## 3 Research Methodology  
Verifying any new scheduling methodology encounters serious difficulties. In the case of developing new scheduling algorithms, tests can be performed on static input data. By comparing different algorithms, it can be shown that the new algorithm is faster or allows for a lower value of a given criterion (it finds a better solution to the scheduling problem). Most often, there is no need to perform production based on the generated schedule. In the case of the new methodology, which is based on the dynamic influence of production conditions (e.g., the influence of an employee and a tool), the matter becomes  
Figure 3: Flowchart of the lab optimization algorithm. more complicated. We need a tool that will allow us to test the implementation of the generated schedule. The best test environment for production scheduling methods is an actual manufacturing company. An example of the validation of the scheduling method in a near-real environment is work [42], where the authors used a dedicated factory workstation. However, enterprises are often unwilling or even unable to risk experimental modifications to their production procedures. The more innovative the methodology, the greater this resistance. Gaining access to the real production environment is difficult, so the validation of scheduling methods is carried out most often using simulation models that are configured properly for the characteristics of the scheduling problem under consideration [43]. Dedicated software (e.g., SIMIO) is most often used to create simulation models [44]. The data sets analyzed there are usually random or previously made available in the literature on the subject [43]. An alternative method to verify the effectiveness of scheduling methods is to determine the distance of the result obtained from the known optimal solution [9]. However, most often, we do not know this optimal solution. The solution used in the research is a virtual production plant [45], which is an extension of the concept of simulation models to the area of the entire plant, taking into account the influence of the implementation conditions on the course of technological operations. The tool was used to evaluate the effectiveness and efficiency of the developed scheduling method. The concept consists of collecting (based on historical data archived in plants, the knowledge of experts, and the experience of technologists and managers) information about the course of production and the relationships present in it. Knowledge must cover all phenomena in production that may be significant during scheduling. In addition to knowing all production resources and production recipes used, it should include information on the efficiency of individual employees and tools and the material and energy consumption of their activities for given types of technological operations. Acquired knowledge is recorded in the form of mathematical formulas and rules. Based on this knowledge, you can build a virtual plant, that is, a tool that allows you to simulate the course of production according to a given schedule. Of course, the representativeness of the simulation will depend on the quality of the data collected. Figure 4 shows the components of this model and the boundaries of the virtual plant system (the gray box). The plant transfers a set of production orders (from its customers) to the APS (Advanced Planning and Scheduling) system with a tested scheduling methodology. Based on these orders, knowledge of the resources available in the plant, and technological recipes (\"know-how\"), the APS system generates a production schedule. The virtual plant performs its simulated production based on its resources and coded knowledge about the dependencies between the selection of production resources and the course of technological operations (obtained from actual production plants).