A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions

Abstract

In this research, a mixed-integer nonlinear programming model is formulated to design cellular manufacturing systems (CMSs) in multiple plants in a dynamic condition. The proposed mathematical model integrates significant manufacturing characteristics in designing CMSs along with the main strategies of production planning (PP) and location–allocation (LA) problems. In addition, there are some novel characteristics that make the designed model remarkable regarding the literature including: (1) multi-plant location; (2) allocation of plants capacity to fulfill multiple markets demand; and (3) the integration of dynamic cell formation, PP and LA decisions. The objective function terms are to optimize the sale revenue and total costs of machine operating, machine overhead, inter-cell material handling, inventory holding, outsourcing, machine installation/uninstallation, products transportation, dispersing machines, establishing plants, and forming cells. A sample test problem is solved by CPLEX solver to show the achievement obtained by the characteristics incorporated into the model. Since the proposed model is NP-hard, two meta-heuristics algorithms as grey wolf optimization and genetic algorithm are developed. To verify the computational effectiveness of the employed meta-heuristics in comparison with that of CPLEX solver, its performance is tested using a number of test problems.