A reconfigurable manufacturing system (RMS) that is designed specifically to adapt to changes in production capacity, through system reconfiguration, is called a scalable-RMS. The set of system configurations that a scalable-RMS assumes as it changes over time is called its configuration path. This paper investigates how to determine the optimal configuration path of a scalable-RMS that minimizes investment and reconfiguration costs over a finite horizon with known demand. First, a practical cost model is presented to compute the reconfiguration cost between two scalable-RMS configurations. This model comprehends labor costs, lost capacity costs, and investment/salvage costs due to system reconfiguration and ramp up. Second, the paper presents an optimal solution model for the multiperiod scalable-RMS using dynamic programming (DP). Third, a combined integer programming/dynamic programming (IP-DP) heuristic is presented that allows the user to control the number of system configurations considered by the DP in order to reduce the solution time while still providing a reasonable solution. Numerical problems involving a two-stage and a three-stage scalable-RMS are solved using the DP and IP-DP methodologies. Experimental results suggest that the DP approach, although it is optimal, is not computationally efficient for large problem sizes. However, the combined IP-DP approach offers reasonable results with much less computational effort.