important machines-called key machine - have a reasonably high utilization and the machines should be allocated to minimize the intercell movement of machine loads. To fulfil the above objectives, 0-1 integer programming model is developed and the solution procedures are found. Next an attempt is made to test the feasibility of the proposed method. Several different problems appearing in the literature are chosen and the results air briefly showed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.94-100
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• 2005

This paper presents the application of integrated mathematical programming approach for the design of cellular manufacturing. The proposed approach is carried out in two phases The first phase concerning exceptional elements(EEs) in cell formation and the second phase facilities layout design. This paper considers the total costs of three important costs for (1) intercellular transfer (2) machine duplication and (3) subcontracting. One of Important issue is the calculation of the number of machines considering the maximum utilization of machines and the available capacity of a machines that can be transferred between cells. Facilities layout design is considered to reflect the real field data taking in to account the operational sequence of the parts to be manufactured. The model is formulated as mixed integer programming that is employed to find the optimal solution.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.285-293
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• 2004

In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.