A cost-based model to select best capacity scaling policy for reconfigurable manufacturing systems

This study presents a system dynamics approach to model and analyse a single stage reconfigurable manufacturing system (RMS). The system is exposed to a random demand that follow a normal distribution. New modifications to the existing state of the art capacity scaling model are applied to bring it closer to reality. A module to account capacity scaling costs and a module for considering seasonal demand are introduced. The objective of this study is to evaluate the performance of different capacity scaling policies for various system scenarios. Experimentations are applied on three stages; preliminary experimentation, Taguchi fractional factorial design, and 24 full factorial design to conduct various system scenarios. Two policy selection rules are produced to help a practitioner in deciding the best scaling policy according to the existing system scenario. The results showed that chasing demand policy and inventory-based policy have the best performance for most system scenarios. [Received 13 March 2014; Revised 26 November 2014; Accepted 26 January 2015]