Dynamic Route Optimization For Waste Collection Using Genetic Algorithm

This study shows how the process of allocating dynamic routes to vehicles in waste collection companies results in wastage of resources and time. The purpose of the study is to find suitable algorithm that can be used to generate optimized dynamic routes for the vehicle routing problem (VRP). The study is based on Alawali District in Mecca City where there are a total of 52 waste collection points including a depot and a delivery point. The coordinates of all these waste collection points were collected and their distances from each other determined to find out the shortest route that can be used by a vehicle to collect wastes in the whole district at a reduced cost. The algorithm used in this study is the Genetic Algorithm (GA). GA was chosen because of its reliability in when compared with the rest of the metaheuristic algorithms that could be used to optimize a VRP for any dynamic route setup. The study focused on utilizing the dynamic route optimization with the VRP, which was solved through the incorporation of the Hamiltonian path and the GA. The study was carried out on two vehicles (A and B), and the total cost of fuel, time taken, and the total distance covered by the two vehicles at the end of every route recorded. The results obtained from GA showed that the total fuel cost for vehicle A and B was $7.3, total time taken by the two vehicles was 2 hours and 45 minutes, and the total distance covered by the two vehicles was 68.57 miles. These results were the most reliable of all metaheuristic algorithms. Therefore, GA was chosen as the best algorithm to optimize a VRP for any route setup because it gave the least fuel consumption, the shortest time, and the shortest distance when compared to the rest of the algorithms as depicted by the results.