Optimizing production scheduling in textile cutting workshops is crucial for improving efficiency and reducing lead times. Traditional scheduling methods frequently exhibit limited adaptability to fluctuating workloads, leading to suboptimal system performance. To address this challenge, this study integrates Fuzzy Logic (FL) with the Simulated Annealing (SA) algorithm to enhance scheduling adaptability. FL dynamically adjusts SA key parameters, including the initial temperature, the cooling rate, and the number of iterations per temperature level, based on three input variables: System Load (SL), Makespan Variability (MV), and Job Urgency (JU). A simulation is conducted in a textile cutting workshop where five cutting orders are processed through three stages: spreading, cutting, and labeling. Machines operate under resource constraints, and scheduling decisions aim to minimize makespan while adapting to workload fluctuations. The results demonstrate that the proposed approach effectively adjusts SA parameters in response to system conditions, leading to an improved scheduling performance. Comparative analysis with a conventional SA approach highlights the benefits of integrating FL, particularly in reducing makespan variability. These findings underline the potential of hybridizing metaheuristic algorithms with intelligent decision-making techniques in complex manufacturing environments. The proposed methodology provides a foundation for further research on adaptive scheduling solutions in textile and other production industries.