ABSTRACT
The present study is focused on the development of new model based on the natural constraints, which is inspired by the human cognition that could reason the differences in patterns and recognize them to solve problems. Partial usage of network topology has been applied to identify cells and their index, with over five differential sub-methods devised to work adjacent to the main module, which interlinked with each other to find the patterns. Coupling with other models has shown an advantage of lowering the actual time of solving the cell formation problems taken from standard journals and some random generated problems with IBC algorithm, which performs better to form cells, machines and part families which results with less number of exceptional elements and exceptional components and greater accuracy. We will be using the IBC algorithm software to fast up the computations of a cellular problem in less time. It has been observed that when this model is applied to large-sized machine component matrix, the time and solution search have lowered. On comparing with the benchmark problem sets, the solution time for searching better solution has greatly decreased while retaining the same optimal solution.
