## 2 Literature Review
### Repetitive and Cyclic Production  
The repetitive production of multiple product types on one or more machines is a critical component of modern manufacturing systems. A central challenge in such environments is determining the optimal number of machines and workers while scheduling production sequences to minimize costs and maximize efficiency. Ref. [39] survey the computational complexity of cyclic scheduling problems, highlighting their NP-hard nature, especially in robotic cell scheduling. They emphasize the challenges in finding optimal solutions and the need for heuristic approaches. These insights support our study, which tackles cyclic scheduling by optimizing resource allocation and minimizing the number of machines and operators. Ref. [40] focus on cyclic scheduling in discrete, repetitive manufacturing systems, blocking tasks without intermediate buffers between machines. They propose a mixed-integer programming model incorporating sequence-dependent setups and show how blocking conditions significantly impact optimal schedules. Their model effectively addresses small to medium-sized problems, highlighting key real-world scheduling constraints. The complexity of scheduling repetitive production increases when a finite number of workers operate the machines, requiring efficient coordination between machine usage and labor allocation. Several studies have developed models that integrate these aspects to improve production planning. For instance, ref. [41] proposed a mixed-integer linear programming (MILP) model to optimize scheduling in multi-product, multi-machine environments while accounting for labor constraints. Their approach significantly improved production efficiency and resource utilization. Similarly, ref. [42] introduced a hybrid scheduling method that combines genetic algorithms with simulation techniques to manage the complexity of multi-product, multi-machine manufacturing systems. Refs. [43; 44] extended this approach by developing a multi-objective scheduling method for distributed and flexible job shop scheduling problems, further enhancing efficiency under dynamic production conditions. Ref. [42] proposed a multi-objective particle swarm optimization approach for solving mixed no-idle, flow-shop scheduling problems, offering improved performance in complex manufacturing settings. These studies collectively emphasize the significance of heuristic algorithms, hybrid methods, and advanced scheduling models in optimizing production processes, particularly in environments with multiple machines, labor constraints, and dynamic demand patterns. The role of labor in ELSP-based scheduling has been widely studied. Ref. [38] extended the ELSP framework to account for labor constraints, demonstrating that optimal scheduling depends not only on lot sizes but also on worker allocation and workload distribution. Ref. [37] further integrated job scheduling with machine allocation to improve labor efficiency while maintaining stable cyclical production. Research on cyclical manufacturing has underscored the need to synchronize production scheduling with workforce availability. Ref. [45] emphasized the importance of aligning production sequences with worker shift patterns, fatigue levels, and machine configuration requirements. Advanced methodologies, including mixed-integer programming and heuristic algorithms, continue to refine scheduling models by incorporating both machine and labor constraints, further optimizing production efficiency. Recent literature continues to broaden the understanding of production management by incorporating modern priorities such as sustainability, digital integration, and smart planning. Ref. [46] offer a systematic literature review on advanced manufacturing management, emphasizing the integration of innovative technologies to improve production efficiency and adaptability. Ref. [47] focuses on the integration of production planning and scheduling under Industry 4.0, presenting how machine learning and analytics can enhance real-time responsiveness and operational control.