# An Evaluation Framework and Algorithms for Train Rescheduling
## 7 Description of the Experiment
### Track Reassignments  
The average track reassignments in the solutions obtained by ALG1 and ALG2, rounded to the nearest integer, are 5 and 1, respectively (see Table 19). This difference in algorithm performance concerning total track reassignments is statistically significant (\(p=0.01\)).  
In the configuration of ALG2, minimizing the number of track reassignments is an objective that has little priority. Irrespective of that, the algorithm produces solutions with minimal track reassignments. It is reasonable to assume that the optimal number of track reassignments in a rescheduled timetable is zero. In other words, for the input dataset, a rescheduled timetable can be obtained without making any track reassignments. With that in mind, one can say that, over the entire dataset, ALG2 achieved a very good trade-off between minimizing the delays and the number of track reassignments. Thus, ALG2 produces solutions with minimal track reassignments while giving close-to-optimal train and passenger delays.  
For scenarios 1-20, in the solutions output by both the algorithms, only passenger trains incur track reassignments. In case of ALG1, the reason for this is as follows. ALG1 does not reallocate the tracks of trains that are not directly affected by the disturbance. In scenarios 1-20, since the initially disturbed train is a passenger train, only the track allocation of that train is modified during rescheduling. A consequence of this rescheduling strategy is as follows. In each of these scenarios, all the track reassignments in the solutions obtained by ALG1 belong to one train, since ALG1 confines the track reassignments to the initially disturbed passenger train. In contrast, ALG2 changes the tracks of various passenger trains at stations. Only one passenger train incurring a reasonable number of track reassignments could be perceived as an advantage of using ALG1, from a passenger perspective as well as from a dispatching perspective. The reason for the latter is that fewer rescheduled trains make it easier for the dispatcher to supervise during a disturbance.  
For scenarios 26-30, the solutions obtained using ALG1 involve many track reassignments (see Table 11). The reason is that, when ALG1 encounters a conflict involving a train with primary delay, it first tries to resolve the conflict by reallocating the train's track. Thus, for the trains with a primary delay, the algorithm prefers track reassignment over retiming and reordering. In each of the disturbances 26-30, more than 24 trains incur primary delays (as shown in Table 7). Due to the rescheduling strategy employed by ALG1, the solutions obtained for these scenarios have many track reassignments (see Table 11). In contrast, for these scenarios, the solutions produced by ALG2 do not involve any track reassignments. Both the algorithms obtained almost-ideal train delays and passenger delays in the solutions for disturbances 26-30. Since ALG2 obtained these solutions without performing any track reassignments, it shows that, for these disturbance scenarios, there is no trade-off between minimizing the number of track reassignments and minimizing the values of train and passenger delays.