# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 1 Introduction
Communication systems are divided into levels ranging from sensors to cloud solutions, with the choice of technology depending on the requirements for reliability, speed, and the volume of data transmission. The efficient interconnection of production data and simulation models allows for accurate planning and a quick response to changes in production, increasing the productivity and flexibility of the production process [16]. Studies have shown that integrating tools like advanced planning and scheduling (APS) and Industrial Internet of Things (IIoT) into zero-defect manufacturing (ZDM) architectures can significantly enhance production planning's flexibility and efficiency, reducing waste and lead times [24]. Similarly, transforming supply chains using demand-driven material requirement planning (DDMRP) ensures the better integration of suppliers and customers, addressing traditional shortcomings and enabling a dynamic response to production changes [25]. In [26], the authors introduced a digital twin model for real-time production scheduling in mass production systems. In this study, the model is specifically designed for mass production and may not be readily applicable to other types of production systems. In [27], the authors proposed an adaptive production scheduling mechanism using reinforcement learning in a digital twin environment. This approach allows the system to learn and adapt to changes in the production environment. However, this approach requires a large amount of data and computing power to train machine learning models. In the field of digital twins, several studies have been conducted, with the present article primarily focusing on the following publications [28; 29; 30]. In [28], the authors presented a study focused on dynamic project planning using a digital twin, where they presented a decision support system for nonlinear aluminum production. The system integrates business data from MES and ERP to create a comprehensive digital twin. Implementing such a system can be challenging in terms of integrating different data sources and requires significant infrastructure investments. This research brings several key benefits that have not been sufficiently addressed in the existing literature so far. The first is a comprehensive approach to order prioritization. This research presents an innovative, comprehensive formula for the calculation of order priorities that includes a number of factors and allows the user to set the importance of each factor. This is a significant improvement over the traditional prioritization method. The second is real-time dynamic scheduling. While many existing systems rely on static scheduling, this research represents a system capable of dynamically adjusting schedules in real time based on up-to-date production data. The third is real-time RTLS data integration. Unlike previous studies, which often relied on  
Figure 1: Modular structure of the production scheduling system. simulated data or historical records, this work uses real-time data from a real-time location system (RTLS). This allows for more accurate and up-to-date decision-making in production planning. The aim of this study is to explore and design a digital twin-based custom manufacturing planning system that enables real-time dynamic scheduling based on data collected from an RTLS. The main contribution of this study is the presentation of an innovative approach to the dynamic scheduling of production processes through the integration of digital twin technology. The study highlights the use of real-time data collection to optimize production plans, enabling the business to respond more effectively to changes and unpredictable situations. Although this issue is being intensively researched, there are several gaps between the current state of knowledge and the needs of the industry. A significant part of the literature remains at the level of conceptual models or experiments, while the practical deployment of digital twins for planning is limited.