# The Impact of Downtime on the Stability of the Production Schedule
## 1 Introduction
In addition, \"interactive\" and \"Pareto pruning\" methods, as well as the consideration of human factors in flexible manufacturing systems, remain under-researched. Despite the fact that these methods and strategies can increase productivity or minimize delays, there are still challenges in integrating them with existing systems, particularly with regard to computational complexity and real-time applications in different production environments. Consequently, classic priority rules for rescheduling are still used in many companies. The articles [30; 31] present simple and commonly used priority rules such as: FIFO (first in, first out), EDD (earliest delivery date), STR (remaining time stock), STR/OP (remaining time stock per operation) and SQNO (shortest queue in next operation). A particularly effective rule for minimizing delays in dynamic environments is the weighted critical factor and shortest processing time (W(CR+SPT)) rule described in [32]. The authors also attempt to combine priority rules, also using computer applications. In article [33], the authors attempt to create a new priority rule (TNPR, The New Priority Rule) that adaptively combines existing rules (SPT: shortest processing time, MIS: most immediate successors, GRWC: greatest resource work content, LNRJ: least non-related jobs) to improve project scheduling. It uses machine learning to select the best-performing strategies for activity lists, increasing the efficiency of rescheduling. In the paper [2], a reinforcement learning approach was proposed to adaptively select simple priority rules of dynamic environments, improving the rescheduling performance under different disturbances compared to traditional heuristic algorithms. The paper [34] proposes a new approach to solve the multi-criteria scheduling problem, in which simple priority rules are used dynamically according to the varying state of the scheduling environment. The rules assign priorities to queued tasks based on their characteristics and/or the scheduling environment. Based on this approach, a new algorithm was designed that uses different rules at the scheduling time. While these simple priority rules provide structured approaches to rescheduling, their effectiveness can vary significantly depending on the specific project context and environmental conditions. This variability suggests that a 'one-rule-fits-all' solution may not be feasible, and further research into context-specific applications is warranted. The above modern methods of production rescheduling have many advantages and disadvantages. The main advantages are their high flexibility (e.g., genetic algorithms or fuzzy logic), real-time adaptation (e.g., reinforcement learning) or the ability to model complex problems (e.g., MILP). The main drawbacks, however, are time and computational complexity (e.g., GA and MILP) or high modeling complexity requiring specific skills (e.g., CP or reinforcement learning), as well as high application costs. Classical methods, unlike modern ones, are characterized by their simplicity of understanding and implementation in production settings. They also do not require the use of sophisticated information systems, which increases their versatility. Despite their relatively low flexibility, they are still used in many manufacturing companies. Accordingly, the authors recognize the need to study these rules in the context of ensuring stability of production scheduling because, as the literature analysis shows, stable production planning and scheduling is essential to ensure continuous production operations [35; 36] and customer order fulfillment [37; 38; 39]. This paper presents research on the relationship between the influence of various factors affecting the stability of the production schedule. Section 2, \"Materials and Methods\", describes the scope and purpose of the research, presents two production flow models along with input data for simulations and presents the research plan along with the obtained research results.