Adapting Ant Colony Optimization for the Capacitated Vehicle Routing Problem in Waste Collection System

Many prior investigations have highlighted the slow convergence of the Ant Colony Algorithm (ACO) compared to various other metaheuristic algorithms in the pursuit of identifying optimal solutions. This research seeks to modify the ACO algorithm for its application in resolving the Capacitated Vehicle Routing Problem (CVRP) within the context of solid waste collection and transportation operations. The primary objective is to enhance the exploration capacity of ACO by integrating it with the Sequential Variable Neighborhood Search Change Step and Sequential Variable Neighborhood Descent algorithms (thereafter referred to as HACO). The primary focus is on minimizing both route distances and the count of vehicles required for servicing all designated containers on a planned route. In a technical sense, HACO meticulously navigates the search space while considering pertinent information associated with the demands and spatial coordinates of the waste containers. Consequently, the performance of the proposed algorithm underwent testing across nine CVRP datasets. The numerical and visual findings demonstrate clearly that superiority of HACO over competing algorithms by average 88.8%.