• Journal of Korean Institute of Industrial Engineers
• /
• v.30 no.3
• /
• pp.175-180
• /
• 2004

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells using grid computing. It forms machine cells from the machine-part incidence matrix by means of Self-Organizing Maps(SOM) whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the machine vectors. It generates machine-part group which are assigned to machine cells by means of the number of bottleneck machine with processing part. The proposed algorithm was tested on well-known machine-part grouping problems. The results of this computational study demonstrate the superiority of the proposed algorithm.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.4
• /
• pp.360-367
• /
• 2002

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells. The purpose of this study is to develop a two-phase machine-part group formation algorithm based on Self-Organizing Maps (SOM). In phase I, it forms machine cells from the machine-part incidence matrix by means of SOM whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the input vectors. In phase II, it generates part families which are assigned to machine cells by means of machine ratio related with processing part and it gives machine-part group formation. The proposed algorithm performs remarkably well in comparison with many well-known algorithms for the machine-part group formation problems.

• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.3
• /
• pp.213-221
• /
• 2003

In this paper, we consider a multi-objective part-machine grouping problem, in which part types have several alternative part routings and each part routing has a machining sequence. This problem is characterized as optimally determining part type sets and its corresponding machine cells such that the sum of inter-cell part movements and the sum of machine workload imbalances are simultaneously minimized. Due to the complexity of the problem, a two-stage heuristic algorithm is proposed, and experiments are shown to verify the effectiveness of the algorithm.

• Korean Management Science Review
• /
• v.24 no.1
• /
• pp.91-111
• /
• 2007

In this paper an effective two-phase approach adopting modified p-median mathematical model is proposed for grouping machines and parts in cellular manufacturing(CM). Unlike the conventional methods allowing machines and parts to be improperly assigned to cells and families, the proposed approach seeks to find the proper block diagonal solution where all the machines and parts are properly assigned to their most associated cells and families in term of the actual machine processing and part moves. Phase 1 uses the modified p-median formulation adopting new inter-machine similarity coefficient based on the non-binary production data-based part-machine incidence matrix(PMIM) that reflects both the operation sequences and production volumes for the parts to find machine cells. Phase 2 apollos iterative reassignment procedure to minimize inter-cell part moves and maximize within-cell machine utilization by reassigning improperly assigned machines and parts to their most associated cells and families. Computational experience with the data sets available on literature shows the proposed approach yields good-quality proper block diagonal solution.

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

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.3
• /
• pp.83-89
• /
• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.125-130
• /
• 1996

This study is concerned with a heuristic algorithm that can make effectively the machine-part grouping in early stage for designing cellular manufacturing systems. By enhancing the Close Neighbour Algorithm(CNA), the proposed algorithm is concerned with making the machine-part grouping that can maximize machine utilization and minimize part's intercell movement by reducing exceptional elements. The algorithm is tested against existing algorithms in solving several machine-part initial matrices extracted from references and obtained by using random number. Test results shows that a solution matrix of the algorithm has superior grouping efficiency to Close Neighbour Algorithm.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.4
• /
• pp.69-87
• /
• 2006

This paper proposes an effective approach for the part-machine grouping(PMG) based on the non-binary part-machine incidence matrix in which real manufacturing factors such as the operation sequences with multiple visits to the same machine and production volumes of parts are incorporated and each entry represents actual moves due to different operation sequences. The proposed approach adopts Fuzzy ART neural network to quickly create the Initial part families and their machine cells. A new performance measure to evaluate and compare the goodness of non-binary block diagonal solution is suggested. To enhance the poor solution due to category proliferation inherent to most artificial neural networks, a supplementary procedure reassigning parts and machines is added. To show effectiveness of the proposed approach to large-size PMG problems, a psuedo-replicated clustering procedure is designed. Experimental results with intermediate to large-size data sets show effectiveness of the proposed approach.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.05a
• /
• pp.354-358
• /
• 2006

This study is concerned with the part-machine grouping(PMG) based on the non-binary part-machine incidence matrix in which real manufacturing Factors such as the operation sequences with multiple visits to the same machine and production volumes of parts are incorporated and each entry represents actual moves due to different operation sequences. The proposed approach adopts Fuzzy ART neural network to quickly create the initial part families and their associated machine cells. To enhance the poor solution due to category proliferation inherent to most artificial neural networks, a supplementary procedure reassigning parts and machines is added. To show effectiveness of the proposed approach to large-size PMG problems, a psuedo-replicated clustering procedure is designed and implemented.

• Journal of Korean Society for Quality Management
• /
• v.21 no.2
• /
• pp.224-231
• /
• 1993

This paper propose a procedure to form the cell and part family using both of similarity coefficient method and mathematical programming in order to slove the exceptional part and bottleneck machine problems. In the procedure proposed by this paper, the machine duplication and subcontracting are considered to slove the bottleneck machine and intercellular moving part problems An example is given to show how this procedure works.