Allocation policies of auxiliary modules to support reconfigurable manufacturing system: an assessment by simulation models

Abstract This study proposes and evaluates two novel workload-based allocation strategies for auxiliary modules in reconfigurable manufacturing systems (RMS). To enhance the responsiveness of RMS in dynamic Industry 4.0 environments, these strategies use direct and indirect workload metrics to optimize the placement of modules that reduce processing time or add tasks. The effectiveness of the proposed policies is assessed through extensive simulations under varying conditions, including workload imbalance, process time reductions, and machine failures. Results demonstrate that the workload-aware allocation policies lead to significant improvements in throughput, delay reduction, work-in-process, and system utilization. The findings highlight the effectiveness of adaptive, data-driven module allocation in enhancing system efficiency and robustness, thus contributing to advancing decision-making frameworks for reconfigurable manufacturing systems.