A Stochastic Programming Approach for Configuration Optimization of Reconfigurable Manufacturing Systems

As a new manufacturing paradigm, Reconfigurable Manufacturing System (RMS) has a modular structure, which can greatly improve the responsiveness and flexibility by adding or removing the modules of the reconfigurable machine tools (RMTs). However, the uncertain demand makes it difficult and challenging to configure the RMS. To deal with this problem, we propose a multi-period stochastic programming model to optimize the configuration of RMS in each period by considering the uncertain demand. The objective is to maximize total costs. Production planning related decisions are considered to better handle the unpredictable demand fluctuation. Numerical experiments compare the solutions by solving the proposed stochastic programming model and those by solving the deterministic linear model without considering production planning decisions. The results show that our model can provide a better solution in terms of the configuration and reconfiguration cost and total costs under the consideration of uncertain demand.