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.

This paper proposes the heuristic approach for the generalized GT(Group Technology) problem to consider the restrictions which are given the number of cell, maximum number of machines and minimum number of machines. This approach is classified into two st

This paper proposes the heuristic algorithm for the generalized GT problem to consider the restrictions which are given the number of cell, maximum number of machines and minimum number of machines. This algorithm is classified into two stages. First stage is the course to form machine cells. we use the similarity coefficient which proposed and calculate the similarity values about each pair of all machines and align these values descending order. If any machine which is composed of selected similarity coefficient is possible to link the other machine on the edge of machine cell and have regard to restrictions and different kind relation among machines in the machine cell, then we assign the machine to the machine cell. Next stage is the course to form part families using proposed grouping efficacy. This stage is also completed when every part is assigned to the machine cell. The results of using the proposed algorithm are compared to the Modified p-median model. The computational results show that the proposed algorithm provides a powerful means of solving the machine-part grouping problem.