A Hybrid Method to Solve the Multi-UAV Dynamic Task Assignment Problem

. 2025 Apr 16;25(8):2502. doi: 10.3390/s25082502

Algorithm 4 Generate Tasks

Input:

M: Number of existing tasks to be generated.
Q: Number of new tasks to be generated.
$m p S$ : Width of the simulation map.
$d i s t$ : Task distribution across the map, which can be random, concentrated, or predetermined.

Output:

T: Set of defined tasks, including their coordinates, arrival time, reward, and duration.

1:
for $j = 1$ to M do
2:
Generate initial coordinates $(x_{j}, y_{j}, z_{j})$ for $T_{j}$ based on $m p S$ and $d i s t$ .

$c o o r d_{j} \leftarrow (x_{j}, y_{j}, z_{j})$ .
3:
    Generate arrival time, end time, duration, and reward for $T_{j}$ :

     $[t s_{j}, t e_{j}] \leftarrow$ Random arrival time and end time.

     $D_{j} \leftarrow$ Random duration.

     $R_{j} \leftarrow$ Random reward.
4:
end for
5:
for $q = 1$ to Q do
6:
Generate initial coordinates $(x_{q}, y_{q}, z_{q})$ for $n T_{q}$ based on $m p S$ and $d i s t$ .

$c o o r d_{q} \leftarrow (x_{q}, y_{q}, z_{q})$ .
7:
    Generate arrival time, end time, duration, and reward for $n T_{q}$ :

     $[t s_{q}, t e_{q}] \leftarrow$ Random arrival time and end time.

     $D_{q} \leftarrow$ Random duration.

     $R_{q} \leftarrow$ Random reward.
8:
end for
9:
Set $n T$ as global data.