Efficient Vehicle Routing Solutions: A Metaheuristic Approach

Abstract

Efficient vehicle directing is fundamental for smoothing out strategies processes, cutting costs, and raising consumer satisfaction. The current study explores the usage of metaheuristic procedures to handle multifaceted vehicle directing issues that emerge in various fields, including public transportation frameworks, transportation logistics, and delivery services. The study is explicitly worried about the application and investigation of a modified version of Ant Colony Optimization (ACO), a metaheuristic algorithm that draws motivation from ant foraging behavior. To increment solution quality, robustness, and convergence speed, the modified ACO algorithm incorporates enhancements like multi-colony systems, local search heuristics, and dynamic pheromone update instruments. This examination shows the adequacy and adaptability of the recommended approach in delivering ideal or almost ideal routing arrangements through mathematical modeling, theoretical analysis, and empirical assessments utilizing real-world datasets. The outcomes add to the corpus of information on optimization algorithms and give valuable exhortation to working on the manageability and proficiency of vehicle routing operations.