A genetic algorithm-based approach to cell composition and layout design problems

In this research, a genetic algorithm based solution approach is proposed to address the machine cell-part grouping problem. Three different objective functions considered are (1) minimize total moves (intercell as well as intracell moves), (2) minimize cell load variation, and (3) minimize both the above objective functions simultaneously. The total moves are determined as the weighted sum of both intercell and intracell moves. In the second objective function, cell load variation is minimized to aid the smooth flow of materials inside each cell and is obtained by computing the difference between the workload on the machine and the average load on the cell. The utilization of the workstations in a cell is evaluated and used in determining the best machine cell-part grouping. Furthermore, the sequence of operations and the impact of the layout of cells are also considered. We show that the results of the genetic algorithm based approach are comparatively better than the known results. The development and implementation of the genetic algorithm based solution approach is further supported by extensive statistical analysis of the results.