Optimal design and scheduling of cellular manufacturing systems: An experimental study

Abstract

Designing and controlling cellular manufacturing systems (CMS) constitute tactical and operational decisions that include cell formation (CF), group layout (GL), and group scheduling (GS). Taking these three decisions simultaneously into consideration when modeling the problem, can improve both the design and operational performance of the system. In a previous paper, a mixed integer linear programming (MILP) model was proposed for the combined CF, GL and GS problem. The model can be solved to optimality for small and fairly medium-sized problems. Because of the complexity of the problem, in this paper, a Genetic Algorithm (GA) is used to solve it, where the GA chromosome is designed to represent the three decisions, simultaneously. Crossover and mutation operators are utilized to explore different schedules for the same cell formations and layouts, and vice versa. The performance of the GA is compared to that of the MILP model in an experimental study by solving a number of randomly generated problems. The GA obtained the optimal solutions for small problems and better solutions than the best feasible solutions obtained by the MILP model for medium and large problems.