Algorithm 1 Genetic Algorithm

Input: $GAP$: a matrix represents the number of nodes required, P: population size, $pc$: crossover probability, $pm$: mutation probability, T: iterations

Output: Routes: dispatch routes. Cost: dispatching cost

begin

1: Initialize population $P\left(0\right)$ with randomly generated chromosomes using natural number encoding;

2: $t\leftarrow 0$;

3: while $(t\le T)$ do

4:       for $i\leftarrow 1$ to P do

5:          Evaluate fitness of each chromosome in $P\left(t\right)$;

6:       end for

7:       for $i\leftarrow 1$ to M do

8:         Select parent chromosomes for crossover from $P\left(t\right)$;

9:       end for

10:       for $i\leftarrow 1$ to $M/2$ do

11:          Perform crossover operation on selected parent chromosomes;

12:       end for

13:       for $i\leftarrow 1$ to M do

14:          Perform mutation operation on chromosomes in $P\left(t\right)$;

15:       end for

16:       for $i\leftarrow 1$ to M do

17:          Decode chromosomes in $P\left(t\right)$ using the decoding rules;

18:          Update chromosomes in $P\left(t\right)$ according to the decoded results;

19:       end for

20:       $t\leftarrow t+1$;

21: end while

22: end