## 2 Literature Review  
The literature review conducted by [PERSON] et al. (2018) [PERSON] et al. (2018) identified five key areas for future research in reconfigurable manufacturing systems (RMSs): reconfigurability level assessment, analysis of RMS features, RMS performance analysis, applied research and field applications, and reconfigurability towards Industry 4.0. They highlighted the importance of exploring the best practices to efficiently guide modern industrial companies toward adopting reconfigurable manufacturing. However, this comprehensive review does not include consideration of multi-spindle machines in RMSs. On the other hand, the authors of [PERSON] et al. (2018) offered a review of optimization problems related to RMSs, categorizing studies into four areas: RMS design, production planning and scheduling, layout design, and line balancing and rebalancing problems. Their review provides valuable insights into various aspects of RMS optimization, shedding light on key challenges and potential solutions in the field. In the work proposed in [PERSON] et al. (2018), a sophisticated decision-making approach grounded in game theory, particularly the Gale-Shapley model, was proposed to streamline machine reconfigurations effectively. Through comprehensive simulations, the authors of [PERSON] et al. (2018) showcased enhanced performance metrics achieved through controlled reconfigurations. Furthermore, the authors of [PERSON] et al. (2018) introduced the concept of focused flexible manufacturing systems (FFMSs), which integrate both general-purpose and dedicated resources to optimize system efficiency. Leveraging genetic algorithms, hybrid manufacturing systems are meticulously designed, taking into account intricate factors such as customer demand, operational requirements, and market uncertainties, which are meticulously assessed through Monte Carlo simulations. These pioneering methodologies mark significant advancements in the field, promising enhanced adaptability and performance in modern manufacturing environments. In their groundbreaking study, the authors of [PERSON] et al. (2018) introduced a comprehensive multi-objective approach aimed at optimizing the design of reconfigurable manufacturing systems (RMSs). Their objectives encompass maximizing system modularity while concurrently minimizing the completion time and cost. The proposed methodology leverages a modularity-based technique employing adapted multi-objective simulated annealing (AMOSA), complemented by a sophisticated decision-making tool based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Through an illustrative example and rigorous numerical analyses, the authors of [PERSON] et al. (2018) illustrated the practical applicability and effectiveness of their approach, underscoring its potential to revolutionize RMS design and performance optimization. In their comprehensive study, the authors of [PERSON] et al. (2018) delved into the intricate realm of quality considerations within process planning for reconfigurable manufacturing systems (RMSs). Their analysis meticulously examined the impact of quality variations on cost-based design and modular features, elucidating the distinction between conforming and failed products. To address these challenges, the authors of [PERSON] et al. (2018) proposed a novel multi-objective, mixed-integer nonlinear programming model, which they applied to an industrial case study using both exact and hybrid meta-heuristic solutions. By providing valuable insights into the interplay between quality and process planning, their findings offer invaluable guidance for decision makers navigating RMS implementation. On a related front, the authors of [13] tackled the formidable challenges of production scheduling in RMSs constrained by limited auxiliary modules (AMs). Their pioneering work introduced a sequence-based mixed-integer linear programming (MILP) model designed to minimize the total weighted tardiness (TWT).