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

Figure 5: c101(50) comparison with [30] (ACOMO) and ACO+K-Means Clustering (PS_KPSO) Figure 6: c101(50) vehicle distribution route ii. 75 Customers The c101(75) dataset has been used in this part. The number of vehicles used is 8 with the most optimal paths chosen respectively: (0, 43, 42, 41, 40, 44, 46, 45, 48, 51, 50, 52, 49, 47, 0), (0, 5, 3, 7, 8, 10, 11, 9, 6, 4, 2, 1, 75, 0), (0, 32, 33, 31, 35, 37, 38, 39, 36, 34, 0), (0, 67, 65, 63, 62, 74, 72, 61, 64, 68, 66, 69, 0), (0, 20, 24, 25, 27, 29, 30, 28, 26, 23, 22, 21, 0), (0, 57, 55, 54, 53, 56, 58, 60, 59, 0), (0, 13, 17, 18, 19, 15, 16, 14, 12, 0) and (0, 71, 70, 73, 0) The final results of carbon emissions, total cost and customer satisfaction are 54.96, 10639.71 and 100 percent respectively. Figures 7 and 8 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 57 ( )D Year 2022 © 2022 Global Journals Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer