## 2 Materials and Methods
### Simulation  
Simulation is a way of recreating a real-world system using a model, which allows for experiments and analysis to be carried out. Such a model can reflect both the physical environment and the logical rules that govern how the system works. Simulation goes beyond simple imitation in that it also includes building the model, designing experiments, preparing input data, and analyzing the results [7]. This approach helps in testing systems, understanding their behavior, generating new ideas, and improving existing processes. Depending on how the system is modeled and how it changes over time, simulations can be grouped into different types [8]. These include the following:* Continuous simulation models systems with continuous state changes over time, often using differential equations. It is commonly used in dynamic system analysis and feedback modeling [9; 10; 11; 12]. * Agent-based simulation (ABS) models systems as collections of autonomous agents whose interactions lead to emergent behavior. ABS is suited for decentralized, complex systems [13; 14]. * Monte Carlo simulation relies on random sampling to analyze systems under uncertainty, supporting risk assessment in fields such as manufacturing and supply chains [8; 15; 16]. * Discrete-event simulation (DES) is a modeling approach used for systems where changes in state occur at specific, discrete points in time referred to as events. These changes are typically triggered by occurrences such as the start or completion of operations, the arrival of materials, or changes in resource availability. Because of this, DES is especially suitable for modeling production systems, logistics operations, and queuing environments. * Simulation is widely applied across diverse industries, including chemical process design for sustainable fuel production, where it supports efficient plant layout and process optimization [17]. The structure of DES relies on a simulation clock and an event list, allowing the model to progress step-by-step from one event to the next. This makes it possible to accurately represent dynamic changes in the system without the need to track continuous state fluctuations. Furthermore, the appropriate granularity of activity recognition significantly influences the accuracy and applicability of simulation models, as demonstrated in heavy civil engineering contexts [18]. DES can be implemented using different methods, such as traditional event list models or agent-based event handling. Recent studies emphasize the integration of simulation optimization approaches with adaptive decision support systems to meet the dynamic requirements of Industry 5.0 environments, enabling flexible and resilient production scheduling [19]. Thanks to its high flexibility and precision, DES has become a widely used tool in industrial engineering. It supports process optimization, identification of bottlenecks, and evaluation of various production scenarios [7; 8]. Recent studies also highlight the integration of simulation with optimization techniques to enhance production system performance, flexibility, and adaptability to changing conditions [20]. For instance, [PERSON] [21] applied DES to simulate production line downtimes, while [PERSON] et al. [22] used it to compare planned and simulated production outcomes, both confirmed by real-world data. DES is particularly effective in environments where activities are performed in stages and transitions occur irregularly over time. Typical examples include manufacturing lines, computer networks, and mining operations [23; 24]. In this study, discrete-event simulation was the core method used to model the selective soldering process. This type of soldering, commonly found in electronics manufacturing, consists of a series of discrete steps: manual component insertion, transport of pallets, soldering by a robotic nozzle, and quality control touch-ups. Each of these operations marks a distinct event in the production timeline, making DES an ideal modeling framework.