AI Summary & Key Takeaways
AI InsightThe SA algorithm outperformed the greedy algorithm by 26.49%.
📌 3 Key Takeaways
Abstract
With the increase in population in cities, the number of solid waste to be collected has also increased. Because the garbage collection route must be traveled repeatedly, even minor improvements in these routes can result in a significant increase in fuel usage. Shortening the journey would provide a significant contribution to lowering fuel expenses in all towns, especially given the rising cost of fossil fuels. Furthermore, lowering fuel usage is critical for Turkey to meet its national objectives under the Paris Agreement. The Simulated Annealing (SA) algorithm, one of the heuristic optimization techniques used to identify the best solutions to complicated problems, is employed to solve the routing problem of solid waste collection vehicles in this study. This method, which was inspired by the metal annealing process, stands out for its ability to avoid regional minima while looking for the optimal solution. The applicant region was selected as the Kosova Neighborhood of Konya's Selçuklu District. The container distances needed for the method to execute were acquired by extracting the coordinates of the containers. Kosova Neighborhood was separated into 7 distinct regions due to the restricted capacity of rubbish collection vans. All regions were analyzed independently, and the best feasible routes were estimated using the SA algorithm approach, and the results were compared to the greedy algorithm findings. The SA algorithm outperformed the greedy algorithm by 26.49%.