Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 22 Issue 1

Figure 23.: rc201(75) vehicle distribution route Figure 24.: rc201(100) vehicle distribution route ptimal solutions in the figure. The number of optimal solutions is less than the one with the test set having 25 to 100 customers. Generally, the fewer the optimal solutions ther are, the fewer distribution plans the distribution center can choose. As an important factor in cold chain logistics, temperature control plays an active role in cold chain logistics distribution. The strict temperature control can effectively reduce the impact of temperature fluctuations on cargo in cold chain transportation. To a certain extent, refrigeration equipment has caused an increase in carbon emission. Below table 5 explains from optimal path, fuel, and cost efficiency for above figure 11 to figure 24 Table 5 : Vehicle distribution route for optimal path, fuel, and cost efficiency Figure Number Comments Figure 11.: c201(25) vehicle distribution route The Figure 11 shows vehicle distribution for case 2, it shows optimal path for 25 customers Figure 12.: c201(50) vehicle distribution route The Figure 12 shows vehicle distribution for case 2, it shows optimal path for 50 customers Figure 13.: c201(75) vehicle distribution route The Figure 13 shows vehicle distribution for case 2, it shows optimal path for 75 customers Figure 14.: c201(100) vehicle distribution route The Figure 14 shows vehicle distribution for case 2, it shows optimal path for 100 customers Figure 15.: r201(25) vehicle distribution route The Figure 15 shows vehicle distribution for case 2, it shows fuel efficiency for 25 customers Figure 16.: r201(50) vehicle distribution route The Figure 16 shows vehicle distribution for case 2, it shows fuel efficiency for 50 customers. Figure 17.: r201(75) vehicle distribution route The Figure 17 shows vehicle distribution for case 2, it shows fuel efficiency for 75 customers. Figure 18.: r201(100) vehicle distribution route The Figure 18 shows vehicle distribution for case 2, it shows fuel efficiency for 100 customers. Figure 19.: r211(25) vehicle distribution route The Figure 19 shows vehicle distribution for case 2, it shows fuel efficiency for 25 customers for random vehicles. Figure 20.: r211(50) vehicle distribution route The Figure 20 shows vehicle distribution for case 2, it shows fuel efficiency for 50 customers for random vehicles. Figure 21.: rc201(25) vehicle distribution route The Figure 21 shows vehicle distribution for case 2, it shows fuel and cost efficiency for 25 customers for random vehicles. Figure 22.: rc201(50) vehicle distribution route The Figure 22 shows vehicle distribution for case 2, it shows fuel and cost efficiency for 50 customers for random vehicles. Figure 23.: rc201(75) vehicle distribution route Figure 23 shows vehicle distribution for case 2, it shows fuel and cost efficiency for 75 customers for random vehicles. Figure 24.: rc201(100) vehicle distribution route The Figure 24 shows vehicle distribution for case 2, it shows fuel and cost efficiency for 100 customers for random vehicles. Global Journal of Computer Science and Technology Volume XXII Issue I Version I 63 ( )D Year 2022 © 2022 Global Journals Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer