This tool is implemented by Genetic Algorithm. You can use it for Job Scheduling Problem or Assignment Problem.
Input format: Columns and rows represent people and jobs, respectively. every cell is the prioritisation score for this job (score can be "0 to n-1" or "1 to n"). If the number is smaller indicates they more want the task.
task1, task2, task3 ...
0, 1, 2 ...
2, 0, 1 ...
1, 0, 2 ...
...
Please make sure your input files in the "inputs" directory.
python main.py
Optional arguments:
| Parameter | Default | Description |
|---|---|---|
| -i --input | data.csv | data to use, please include the extension |
| -o --output | result.csv | output file name |
| -p --p-size | 50 | population size |
| -c --c-rate | 0.2 | crossover rate |
| -m --m-rate | 0.1 | mutation rate |
| -n --n-iter | 1000 | number of iterations (generations) |
Output example (csv format)
n1,n2,n3,n4,n5,n6,n7,n8,n9,n10 <- the header row is task names
t2,t1,t1,t4,t3,t5,t4,t2,t5,t3 <- each row shows the calculation results for each generation
t2,t4,t1,t3,t3,t5,t1,t4,t5,t2
... (skip many iteration)
t3,t1,t4,t4,t3,t5,t2,t1,t5,t2
t3,t1,t4,t4,t3,t5,t2,t1,t5,t2
t3,t1,t4,t4,t3,t5,t2,t1,t5,t2
... (skip many iteration)
t2,t4,t4,t3,t3,t5,t1,t1,t5,t2
t2,t4,t4,t3,t3,t5,t1,t1,t5,t2
t2,t4,t4,t3,t3,t5,t1,t1,t5,t2
t2,t4,t4,t3,t3,t5,t1,t1,t5,t2
t2,t4,t4,t3,t3,t5,t1,t1,t5,t2 <- the last generation is optimal solution
python gen.py
Optional arguments:
| Parameter | Default | Description |
|---|---|---|
| -o --output | simulated-data.csv | output file name |
| -nt --n-tasks | 5 | number of tasks (jobs) |
| -ni --n-items | 10 | number of items (persons, machines, etc.) |
Output example (csv format)
t1,t2,t3,t4,t5
0,3,2,4,1
3,2,1,4,0
4,1,2,3,0
4,3,1,0,2
3,4,2,0,1
1,3,4,2,0
2,1,3,0,4
1,4,0,2,3
0,1,3,4,2
4,3,1,0,2
- numpy >= 1.20.3
- pandas >= 1.3.4