*3.2. Algorithm Process*

Figure 7 represents the whole calculation process. The detailed procedure was as follows:

Step 1: To determine the population size, maximum number of iterations, crossover probability and mutation probability, use the greedy algorithm to generate the initial population.

Step 1.1–Step 1.3 are the processes of generating the initial solution:

Step 1.1: Calculate the distance between two customer points and sort them from smallest to largest.

Step 1.2: Determine whether the solution satisfies the subpath condition. If it does, add it to the current path or determine the next path.


Step 1.3: Execute the previous step until all subpaths are assigned while both endpoints of each path are connected to the distribution center to form a closed loop.

Step 2: Perform an improved nondominated ranking based on the biobjective function values of the individuals in the initial population. Then, apply a tournament strategy to select and generate new offspring populations through a cyclic crossover and mutation.

Step 3: Combine the offspring and parent populations to generate a new population. Through the elite strategy, compare the improved nondominated ranking value and crowding distance to obtain a better combination of individuals to generate the parent population.

Step 4: Iteratively update the newly generated parent population with genetic manipulation.

Step 5: Judge whether the current iteration number reaches a maximum and output the final result. Otherwise, repeat Step3.

**Figure 7.** G-NSGA-II algorithm flow chart.
