## 5 Results and Analysis
### Feature Selection  
The contribution of each feature to the three scheduling objectives has been analyzed and a subset of the features that play an important role in designing dispatching rules in the three dynamic job-shop scenarios have been selected in this section. The results of the feature selection process for the MT, MWT, and MFT scenarios are presented in Figure 6a-c, respectively. These figures depict the outcomes of 30 independent runs of the proposed algorithm. Each column of a matrix represents a feature, whereas each row represents a run (for specifics, see Table 1). A point is drawn at the intersection of the selected feature \(f\) and the \(i\)th run. Figure 5: The computational time of the GEP algorithms in the three scenarios: (**a**) computational time across generations in the MS scenario; (**b**) computational time across generations in the MFT scenario; (**c**) computational time across generations in the MWT scenario. For the MS scenario, the feature PT, NPT, and WKR were selected in all 30 runs. This indicates that the processing time of operations in the MS scenario had an important impact on the computation of job priorities. At the same time, the machine prefers to select jobs with shorter processing times, fewer remaining processes, and small residual workloads, which is in line with the logic of shortening the makespan. Moreover, the features NOINQ, NOR, and OWT were selected in more than half of the running times of the algorithm, indicating that they significantly contributed to the generation of optimal dispatching rules at least half of the time. It can also be seen that the feature FDD, W, and SL were selected only a few times, which indicated that these features were irrelevant or redundant in this scenario and may not have contributed to the generation of best dispatching rules. The significance of the remaining features, namely AT, NOPS, WIQ, NOIQ, DD, and MRT, is not distinctly evident. Regarding the MFT scenario, the feature PT was selected in all 30 runs. This indicates that, as in the MS scenario, the processing time of the operation also had a significant impact on the computation of job priorities in the MFT scenario. In addition, the machine preferentially selected jobs with short processing times, which can shorten the flow time of the jobs as much as possible. WINQ and NPT were selected only a few times more than PT, which means that the jobs with fewer workloads in the next operation had a higher priority to be processed. NOINQ and WKR were also selected in more than half of the runs, indicating that machines in the MFT scenario preferred to select the jobs with fewer remaining workloads and fewer operations in the next waiting queue. To put it simply, the characteristics of the next operation were the key point to designing effective dispatching rules in the MFT scenario. This lack of selection of the WIQ, NOIQ, SL, and W features over most runs implies that they did not contribute to the best-developed rules for the MFT objective. However, the contribution of AT, OWT, NOPS, DD, MRT, NOR, and FDD to the MFT scenario is not very clear. In terms of the MWT scenario, PT and W were selected in the whole running process of the algorithm, which indicates that the machine preferred to prioritize the jobs with short processing time and high weight, so as to shorten the tardiness. This is consistent with the general conclusion in the field of production scheduling that PT and W were important features influencing the mean weighted tardiness. In addition to the aforementioned features, it was observed that WIQN, NOINQ, and NPT were commonly selected over a large number of iterations, indicating the significance of the workload data in the subsequent queues toward the minimization of the MWT objective. Unlike the findings of early research [60], the inclusion of terminal DD in the feature sets was not deemed irrelevant, as it was selected over 60% of the time.