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

Figure 7: c101(75) comparison with [30] (ACOMO) and ACO+K-Means Clustering (PS_KPSO) Figure 8: c101(75) vehicle distribution route iii. 100 Customers This section has used the c101 (100) dataset. Now looking [30], there are better results in terms of carbon emission, cost and customer satisfaction (69.03, 13561.41 and 100 percent). Instead of 23 vehicles, 10 vehicles have been employed and the most optimal paths are chosen: (0, 5, 3, 7, 8, 10, 11, 9, 6, 4, 2, 1, 75, 0), (0, 43, 42, 41, 40, 44, 46, 45, 48, 51, 50, 52, 49, 47, 0), (0, 20, 24, 25, 27, 29, 30, 28, 26, 23, 22, 21, 0), (0, 67, 65, 63, 62, 74, 72, 61, 64, 68, 66, 69, 0), (0, 90, 87, 86, 83, 82, 84, 85, 88, 89, 91, 0), (0, 57, 55, 54, 53, 56, 58, 60, 59, 0), (0, 98, 96, 95, 94, 92, 93, 97, 100, 99, 0), (0, 32, 33, 31, 35, 37, 38, 39, 36, 34, 0), (0, 13, 17, 18, 19, 15, 16, 14, 12, 0), (0, 81, 78, 76, 71, 70, 73, 77, 79, 80, 0). Figures 9 and 10 showcase the comparison between [30] and this paper and the route distribution of the vehicles. Global Journal of Computer Science and Technology Volume XXII Issue I Version I 58 ( )D Year 2022 © 2022 Global Journals Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer