In today’s dynamic business environment, the product mix and demand may vary from period to period. Therefore, the same configuration of cellular systems will not be optimal in different periods. In this paper, a new mixed integer non-linear mathematical programming model is presented to design dynamic cellular manufacturing systems and combines several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. The required capacity for parts production is modeled based on flow shop perspective and the aim of mixed integer model is to find optimal independent cells, the quantity of machine types in each cell, and production quantity of the parts during each period of the time horizon. Since this problem belongs to NPhard class, a three-phase approach is developed to solve the model for practical purposes. Phase 1 finds a feasible solution, phase 2 finds the neighbor solutions, and phase 3 improves a feasible solution. To analyze the computational efficiency, eight test problems with different sizes are considered and the optimal and near-optimal solutions are compared. The efficiency of the algorithm in terms of the objective function values and computational times is shown by the obtained results.
