## 2 Literature Review
Additionally, they presented an improved genetic algorithm (IGA) tailored to addressing these scheduling challenges. Through extensive experimental validation, including real-world case studies, the authors of [13] demonstrated the remarkable effectiveness of the IGA across diverse scenarios, highlighting its potential to optimize production scheduling in RMSs with limited AMs. In their pioneering work, the authors of [5] introduced a systematic approach to configuring reconfigurable machine tools (RMTs) through modular design principles. Their methodology entails the generation of geometry and basic structure modules which are then integrated into conceptual autonomous functional modules, ensuring adaptability to diverse manufacturing requirements. By optimizing processing plans tailored to specific part families, the authors of [5] demonstrated the efficacy of their approach in deriving RMT configurations that effectively meet process requirements. Furthermore, this study explored the feasibility and implications of incorporating multi-spindle machines into the reconfigurable manufacturing landscape. Through a comprehensive analysis, the authors of [5] illustrated the practical effectiveness of their methodology using a gearbox part family production as a case study, underscoring its potential to enhance manufacturing flexibility and efficiency in real-world applications. On a related note, the authors of [14] addressed the formidable challenge of scheduling in reconfigurable manufacturing systems (RMSs) through a formalized approach utilizing integer linear programming. Their work introduced an iterative search method aimed at optimizing scheduling solutions, revealing promising improvements in solution quality. However, the study also highlights the inherent computational complexities associated with this approach, emphasizing the need for further investigation to address these challenges and unlock the full potential of scheduling optimization in RMSs. The authors of [15] introduced an innovative integrated approach that merges stochastic analysis with lot sequence optimization, aimed at maximizing the system service level within real industrial contexts. Their method represents a significant advancement in addressing complex scheduling challenges within manufacturing systems, offering practical insights into enhancing operational efficiency and performance. The authors of [16] proposed a comprehensive framework for configuration selection in manufacturing flow lines (MFLs) utilizing the non-dominated sorting genetic algorithm-II (NSGA-II). Their research emphasizes the importance of considering multiple objectives in optimizing reconfigurable manufacturing systems (RMSs), contributing valuable insights into improving system performance across various operational parameters. The authors of [17] introduced a novel nonlinear integer programming model designed to optimize both modular product and RMS configurations, making them tailored to individual customer needs. By employing a genetic algorithm approach and utilizing customizable office chairs as an illustrative example, their study underscores the critical role of integrated configuration decisions in achieving optimal process plans and minimizing associated costs. Furthermore, the authors of [18] presented an innovative nonlinear integer programming model focusing on optimizing modular product and RMS configurations to minimize the manufacturing costs for mass-customized products. Their approach, which integrates a modified brute force algorithm (MBFA) and genetic algorithm (GA), demonstrated efficient solution finding within reasonable computation time constraints, as validated through experimentation with modular smartphones. This research contributes valuable insights into the effective management of configuration decisions in reconfigurable manufacturing contexts, facilitating improved cost-effectiveness and adaptability in meeting diverse customer demands.