Optimization of cell configuration and comparisons using evolutionary computation approaches

This paper examines a cellular manufacturing optimization problem in a new facility of a pharmaceutical company. The new facility, together with the old one, should be adequate to handle current and future production requirements. The aim of this paper is to investigate the potential use of evolutionary computation in order to find the optimum configuration of the cells in the facility. The objective is to maximize the total number of batches processed per year in the facility. In addition, a two-objective optimization search was implemented, using several evolutionary computation methods. One additional objective is to minimize the overall cost, which is proportional to the number of cells in the facility. The multi-objective optimization programs were based on three approaches: The weighted-sum approach, the Pareto-optimality approach, and the Multiobjective Genetic Algorithm (MOGA) approach.