Ant Colony Optimization for Waste Collection Routing: A Case Study in Sekar Tunjung Residential

Abstract

Waste collection is a critical issue in the management of efficient urban systems, particularly in densely populated residential areas. Inefficient waste transportation routes can lead to excessive fuel consumption, prolonged collection times, and increased operational costs. This study proposes the application of the Ant Colony Optimization (ACO) algorithm as a metaheuristic approach to solve the Capacitated Vehicle Routing Problem (CVRP) in the context of waste collection. ACO mimics the foraging behavior of ant colonies, allowing it to explore and reinforce optimal routing paths based on pheromone intensity and distance-based desirability. The methodology involves modeling a spatial network of waste pick-up points, each with varying waste volumes, and incorporating truck capacity constraints into the routing algorithm. The case study is situated in the Sekar Tunjung residential in East Denpasar, where the simulation uses ten pick-up points and a waste truck starting and ending at TPS3R Kesiman. Parameters such as pheromone influence , visibility , and evaporation rate  are tuned to find the best route configuration. Simulation results show that ACO efficiently constructs a single-trip route covering all points while respecting capacity limits. The algorithm demonstrates adaptability to changes in volume distribution and capacity scenarios, resulting in minimal total travel distance. This research confirms the potential of ACO as a robust and flexible solution for optimizing waste transportation logistics in urban environments.