A genetic algorithm for solving a dynamic cellular manufacturing system

Abstract

This paper proposes a genetic algorithm (GA) to solve an integrated mathematical model for dynamic cellular manufacturing system (DCMS) and production planning (PP) concurrently. The model simultaneously seeks to determine the variables associated with the production planning and the cell construction and formation. The total costs include the cost of machine procurement, the cell reconfiguration cost, the cell setup cost, the unexpected variable costs of cells alongside the production planning costs. At first the mathematical model, which is an integer nonlinear programming (INLP), is converted to a linear programming (LP) model. Then, the branch and bound (B&B) method is used for solving small size problems employing the Lingo 8 software. Finally because the problem is NP- hard, a GA is used to solve the large-scale problems as a meta-heuristic algorithm. To evaluate the results obtained by the genetic algorithm, they are compared with those obtained with the Lingo 8 software. Computational results confirm that the genetic algorithm is able to produce good solutions.