## 4 Experiments, Results, and Discussion
### Production of the Scraper Conveyor Chute  
The scraper conveyor chute is a product manufactured for the mining industry by a company specializing in mining equipment. A characteristic feature of the analyzed technological process is the total lack of automation. All the operations that make up the technology (Table 1) are performed manually. For this reason, the changeability of the time of individual production operations is at the level of \(11\div 20\%\), and the cost changeability is at the level of \(7\div 36\%\) depending on the selected set of production resources. For each operation, the plant has a set of production resources enabling their purposeful selection (at least two tools of the required type and at least three employees with the required qualifications). The available number of combinations of production resources for a single operation reaches the value of 18. For the generation of the schedule in both methodologies (without and taking into account the influence of resources and including this influence), a dedicated algorithm was used, as presented in Section 2.4. The stop condition was defined based on an analysis of the optimization test run (Figure 6). The analyzed test instance concerned the scheduling of 175 production orders. The total number of scheduled technology operations was 875. It was assumed that the optimization process will be stopped when the stagnation period exceeds the minimization progress period by 100%. The authors assumed that in the case of the Monte-Carlo algorithm (protected against getting stuck in the local minimum), it means there is a high probability of finding the global minimum. The same relative stop condition was used in the remaining experiments. For the example of the scraper conveyer chute production, the schedule building process (using the algorithm described in Section 2.4) is presented in Figure 7. The top of the figure shows the order set to be scheduled from 11 April 2022. To improve readability, the example of the process implementation was limited to three orders. As you can see, the technological recipe for each order for the production of the chute is identical and consists of five technological operations. The final operation called \"Grinding\" integrates the two production paths and includes the final assembly in addition to grinding. Recipe operations do not have a specific duration, as it will depend on the set of production resources allocated when the operation is inserted into the schedule. The Gantt chart below the definition of orders shows a frozen state after ten iterations of the loop \(A\) from the flowchart in Figure 3. Subsequent operations are inserted into the schedule in subsequent iterations of this loop. In the description of the operation block, the iteration number is marked with the symbol \(I\):\(n\). According to the algorithm flowchart, first, an order was randomly selected for planning. According to the Gantt chart in Figure 7, order 3 was selected in the first iteration. In the next step of this iteration, the operation to be scheduled was selected from the recipe for the order. To fulfill the postulate of total stochasticity of the algorithm, for both initial, parallel assembly operations, the insertion probability is identical. In the example, the operation _Assembly 1_ was inserted into the first iteration. At the time of insertion, the necessary resources for its implementation were allocated: the employee _Assember 3_ and the tool _Assembly station 2_. Please note that due to its stochastic nature, the algorithm selects resources completely randomly and during this selection is \"blind\" to the employee's performance. It manifested itself in the selection of the slowest worker. Due to this, the algorithm searches the entire available solution space.