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

Table 2: Shows the Legends used in this Paper CNY Cost in CNY CE Carbon Emissions CS Customer Satisfaction NY Number of Vehicles Used i. 25 Customers C101(25) test table is used here for obtaining the most optimal path with better results of the constraints set. This solution has used the Pareto optimal approach and figure 3 has shown the comparison between [3] and this paper. It is clearly visible from the graph that the proposed algorithm of ACO+K-Means [PS_KPSO] clustering has better output in terms of carbon emission, customer satisfaction and total transportation cost. ACO (Ant Colony optimization) and ACOMO (Ant Colony optimization Multi objective) which is heuristic function. Figure 3: c101(25) comparison with [30] (ACOMO) and ACO+K-Means Clustering (PS_KPSO) PS_KPSO algorithm has given total cost as 3138.07, lower carbon emission of 19.50 and 100 percent customer satisfaction in all cases. Below in figure 4, showst he trucks used is 3 with respective optimal travel paths (0, 5, 3, 7, 8, 10, 11, 9, 6, 4, 2, 1,0), (0, 13, 17, 18, 19, 15, 16, 14, 12,0) and [(0, 20, 24, 25, 23, 22, 21, 0 ) Figure 4: c101(25) vehicle distribution route50 Customers This part has used the c101(50) test table. 5 trucks have been employed with respective paths (0, 43, 42, 41, 40, 44, 46, 45, 48, 50, 49, 47,0), (0, 5, 3, 7, 8, 10, 11, 9, 6, 4, 2, 1,0), (0, 20, 24, 25, 27, 29, 30, 28, 26, 23, 22, 21,0), (0, 32, 33, 31, 35, 37, 38, 39, 36, 34,0) and (0, 13, 17, 18, 19, 15, 16, 14, 12, 0). The end results are 33.43 for carbon emissions, 5942.72 cost and 100 percent customer satisfaction. Figure 5 shows the comparison between [30] and this paper results while figure 6 displays the routes taken by the 5 trucks. Global Journal of Computer Science and Technology Volume XXII Issue I Version I 56 ( )D Year 2022 © 2022 Global Journals Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer Table 2 shows the used legends as explained in figure 3, figure 5, figure 7 and figure 9.