The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.

Neural network is a model of brains’s cognitive process. Neural network originated as a model of how the brain works. Neural network research has its beginnings in psychology. Today neural network methods are being used to solve numerous problems associated with manufacturing operations. A review of neural network applications to problems in production and operations management is presented. Applications reviewed in this paper include character, image andpattern recognition, managerial decision making, manufacturing cell design, tool condition monitoring, real-time robot scheduling and statistical process control. Methods and structures of neural network are explained.