# The Impact of Downtime on the Stability of the Production Schedule
## 1 Introduction
[PERSON] proposes a new genetic algorithm (NGA) to solve the flexible job-shop scheduling problem (FJSP) to minimize the makespan (the amount of time needed to process all jobs). On the other hand, the authors of the article [19] propose a hybrid algorithm combining a gene algorithm (GA) with simulated annealing (SA) and variable neighborhood search (VNS) to optimize the FJSP. In the context of the dynamic FJSP (DFJSP), events such as single-machine failures, new job arrivals, multiple-machine failures and multiple job arrivals were studied. The application of the proposed hybrid algorithm with a rescheduling strategy showed a significant improvement in the results, leading to a significant reduction in the total task completion time (makes-pan). Ref. [20] proposes a multi-population coevolutionary algorithm for the flexible production scheduling problem. The proposed algorithm uses ant colony optimization and a genetic algorithm with different configurations. On the other hand, ref. [21] considers that the use of fuzzy logic allows for the better handling of uncertainty. Fuzzy logic can facilitate dynamic rescheduling by providing a framework that adjusts schedules based on real-time data and changing circumstances, thus minimizing disruptions to the production process. The authors of [22] propose the use of mixed-integer programming (MILP) and constrained programming (CP) to model and solve a complex scheduling problem. The results of the study indicate that both approaches--MILP and CP--can be effectively applied to optimize scheduling in complex systems with multiple robots, each with its own advantages depending on the specifics of the problem and time requirements. In the article [23], the authors propose a novel scheduling mechanism based on a multi-agent system that enables the adaptation of production plans in the event of supply chain disruptions. This mechanism allows for the effective and rapid rescheduling of production in the face of various types of disruptions, such as delivery delays and product defects. The article [24] presents a dynamic scheduling strategy for shop floor production based on a digital twin and compares it with the results of rescheduling without a digital twin under fault disruption. The results show that the scheduling method based on the digital twin can effectively deal with disruptions and improve shop floor performance. In the paper [25], the rescheduling problem for a single machine with timed incoming tasks is considered. A mixed-integer linear programming (MILP) model for this problem and an iterative predictive-reactive strategy to deal with the online part are designed. The paper [26] proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The paper [27] discusses online scheduling methods. These focus on adjusting production schedules in real time as new orders are received, minimizing disruption to existing schedules. This approach uses constraint programming to balance the integration of new orders while maintaining efficiency in the current production flow. Knowledge-based systems, on the other hand, dynamically adjust schedules by changing only the necessary operations, thereby increasing overall production efficiency [28]. These systems use historical data and inference processes to effectively adapt to uncertainty. The [29] article notes that existing academic works have paid limited attention to multi-criteria optimization techniques applied in this context. To address this gap, the article presents a systematic review of the literature on multi-criteria production rescheduling. The results reveal a growing interest in this research area. Hybrid algorithms have shown better performance compared to standalone algorithms, taking advantage of combined strengths and mitigating individual weaknesses.