A Neural Network-Based Algorithm for Group Technology

In this paper, we propose a neural network-based algorithm for grouping machine and parts in cellular manufacturing. For grouping machines, we develop similarity coefficients which take into account both similarity and dissimilarity between machines. The machine cells are formed by an algorithm which is based on the maximum neural network. Another algorithm is used to find the part families associated with each machine cell. When compared with an existing algorithm, our algorithm shows better performance in terms of grouping efficiency and grouping efficacy.

An Estimation of Distribution Algorithm for Part Cell Formation Problem

The aim of this chapter is to propose a new heuristic for Machine Part Cell Formation problem. The Machine Part Cell Formation problem is the important step in the design of a Cellular Manufacturing system. The objective is to identify part families and machine groups and consequently to form manufacturing cells with respect to minimizing the number of exceptional elements and maximizing the grouping efficacy. The proposed algorithm is based on a hybrid algorithm that combines a Variable Neighborhood Search heuristic with the Estimation of Distribution Algorithm. Computational results are presented and show that this approach is competitive and even outperforms existing solution procedures proposed in the literature.