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

Table 1: Parameters to Be Used Throughout this Paper Parameters Implications Predefined Value Gravitational constant of Earth 9.81 / 2 Density of atmosphere 1.225 / 3 Gradient 0 ∆ Temperature Difference between container of vehicle and atmosphere 20 ∘ Frequency of opening the vehicle door 0.6 Transportation price of per unit weight of goods 1 Decline coefficient of product freshness 0.01 Early arrival time penalty coefficient 0.6 Late arrival time penalty coefficient 0.8 Number of iterations of pheromone renewal 20 Weight of pheromone concentration 1 Weight of heuristic function 3 Volatility coefficient 0.8 The parameter of the selection rule of Pseudo-random proportional action 0.9 Minimum value of pheromone 0.001 Maximum value of pheromone 10 , , Weights of optimization objectives 1 3 Fuel consumption per unit time ( ) 22.37 Fuel consumption and carbon emission conversion factor ( ) 1/2.7 Fuel-air ratio 1 Engine friction factor 0.2 Engine speed of refrigerated truck(r/min) 2000 Air Displacement(L) 1.051 Effective power transmission 0.45 Energy consumption constant 44 Refrigerated truck weight(kg) 1510 Windward area of vehicle(m²) 3.96 Coefficient traction 0.4 Coefficient of rolling resistance 0.01 Heat transfer coefficient refrigerated truck 0.4 External surface area of refrigerated truck(m²) 43.36 Interior surface area of refrigerated truck(m²) 22.32 Vehicle maximum load 200 Vehicle fixed cost 200 Soft time window 20 Carbon tax 20 c) Result Analysis The entire result section has used the Pareto optimal principle for obtaining the solution. The Pareto Principle states that 80 percent of a project’s benefit comes from 20 percent of the work. The optimal version of it makes the sub objectives suppressed so as to efficiently solve the main objective. Due to this there is very little scope of conflict of objectives from the sub objectives and a noiseless solution id obtained. Referring to [30], this paper the objectives chosen will be carbon emission reduction, total cost, time frame and customer satisfaction. Using several test cases of 25,50,75 and 100 customers in three different scenarios, the proposed ACO algorithm with K-Means clustering provides a better solution in comparison. The results are arranged in the Pareto optimal solution format. The test cases and their outputs are given below from figure 3 to figure 26. Global Journal of Computer Science and Technology Volume XXII Issue I Version I 55 ( )D Year 2022 © 2022 Global Journals Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer