## 2 Literature Review
The authors of [19] introduced intelligent decision making for dynamic scheduling and reconfiguration using deep reinforcement learning (DRL) in a reconfigurable flow line (RFL) with dynamic job arrivals. They proposed a system architecture and mathematical model to minimize the total tardiness cost, integrating a DRL system with advantage actor critic (A2C). The results exhibited significant improvements over traditional meta-heuristics. The authors of [20] presented a constrained nonlinear optimization problem to determine an optimal layout of reconfigurable fixtures for specific workpieces. Considering the kinematic limitations of Stewart platforms and workpiece characteristics, the experimental results confirmed that the automatically computed layouts satisfied typical production constraints effectively. The authors of [21] introduced a digital twin-driven approach for rapid reconfiguration of automated manufacturing systems. Their method involves a semi-physical simulation integrated with an optimization component. Open-architecture machine tools (OAMTs) allow for quick module swapping, enhancing adaptability. The key techniques include twinning cyber-physical systems and bi-level programming for adaptive production. Physical implementation validates the improved system performance with minimized reconfiguration overheads. The authors of [22] proposed reconfiguration management as a holistic problem, outlining the potential goals achievable through this approach. The authors of [23] aimed to efficiently select between two types of reconfigurable machine tools (RMTs) for executing part process plans. A machine's configurational capability determines the RMT type selection, which is addressed through a genetic algorithm-based approach. An example demonstrates the method's validity for reconfigurable manufacturing system (RMS) design, with future research considerations. The authors of [24] endeavored to optimize the set-up and process planning simultaneously, aiming to minimize the overall costs including processing, tolerance, set-up change, and tool module expenses. They introduced a hybrid genetic algorithm-based approach comprising two stages: heuristic set-up generation followed by genetic algorithm-based process plan optimization. Numerical experiments validated the approach's applicability and economic advantages. Table 1 summarizes the main characteristics of the literature review we discussed. The main issues considered were the design of the manufacturing system (design) or the operational activities (as reconfigurations or scheduling). The main methods used to support the works were the simulation or heuristic approaches (e.g., game theory or genetic algorithms). Then, the introduction of multi-spindle machines was considered, and these cases were also studied for real industrial potential applications (case study). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Design** & \begin{tabular}{c} **Reconfiguration** \\ **or Scheduling** \\ \end{tabular}  & **Simulation** & **Heuristic** & \ \begin{tabular}{c} **Multi-** \\ **Spindle** \\ \end{tabular}  & \begin{tabular}{c} **Case** \\ **Study** \\ \end{tabular}  \\ \hline
[9] & & \(\times\) & \(\times\) & \(\times\) & & & \\ \hline
[10] & \(\times\) & & \(\times\) & & & \\ \hline
[11] & \(\times\) & & & \(\times\) & & \(\times\) \\ \hline
[12] & & \(\times\) & & \(\times\)  
From the above literature review, few works studied the introduction of multi-spindle machines in reconfigurable manufacturing systems. These works concern the design of the manufacturing system, but all works investigated the performance measures and the impact of multi-spindle machines on them, including the reconfiguration policy. The original contribution of this work is the evaluation of the impact of multi-spindle machines in reconfigurable manufacturing systems under a reconfiguration policy. The simulation model developed allows testing the different conditions in terms of interarrival, process time variation, and the introduction of failures.