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The try_job_addition worst-case complexity is $O(U^2\times R)$ where $U$ is the number of unassigned jobs we try to re-insert, and $R$ is the number of candidate routes for re-insertion. This is not a big deal in typical re-insertion tries after applying a local search operator because then $R\le 2$ and $U$ is usually small.
On the other hand, when we ruin a bigger part of the solution and want a full recreation, then $R$ is the number of existing vehicles in the current solution and $U$ can be much higher. So the time spent doing the recreation can start to be significant, especially in the light of #874 where we'll probably tend to remove more jobs than we currently do.
We could have a pre-established insertion order for unassigned jobs, based say on priority, capacity, TW narrowness, closeness to some vehicle or similar. In which case the above complexity would drop to $O(U\times R)$.
The text was updated successfully, but these errors were encountered:
The$O(U^2\times R)$ where $U$ is the number of unassigned jobs we try to re-insert, and $R$ is the number of candidate routes for re-insertion. This is not a big deal in typical re-insertion tries after applying a local search operator because then $R\le 2$ and $U$ is usually small.
try_job_addition
worst-case complexity isOn the other hand, when we ruin a bigger part of the solution and want a full recreation, then$R$ is the number of existing vehicles in the current solution and $U$ can be much higher. So the time spent doing the recreation can start to be significant, especially in the light of #874 where we'll probably tend to remove more jobs than we currently do.
We could have a pre-established insertion order for unassigned jobs, based say on priority, capacity, TW narrowness, closeness to some vehicle or similar. In which case the above complexity would drop to$O(U\times R)$ .
The text was updated successfully, but these errors were encountered: