# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 2 Materials and Methods
* the due date of the order;
* the expected time of the order in the system;
* the importance level of the order;
* the date and time of arrival of the order;
* the profit from the order;
* the loss due to order delays;
* the losses due to a pending order. To sort the production orders for release into production, we use several options, among which is the application of common priority rules. First In First Out (FIFO)--This means that orders will be sorted in the order in which they arrived in the system. Last In First Out (LIFO)--This is the opposite of the FIFO method. Random order--This involves sorting in random order. Shortest Processing Time First (SPT)--This involves sorting according to the shortest time needed to process an order. Earliest Due Date (EDD)--This involves sorting according to the earliest due date. Minimum Slack Time First (MS)--The task with the smallest time margin is ranked first. \[S_{o}=D_{o}-P_{o} \tag{1}\]  
where  
\(S_{o}\)--time reserve of the order;  
\(D_{o}\)--order delivery date;  
\(P_{o}\)--estimated time of order processing. Critical ratio (CR)--This allows orders to be sorted in ascending order regarding their critical ratio. \[CR_{o}=\frac{D_{o}}{P_{o}} \tag{2}\]  
where  
\(CR_{o}\)--critical ratio of the given order;  
\(D_{o}\)--order delivery date;  
\(P_{o}\)--estimated time of order processing. For the comprehensive calculation of the order priority, a custom formula that includes a number of factors and some priority rules is proposed. Order sorting is performed according to evaluation criteria and the coefficients of their impact on the order priority. The values of the coefficients are adjustable, and the user can therefore choose the importance of each factor that is marked in the formula. The coefficient can be negative or positive, depending on the nature of the impact of the indicator, or it can be zero (in the case of a zero coefficient, the corresponding factor is not considered in the calculation). For example, if the coefficient of the critical ratio is \"1\" and the coefficient of the number of pieces in the order is \"0.5\", this means that the critical ratio has a greater impact on the prioritization of the order, and it is more likely that the order with the best critical ratio will be scheduled first. The value of this coefficient can be adjusted based on planner requests, customer requests, or historical data. The coefficients used in the prioritization formula are configurable to reflect various operational strategies or client preferences. While users can adjust these values manually based on experience or business priorities, the model also allows data-driven calibration. In future applications, historical production data may be utilized to identify coefficient values that minimize key performance indicators, such as the makespan or order delays, using optimization techniques. For instance, regression analysis, sensitivity testing, or metaheuristic algorithms (e.g., genetic algorithms) could be applied to tune the coefficients towards optimal values under specific production conditions. Although such a method is not the focus of this paper, it represents a logical extension of the current model and would strengthen its adaptability in dynamic manufacturing environments.