## 1 Introduction  
In the realm of manufacturing and production, the scheduling of tasks across identical parallel machines constitutes a fundamental aspect of operational efficiency and resource utilization. The dynamic nature of production environments, characterized by uncertainties, disruptions, and varying priorities, necessitates agile scheduling strategies capable of adapting to evolving conditions in real time. [PERSON] [1] emphasizes the importance of such adaptive strategies, noting that effective scheduling must account for both the inherent uncertainties and the need for timely adjustments to maintain system efficiency. In this context, predictive-reactive rescheduling emerges as a crucial paradigm, offering the promise of enhanced adaptability and responsiveness to changing production dynamics. Driven by the complexities of space sector production and dynamic scheduling challenges, this paper introduces a comprehensive framework for predictive-reactive rescheduling in identical parallel-machine environments. Our methodology is grounded in real-world data and scenarios, reflecting the intricacies of production dynamics and the influence of managerial decisions. The data presented in this paper have been adapted from real-world scenarios to ensure confidentiality and protect company information. While the specific data cannot be shared for security reasons, they have been altered in a manner that preserves their relevance to practical industry settings, and the methodologies used ensure the applicability of the findings. All calculations and results are based on these adapted data sets, maintaining the integrity of the analysis. In contrast to traditional optimization-based approaches, our methodology adopts a heuristic-driven strategy to overcome the computational complexities associated with NP-hard problems, ensuring timely and efficient solution generation. A distinguishing feature of our approach lies in the integration of 13 distinct dispatching rules to cater to diverse production objectives and constraints. Our approach integrates 13 dispatching rules to address various production objectives and constraints. This broad selection of rules offers flexibility in scheduling solutions, allowing decision-makers to improve production outcomes while reducing computational overhead. Crucially, our approach emphasizes periodic rescheduling over event-driven approaches, enabling proactive adaptation to changing production conditions and minimizing the impact of disruptions on schedule performance. Through complete rescheduling, we ensure comprehensive adjustments across schedules, mitigating the risk of cascading disruptions and optimizing resource utilization in dynamic manufacturing environments. Central to our methodology is the consideration of stochastic processing time deviations and job rework occurrences, acknowledging the inherent uncertainties inherent in real-world production processes. By incorporating these factors into our rescheduling framework, we enhance the adaptability and robustness of our scheduling solutions, enabling an effective response to dynamic production conditions and uncertainties. Drawing from the rich body of literature on predictive-reactive rescheduling, identical parallel-machine scheduling, and dispatching rules, our study contributes empirical insights and practical methodologies to inform scheduling practices in dynamic manufacturing environments. By synthesizing theoretical foundations with practical considerations, we aim to bridge the gap between research and industry practice, facilitating the adoption of predictive-reactive rescheduling strategies to enhance operational efficiency and competitiveness in modern manufacturing settings. Instead of commonly utilized objective functions such as makespan or cost minimization [2; 3; 4], this study adopts total weighted tardiness minimization and tardy job number minimization as its primary goals.